phd research proposal artificial intelligence

Artificial Intelligence Research Proposal

Artificial intelligence or AI is one of the latest technologies being used in integration with machine learning, deep learning, and deep reinforcement learning. Developers and software designers craft solutions to some of the important problems in AI . Data or information in the form of digital satellite images, visual data, structured unstructured, and text data. Artificial intelligence research proposal writing needs expert advice since the field is extensively growing in research and development.

  Robust data and proper algorithms to detect patterns are essential for the effective functioning of artificial intelligence systems . This article provides a complete picture of artificial intelligence projects where will start by defining the basics

What is Artificial intelligence?

• The aim of developing intelligent machines is the motive behind artificial intelligence
• It becomes one of the inherent and the most necessary parts of many sectors such as real-time applications (industry 4.0, smart city, and robotics)

Due to its growing importance, the research in artificial intelligence is increasing at large. With the help of our highly specialized and technically well-versed group of experts , you can surely produce the best artificial intelligence research proposal. One of the underlying problems in AI research respect the following characteristics of programming.

• Knowledge and perception
• Learning, planning, and reasoning
• Problem solving
• Manipulation capacity and object motion

Get in contact with us if you want to learn about the strategies used by our specialists to solve artificial intelligence research issues . The important technicalities will then be simply shared with you.

Types of Artificial Intelligence

The following are the most important types of artificial intelligence

• It is developed because of the demand from a large number of users and regulators
• The system is developed in such a way to learn and get trained from critical data sets while protecting the user privacy
• NLP strengths are incorporated or utilized by collaborative AI for superior and advanced manipulation
• Diverse testbed is also it’s characteristic
• This model of artificial intelligence helps in improving its adoption and deployment
• It is built over the trusted brand of Singapore
• Incremental learning of artificial intelligence systems automatically is a characteristic feature of this model
• Artificial general intelligence can in turn be enhanced using this model
• The datasets used in this kind of AI is highly qualitative
• Here nearly a small data set comparable to a small country is utilized

We will do a comparative analysis of all of these topic areas so that you can easily identify the subject study that best meets your demands. We support the wide interpretation of liberty of researchers , which we believe will lead to many of the improvements that society requires.

As a result, we encourage our clients to do in-depth research on any topic on their own and act as a facilitator to them. Then, if necessary, we will examine their thoughts and assist them in selecting the most interesting study topic or writing a artificial intelligence research proposal . Let us now look into the applications of AI below

Artificial Intelligence applications

AI has a lot of applications in diverse fields. It is the technology of the present that has huge potential to become the only technology of the future. In this regard, we have a look into the applications of AI below.

• AI is well known for its use in smart cities and other smart applications like finance, health, transport, and justice delivery

With its capacity to supplement human intelligence and capacities , AI intelligence and human abilities are now getting integrated to produce greater results

• Artificial intelligence is integrated into light detection and ranging systems called LIDAR (combines radar and light) for advanced details during navigation and avoiding Collision

For further explanation on all such technicalities, you can contact us. We can also assist you with any research needs you may very well have. We believe that present techniques should be questioned. This is because we think that only by asking questions can we have a better knowledge of what we’re talking about, and only then can we create optimal technologies .

Latest Research Topics in Artificial Intelligence

The following are some of the important and recent artificial intelligence research topics

• Pattern recognition and expert systems
• Artificial neural networks and natural language processing
• Robotics and genetic algorithm
• Machine learning and computer vision
• Automated reasoning and complex systems
• Intelligent search engine, control, and data mining

We intended to influence society by guiding prospective research at a fair cost in all the above topics . As a result, we supply you with a variety of additional services that you will require during your study. Artificial intelligence Dissertation , as you may know, is made feasible by mathematical operations conducted on digital forms of signals utilizing complex algorithms.

Artificial Intelligence Technologies List

• Deep learning image recognition and computer vision skills are utilised by the vehicles for self-driving
• It can intelligently avoid Collisions and unexpected obstacles and it can also pilot a given vehicle by staying in a particular lane
• Robotics engineering field whose primary aim is to manufacture and develop advanced robots
• Automation is involved in developing autonomous mechanisms and systems
• Machine vision is the technology that allows the computers and other devices to have vision
• Machine learning enables a computer to work on its own without getting programmed for each and every aspect
• By NLP you can process the human words and languages using computers

All these AI-based methods and systems are built on the foundations of coding and mathematics . The programming frameworks and simulation techniques related to AI ought to have been obvious to you. You can also feel free to get in touch with our experts at any time concerning these methodologies. Let us now look into the research proposal format.

Format of a research proposal

The following are the important aspects of a research proposal,

• A suitable and unique title to a topic can attract the reader’s attention
• You need to highlight the important points with regard to the background of the topic and the field development timeline
• Research has to be well establish in depth investigation for providing evidence
• The proposed methods and techniques have to be clearly mentioned along with their merits and demerits of the existing works.
• A detailed working plan along with the timeline has to be developed and your research must be scheduled in line with it
• Sources used in proposal writing must be properly acknowledged in bibliography
• You can also include a reference section in place of bibliography

It is now really important that you have a clear and expert view of the various aspects of artificial intelligence proposal in great detail. Because attempting to write a research proposal by knowing all its necessities in prior can help you get the best outcome. So latest now have a look into every aspect of artificial intelligence research proposal in great detail in the following sections

Conduct Preliminary research

To choose one of the best topics, you need to have preliminary research. Make sure that you look into all the aspects of atopic and choose the most specific issue in place. This helps you to focus your research proposal on the right track. You can get all the books, benchmark references, journals, and authentic websites for collecting information regarding your research objective from us. Make sure to look into both pros and cons of your topic. Consider the following points during preliminary research

• Points that are overlooked by the readers in your research sources
• Potential debatable topics to be addressed
• Your stance over the topic
• Recent breakthroughs in your field

You must include explanations from reliable sources on all these points in your proposal. We provide you with the essential support and motivation to conduct research and complete your artificial intelligence research proposal successfully. We are well versed in the proposal format of all the universities of the world.

To formulate research questions, you can use the phrase ‘I want (or attempted) to know what (why or how) of the problem’ so that it looks standard. Let us now have some more ideas on the topic being selected.

Discovering, narrowing and focusing a researchable topic

• The most interesting topic according to you have to be selected
• Then you need to attempt to write all about the topic in your way
• Have interactions with your peer groups and course instructor
• Finally given up your topic in the form of a question which you should proclaim to address in the proposal

In addition, a topic with potential and reliable reference sources can help you to a greater extent. Here we assist you in fetching advanced research materials and data for any novel topic of your interest. As we have established associations with the world’s top researchers and experts , we can bring any kind of materials for your research at your disposal . Reach out to us for all such most needed research assistance. Let us now look into source selection,

Finding, selecting and reading sources  

As you start looking for the standard sources for your artificial intelligence research proposal we insist you give priority to the following sources

• Standard primary and secondary sources of references
• Limitations, research gaps, and drawbacks of existing methods

You can get the necessary practical explanations along with the massive reliable data from our research guidance facility. With world-class certified developers, writers, and engineers you can get a greater insight into all aspects of computer simulation and artificial intelligence from us. Let us now look into the ways of documenting the collected information.

Grouping, sequencing and documenting information

When you are working to present and document the data collected you must make proper grouping and sequencing of them.

For all formatting and editing guidance, you can check out our website. We are offering one of the best artificial intelligence research proposal writing guidance with highly qualified and experienced writers of the world. We ensure to offer customized online research support 24×7 . Let us now see about writing an outline and a prospectus.

Writing an Outline of Research Proposal

The following are the important questions to be dealt with in your research proposal

• The topic to be dealt
• Significance of the topic
• Relevant background knowledge and material
• Problem statement along with its purpose
• Plan of the organization to support the statement to its best

By ensuring multiple grammatical checks and confidential research support we become the most reputed and trustworthy research guidance providers across all the countries. Also, you can expect complete support from our side concerning assignment writing, paper publication , and survey and conference paper writing , and so on. Let us now talk about writing an introduction,

How to write the introduction section?

The following aspects have to be included with huge importance in your research proposal introduction

• All the important points concerning background and context materials
• Necessary terms and concepts definition
• Proper explanations on the focus and purpose of the research proposal
• Plan of organization has to be revealed perfectly

To better understand the style of writing, you can look into the standard examples of the best and successful research proposals that we guided. We have more than two decades of experience in artificial intelligence research. So our experts are capable of solving all the research issues, problems, and concerns of it . Let us now talk about writing the body of the proposal.

Writing the body

The following are all the important points to be remembered while writing the body of a research proposal

• Develop your proposal in and around your topic
• The sources should not direct your proposal whereas the search of sources have to be in line with your objective
• Integration of the sources and discussion must be given prime importance
• Summarising, analyzing, explaining, and evaluating the published work is more important than making a report of it
• Make sure to include the generalized and specific points about the topic

To include the authentic research data in the body of your artificial intelligence research proposal , readily contact our technical experts. We also provide all necessary help in the successful implementation of accurate codes and writing respective algorithms . Let us now talk about the research proposal conclusion

Writing the conclusion

• In case of complexities in the proposal you are expected to provide a summary
• The importance of findings and observations has to be recorded even before the conclusion part. In cases when such points are missed out, you can add and explain their significance at the end
• In the finishing stage, from being more specific you need to shift towards a generalized approach in line with the introduction
• At last, the scope for further research in the future gives a good frame to your proposal

Get to read the best conclusions from our website. An artificial intelligence research proposal is one of our major services through which we have delivered more than 300+ Artificial Intelligence Projects in the field. With the highly experienced technical team and engineers, we are providing experimentation and Research support to our customers. We will now discuss the important aspects of the experimentation section

Experimental section

• The simulation tools being used have to be introduced and explained properly
• Proper configuration details of the software and hardware are essential
• Description of the data sets have to include their links, attributes, and analysis
• Latest years of papers from authentic sources like Elsevier, Springer, and IEEE
• At least from 50+ papers, doing the literature works
• Clear definition of the performance parameters and metrics can fetch you more credibility
• Graphical and tabular comparative analysis attach the visualization aspect to your study
• Summarization of the result has the potential to retain your study in the mind of the reader

As we mentioned earlier, having a better idea of the simulation tools, techniques, platforms, and software becomes highly significant to conduct the best research. Our experts update themselves regularly to provide advanced technical assistance to you. Let us now see the criteria for writing the best thesis

What are the important criteria for the best proposal?

The best proposal is expected to consist of the solutions and answers to all the following questioning aspects

• Ability to arrive at the result at times of less resolution and quality of data
• Comparatively the ability to solve data processing parameter trade-offs efficiently
• Enhancing accuracy when the training videos and proper guidance is not available to carry out the testing
• Proper explanation for scalability of your system
• Proper statistical information at the introduction with real-time examples
• Unique and many advanced features are expected to be a part of the proposal
• The number and quality of testing features under consideration

For proper technical notes and standard reference sources in order to holistically cover all the above aspects, you can talk to our experts. Let us now have a look into scalability and the aspects of data sets below

• Scalability must handle very large datasets to provide greater accuracy and efficiency
• For this purpose, during testing make sure that you use a large number of servers, users, and devices
• The real-time examples, applications, and innovations have to explain in a easy to comprehend manner
• Evaluating the datasets by comparing only two of them might not be sufficient
• Along with artificial datasets evaluation becomes more standardized
• Dimensionality, noise level, outliers, and data size are the important aspects that can potentially impact your proposal
• Computation of all metrics have to be explained properly
• Number of metrics under consideration were taking up more than six metrics and parameters are recommended

By providing multiple revisions and professional proposal writing guidance they have been rendering excellent expert aid in artificial intelligence research proposal . Zero plagiarism and on-time delivery are our mottos. Get in touch with us to get guidance from the world’s best research experts.

Why Work With Us ?

Senior research member, research experience, journal member, book publisher, research ethics, business ethics, valid references, explanations, paper publication, 9 big reasons to select us.

Our Editor-in-Chief has Website Ownership who control and deliver all aspects of PhD Direction to scholars and students and also keep the look to fully manage all our clients.

Our world-class certified experts have 18+years of experience in Research & Development programs (Industrial Research) who absolutely immersed as many scholars as possible in developing strong PhD research projects.

We associated with 200+reputed SCI and SCOPUS indexed journals (SJR ranking) for getting research work to be published in standard journals (Your first-choice journal).

PhDdirection.com is world’s largest book publishing platform that predominantly work subject-wise categories for scholars/students to assist their books writing and takes out into the University Library.

Our researchers provide required research ethics such as Confidentiality & Privacy, Novelty (valuable research), Plagiarism-Free, and Timely Delivery. Our customers have freedom to examine their current specific research activities.

Our organization take into consideration of customer satisfaction, online, offline support and professional works deliver since these are the actual inspiring business factors.

Solid works delivering by young qualified global research team. "References" is the key to evaluating works easier because we carefully assess scholars findings.

Detailed Videos, Readme files, Screenshots are provided for all research projects. We provide Teamviewer support and other online channels for project explanation.

Worthy journal publication is our main thing like IEEE, ACM, Springer, IET, Elsevier, etc. We substantially reduces scholars burden in publication side. We carry scholars from initial submission to final acceptance.

Related Pages

Our benefits, throughout reference, confidential agreement, research no way resale, plagiarism-free, publication guarantee, customize support, fair revisions, business professionalism, domains & tools, we generally use, wireless communication (4g lte, and 5g), ad hoc networks (vanet, manet, etc.), wireless sensor networks, software defined networks, network security, internet of things (mqtt, coap), internet of vehicles, cloud computing, fog computing, edge computing, mobile computing, mobile cloud computing, ubiquitous computing, digital image processing, medical image processing, pattern analysis and machine intelligence, geoscience and remote sensing, big data analytics, data mining, power electronics, web of things, digital forensics, natural language processing, automation systems, artificial intelligence, mininet 2.1.0, matlab (r2018b/r2019a), matlab and simulink, apache hadoop, apache spark mlib, apache mahout, apache flink, apache storm, apache cassandra, pig and hive, rapid miner, support 24/7, call us @ any time, +91 9444829042, [email protected].

Questions ?

Click here to chat with us

Artificial Intelligence

Since the 1950s, scientists and engineers have designed computers to "think" by making decisions and finding patterns like humans do. In recent years, artificial intelligence has become increasingly powerful, propelling discovery across scientific fields and enabling researchers to delve into problems previously too complex to solve. Outside of science, artificial intelligence is built into devices all around us, and billions of people across the globe rely on it every day. Stories of artificial intelligence—from friendly humanoid robots to SkyNet—have been incorporated into some of the most iconic movies and books.

But where is the line between what AI can do and what is make-believe? How is that line blurring, and what is the future of artificial intelligence? At Caltech, scientists and scholars are working at the leading edge of AI research, expanding the boundaries of its capabilities and exploring its impacts on society. Discover what defines artificial intelligence, how it is developed and deployed, and what the field holds for the future.

Artificial Intelligence Terms to Know >

What Is AI ?

Artificial intelligence is transforming scientific research as well as everyday life, from communications to transportation to health care and more. Explore what defines AI, how it has evolved since the Turing Test, and the future of artificial intelligence.

What Is the Difference Between "Artificial Intelligence" and "Machine Learning"?

The term "artificial intelligence" is older and broader than "machine learning." Learn how the terms relate to each other and to the concepts of "neural networks" and "deep learning."

How Do Computers Learn?

Machine learning applications power many features of modern life, including search engines, social media, and self-driving cars. Discover how computers learn to make decisions and predictions in this illustration of two key machine learning models.

How Is AI Applied in Everyday Life?

While scientists and engineers explore AI's potential to advance discovery and technology, smart technologies also directly influence our daily lives. Explore the sometimes surprising examples of AI applications.

What Is Big Data?

The increase in available data has fueled the rise of artificial intelligence. Find out what characterizes big data, where big data comes from, and how it is used.

Will Machines Become More Intelligent Than Humans?

Whether or not artificial intelligence will be able to outperform human intelligence—and how soon that could happen—is a common question fueled by depictions of AI in movies and other forms of popular culture. Learn the definition of "singularity" and see a timeline of advances in AI over the past 75 years.

How Does AI Drive Autonomous Systems?

Learn the difference between automation and autonomy, and hear from Caltech faculty who are pushing the limits of AI to create autonomous technology, from self-driving cars to ambulance drones to prosthetic devices.

Can We Trust AI?

As AI is further incorporated into everyday life, more scholars, industries, and ordinary users are examining its effects on society. The Caltech Science Exchange spoke with AI researchers at Caltech about what it might take to trust current and future technologies.

What is Generative AI?

Generative AI applications such as ChatGPT, a chatbot that answers questions with detailed written responses; and DALL-E, which creates realistic images and art based on text prompts; became widely popular beginning in 2022 when companies released versions of their applications that members of the public, not just experts, could easily use.

Ask a Caltech Expert

Where can you find machine learning in finance? Could AI help nature conservation efforts? How is AI transforming astronomy, biology, and other fields? What does an autonomous underwater vehicle have to do with sustainability? Find answers from Caltech researchers.

Terms to Know

A set of instructions or sequence of steps that tells a computer how to perform a task or calculation. In some AI applications, algorithms tell computers how to adapt and refine processes in response to data, without a human supplying new instructions.

Artificial intelligence describes an application or machine that mimics human intelligence.

A system in which machines execute repeated tasks based on a fixed set of human-supplied instructions.

A system in which a machine makes independent, real-time decisions based on human-supplied rules and goals.

The massive amounts of data that are coming in quickly and from a variety of sources, such as internet-connected devices, sensors, and social platforms. In some cases, using or learning from big data requires AI methods. Big data also can enhance the ability to create new AI applications.

An AI system that mimics human conversation. While some simple chatbots rely on pre-programmed text, more sophisticated systems, trained on large data sets, are able to convincingly replicate human interaction.

Deep Learning

A subset of machine learning . Deep learning uses machine learning algorithms but structures the algorithms in layers to create "artificial neural networks." These networks are modeled after the human brain and are most likely to provide the experience of interacting with a real human.

Human in the Loop

An approach that includes human feedback and oversight in machine learning systems. Including humans in the loop may improve accuracy and guard against bias and unintended outcomes of AI.

Model (computer model)

A computer-generated simplification of something that exists in the real world, such as climate change , disease spread, or earthquakes . Machine learning systems develop models by analyzing patterns in large data sets. Models can be used to simulate natural processes and make predictions.

Neural Networks

Interconnected sets of processing units, or nodes, modeled on the human brain, that are used in deep learning to identify patterns in data and, on the basis of those patterns, make predictions in response to new data. Neural networks are used in facial recognition systems, digital marketing, and other applications.

Singularity

A hypothetical scenario in which an AI system develops agency and grows beyond human ability to control it.

Training data

The data used to " teach " a machine learning system to recognize patterns and features. Typically, continual training results in more accurate machine learning systems. Likewise, biased or incomplete datasets can lead to imprecise or unintended outcomes.

Turing Test

An interview-based method proposed by computer pioneer Alan Turing to assess whether a machine can think.

Dive Deeper

More Caltech Computer and Information Sciences Research Coverage

• [email protected]
• +91-97 91 62 64 69

PhD Research Proposal Artificial Intelligence

One of the most important subject areas of computer science is Artificial Intelligence. It provides a wide platform for building a machine with learning capabilities . Artificial intelligence makes machines think and react similarly to humans in uncertain situations. In other words, this machine intelligence to behave artificially like human intelligence is known as artificial intelligence. This page is intended to present you useful information on PhD Research Proposal Artificial Intelligence along with the latest research areas, technologies, challenges, trends, techniques, and ideas !!!

Assume that there is a situation in which a human is performing a particular task by learning and understanding the event to solve associated problems. This human task is performed by machine artificial learning abilities are known as artificial intelligence.

For instance: a self-driving car without a driver. In this, the vehicle monitors the environment and takes effective decisions for secure destination attainment.  

What are the requirements for good research proposal writing? 

We believe that we made you are clear with the exact purpose and importance of artificial intelligence at this moment. Now, we can see about the requirements of good PhD Research Proposal Artificial Intelligence . Basically, the writing of PhD proposal needs more concern and study to create a qualified proposal. Since it is the reflection of your research activities and efforts in the form of valuable words. Here, we have given you a few important tips to prepare good proposal writing.

• Need to be adaptable to access required information and resources
• Need to be meet the expected standard and enhance interest to read
• Need to be original to create a new contribution to the handpicked research area
• Need to be related with your degree and present research areas of artificial intelligence

In general, the PhD research proposal has a standard format to write. As well, it is composed of different components such as title, abstract, introduction, literature study, methodologies, conclusion, and references. In fact, we have a native writer team to give complete assistance in perfect proposal writing. Further, we also help you in literature review writing, paper writing, and thesis writing. Here, we have given you a few important things that need to be focused on while writing PhD Research Proposal Artificial Intelligence.   

What are the Components of a Good Research Proposal? 

• Give a short and crisp title for your research proposal
• Choose a title that addresses your research problem and proposed solutions
• Provide a summary of your research work
• Act as detailed synopsis that answers why, how, and what questions of your research
• Present your selected research area and research problem(s)
• Highlight the significance of your study
• Provide sufficient hypothesis of research
• Mention the methodologies that going to be used as solutions
• Talk about the review of secondary research materials
• Address the identified research gaps in previous related studies
• Do a comparison of techniques and arguments in existing researches
• Describe the contribution and findings of the previous research
• List the merits and demerits of existing research works
• Present system architectural design
• Give a detailed explanation on used research tools and techniques/methodologies
• Speak about the need and importance of choosing those methodologies
• Explain the numerical formulas and used algorithms
• Give justification for your proposed research methodologies
• Mention in what way your research methodologies solve your research problem
• Again give an overview of your research
• Point out the objectives and importance of your research
• Encapsulate all highlights of your research in brief
• A present unique point of your study
• Overall, write nearly two paragraphs
• Provide citation of your referred research websites and books
• Implicitly these references mention your supportive hypothesis
• Narrow down your wide research sources
• Smart picking of research materials will impress the research committee

We hope that you are clear with the fundamentals of writing a good PhD research proposal artificial intelligence . Now, we can see about the three primary research terms of artificial intelligence. Since these terms are most widely used in many research areas of artificial intelligence.  As well, it is categorized into three classifications such as, 

• Exploration Areas
• Real-Time Applications

Our researchers are good at proposing modern research work in upcoming research areas for smart applications . If you are interested to know more research ideas from the following classifications, then make an online or offline connection with us.   

What are three important terminologies in Artificial Intelligence? 

• Genetic Evolutionary
• Logical Rationalism
• Molecular Biological
• Statistical Empiricism
• Neural Connectionism
• Smart System Design
• Learning Approaches
• Inference Mechanism
• Knowledge Representation
• Expert System
• Electronic Commerce
• Bioinformatics
• Intelligent Robots
• Natural Language Processing
• Information Retrieval
• Data Mining

In addition, we have also given you some significant research areas of artificial intelligence . We assure you that all these areas are recognized in current AI research topics and ideas. 

Moreover, we also support you in other important research ideas to support you in all aspects of artificial intelligence . By the by, our first and foremost task in AI research is identifying your interesting research area. Then, we provide you list of the latest research notions and phd topics in artificial intelligence .

Research Areas for PhD Research Proposal Artificial Intelligence

• Reinforcement Learning
• Supervised Learning
• Unsupervised Learning
• Dialogue Systems
• Natural Language
• Understanding
• Recognition
• Classification

Furthermore, we have given you a few important supporting AI technologies. Due to the beneficial impact of AI, it is employed and demanding in several research fields (i.e., other technologies). For your information, here we have given you only a few of them. Once you connect with us, we let you know more about up-to-date research topics of your selected technologies . Specifically, these technologies are currently successful in creating real-time AI applications for the development of a smart society.

Converging Technologies of AI 

• Internet of Things
• Big Data Analytics
• Blockchain Technology
• Lightweight Cryptography
• Cloud Computing
• Software-Defined Networking
• Fog Computing
• 6G Networks
• Industry 4.0
• UAV Communication
• Autonomous Vehicles
• Edge Networks

As a point of fact, AI is treated as the shared technology which used to solve different problems in different technologies. So, it can be recognized in many real-time applications and services. Although this field has so many developments in real-time applications, it has some technical issues that arise in the time of development and deployment . For your reference, here we have listed a few important technical issues of AI in recent research.      

Artificial Intelligence Research Problems 

• Optimized Modern Parameters
• Non-linearity from learning to compensate
• Hard-to-Model Issues
• Knowledge and Learning Representation
• Solution for Computational Infeasibility
• Computationally Understanding Solutions
• Training Policies

Already, we have seen converging technologies of artificial intelligence in an earlier section. To the continuation, now we can see about the current trends of AI. In order to identify these trends, our research team has studied the present and past 2-3 years’ research articles and magazines. Through this review, we analyzed and identified

1) Research gaps that need to address

2) Problems that need enhanced solutions than existing one

From this collection, we have listed only a few of them for your reference. Further, we are also ready to share more trends that are sought by active research scholars in the field of artificial intelligence.    

Artificial Intelligence Current Trends

• Mainly in sustainable developments, energy usage has a key player role
• Provides productive communication plans for improving energy-efficiency
• Support significant services in 6G communication
• Human-sensed data are composed with 5D services to enhance the holographic communication
• Assure high QoS, precision, deterministic in 6G communication
• Need tremendous data rates like Tb/s
• Currently, manufacturing industries are moving towards automation technologies and precision communication
• In this, 6G is assured to give ultra-low delay and ultra-high reliability
• For real-cases, the general data transmission need industrial networks for low latency jitters
• For achieving a secure environ, wireless technologies, IoT and fog-cloud computing are advancing over global sustainability and QoS
• Presently, the 6G network understands 3D communication to enhance several applications like smart transportation, smart cities, smart healthcare, etc.
• For instance – Self-driving vehicles delay < 1ms and reliability > 99.999% for fast decisions over sudden accidents

Now, we can see emerging techniques that play a major role in bringing effective research solutions for different current research problems. As a matter of fact, our developers are proficient-enough to identify the best-fitting research techniques and algorithms for any sort of research problem .

In the case of complications in solving problems, our developers analyze the degree of problem complexity and create hybrid technologies or new algorithms accordingly. Overall, we are good to tackle the problem at any level of complexity in smart ways. Also, we suggest key parameters and development tools that enhance your system performance.   

Latest Techniques in AI 

• Generally, the data are collected from different formats, mode representations and sources
• Merging all these dissimilar data in one place is a tedious task
• For the data fusion, advanced neural networks and bayesian learning is used
• For instance – CNN, RBM, and DBM
• Through sensors, collect raw data and transfer it into high-computational devices for data processing
• This may cause more power usage and high traffic load over the network
• So, it is required to design a system that minimizes load and power usage without losing vital information
• Utilize ANN and perform preprocessing
• Also, network topology and architecture are required to be chosen appropriately for add-on benefits
• Prevent interference for primary user benefits through spectrum sensing
• The significant role of the primary user is to transmit data between secondary users and the succeeding layer
• This process is executed by Cooperative Spectrum Sensing (CSS) with high power usage
• The power usage increases because of report findings and spectrum sensing with respect to a centralized location
• Similarly, Convolutional Neural Network is utilized in Deep Corporate Sensing

Additionally, we have given you some growing ideas about artificial intelligence. These ideas are selected from different trending research areas that gain more attraction from the research community. If you have your own ideas to implement an artificial intelligence project, then we support you to upgrade your idea to match the latest advancements of artificial intelligence. So, create a bond with us, to know new interesting PhD research propsoal artificial intelligence . Overall, we give assistance on not only these ideas but also beyond this list of ideas.   

Emerging Ideas on AI 

• Artificial Intelligence for Internet of Things
• Privacy-Aware AI-assisted Edge System for Trustable Services
• Fast AI Services Migration from Cloud into Edge
• Secure Data Dissemination on AI-assisted Edge Systems
• In-depth Learning Services over Edge Network
• Energy-Aware AI-assisted Edge System for Quality of Services
• Real-time AI-assisted Edge Systems with Optimized Solutions
• Edge-intensive Distributed / Collaborate / Federated Smart Services

On the whole, we are here to update you about the recent research updates of artificial intelligence in every possible area. Particularly, we help you in research problem selection, corresponding solutions selection, PhD Research Proposal Artificial Intelligence Writing, code development, paper writing, paper publication, and thesis writing. So, think smartly and hold your hands with our technical experts to shine your AI research career.

Opening Hours

• Mon-Sat 09.00 am – 6.30 pm
• Lunch Time 12.30 pm – 01.30 pm
• Break Time 04.00 pm – 04.30 pm
• 18 years service excellence
• 40+ country reach
• 36+ university mou
• 194+ college mou
• 6000+ happy customers
• 100+ employees
• 240+ writers
• 60+ developers
• 45+ researchers
• 540+ Journal tieup

Payment Options

Our Clients

Social Links

• Terms of Use

Opening Time

Closing Time

• We follow Indian time zone

Office Address

• #5, First Floor, 4th Street Dr. Subbarayan Nagar Kodambakkam, Chennai-600 024 Landmark : Samiyar Madam
• [email protected]
• +91- 81240 01111

Social List

Latest research proposal in artificial intelligence (ai).

Best Masters and PhD Research Proposal in Artificial Intelligence (AI)

Nowadays, people live their life with more advancements and ease of access to the developed technologies. Artificial intelligence is the process of mimicking behaviors, or functions of the human brain by the computes or machines in problem-solving and learning information. The advanced artificial intelligence model greatly assists computer vision, natural language processing, prediction, and decision-making functions in various real-world application domains. With the real-life successes in machine learning, artificial intelligence has gained enormous attention among people. Owing to the advances in artificial intelligence, the applications of self-driving cars, smart speakers, image recognition, and computer vision have been assisted day-to-day human lives. With the rise of big data, artificial intelligence plays a significant role in public life and the business environment. Spam classification, handwritten digits recognition, medical decision-making, gaming, opinion mining, next-word prediction, chatbot, fake review recognition, automatic attendance, and music recommendation are the rapidly emerging artificial intelligence research areas.

• Guidelines for Preparing a Phd Research Proposal

Latest Research Proposal Ideas in Artificial Intelligence

• Research Proposal on AI for air quality monitoring and control in smart cities
• Research Proposal on Machine learning for drug efficacy and safety prediction in clinical trials using AI
• Research Proposal on Natural language processing for student modeling in AI
• Research Proposal on Explainable Scene Understanding and Context-Aware Vision for AI
• Research Proposal on Multi-Agent System Security and Privacy in Decentralized Systems for AI
• Research Proposal on AI-based Algorithmic Game Theory for Multi-Agent Systems
• Research Proposal on Aligning AI with Human Values
• Research Proposal on Privacy-Preserving Multi-Agent Communication Protocols for AI
• Research Proposal on Hierarchical Planning and Scheduling for AI
• Research Proposal on Knowledge representation and reasoning for educational systems in AI
• Research Proposal on Design and evaluation of educational robotics curricula with AI
• Research Proposal on Visualization of Neural Network Decisions for AI
• Research Proposal on AI Regulation and Policy
• Research Proposal in AI for Context-Aware Machine Learning
• Research Proposal on Privacy and Confidentiality in AI
• Research Proposal on Decentralized Multi-Agent Control in AI
• Research Proposal on Multi-Agent Communication and Coordination in Distributed Systems for AI
• Research Proposal on Game-Theoretic Approaches for Multi-Agent Communication in AI
• Research Proposal on User-Centered Explanations in Recommender Systems for AI
• Research Proposal on Explainable Fraud Detection with AI
• Research Proposal on Reinforcement Learning for Autonomous Vehicle Energy Management in AI
• Research Proposal on Visual Explanations for Explainable Anomaly Detection in AI
• Research Proposal on Deep Learning for Computer Vision with AI
• Research Proposal on Transparent and Explainable AI
• Research Proposal on Multi-Agent Communication and Coordination in Decentralized Systems for AI
• Research Proposal on Deep Generative Models for Image Restoration and Enhancement in AI
• Research Proposal on Reinforcement Learning for Autonomous Vehicles using AI
• Research Proposal on User-Centered Interpretable Machine Learning for AI
• Research Proposal on Contextual Reasoning in Explainable AI
• Research Proposal on Bias and Fairness in AI
• Research Proposal on AI-based Computational Game Theory for Multi-Agent Systems
• Research Proposal on Visual Explanations for Deep Learning Models in AI
• Research Proposal on Counterfactual Explanations in Explainable AI
• Research Proposal on Weakly Supervised Learning for Computer Vision in AI
• Research Proposal on Multi-Agent System Privacy Threats and Attacks in AI
• Research Proposal on Natural language processing for smart city citizen engagement with AI
• Research Proposal on AI-based drug discovery and target prediction for cancer treatment
• Research Proposal on AI for resource-constrained devices in IoT
• Research Proposal on Reinforcement learning for treatment recommendation with AI
• Research Proposal on Predictive analytics for clinical trial recruitment and retention with AI
• Research Proposal on Fraud Detection and Waste Reduction in Healthcare Management for AI
• Research Proposal on AI-based fraud detection in business operations
• Research Proposal on AI-based Predictive modeling for personalized medicine
• Research Proposal in AI for Impact of educational robotics on student motivation and engagement
• Research Proposal on Real-time Planning and Scheduling for Robotics and Automation in AI
• Research Proposal on Real-Time Image and Video Pattern Matching for AI
• Research Proposal on Nash Equilibrium in Multi-Agent Systems for AI
• Research Proposal on Deep learning for early disease diagnosis and prediction in AI
• Research Proposal on Bayesian deep learning for uncertainty modeling in AI
• Research Proposal on Machine learning for clinical trial design and optimization in AI
• Research Proposal on Measuring and evaluating the impact and ROI of AI investments in business
• Research Proposal on Personalized learning recommendation systems for AI
• Research Proposal on Deep Reinforcement Learning for Planning and Scheduling with AI
• Research Proposal on AI-based Integration of educational robotics in the classroom
• Research Proposal on AI-enabled student performance assessment and analysis
• Research Proposal on Chatbots and Virtual Assistants for Patient Support with AI
• Research Proposal on AI-powered traffic management in smart cities
• Research Proposal on Reinforcement Learning for Autonomous Vehicle Trajectory Prediction in AI
• Research Proposal on Explainable Risk Assessment with AI
• Research Proposal on Transfer Learning for Context-Aware Vision in AI
• Research Proposal on Deep learning based models for automated essay grading in AI
• Research Proposal on Deep Learning for Medical Image Analysis using AI
• Research Proposal on Causal inference methods for medical decision making with AI
• Research Proposal on Multi-modal learning analytics for AI
• Research Proposal on Deep Learning for Human Pose Estimation in AI
• Research Proposal on Non-monotonic Reasoning for Defeasible Logics in AI
• Research Proposal on Explainable AI for IoT decision-making
• Research Proposal on Human-in-the-loop essay grading systems for AI
• Research Proposal on Formal Methods for Multi-Agent Communication Protocols in AI
• Research Proposal on Human-Centered Visual Explanations for AI
• Research Proposal on Personalized learning and Adaptive educational systems for AI
• Research Proposal on Multi-modal fusion for disease diagnosis and prognosis using AI
• Research Proposal on AI-enabled data analysis for improved clinical trial outcomes
• Research Proposal on Reinforcement Learning for Autonomous Vehicle Interaction with Other Agents using AI
• Research Proposal on AI-based prediction of disease progression and treatment response
• Research Proposal on AI-based device management and control in IoT
• Research Proposal on AI-powered patient stratification for personalized clinical trials
• Research Proposal on Ethical and privacy considerations in AI-powered classroom management
• Research Proposal on AI-based Reinforcement Learning for Treatment Planning
• Research Proposal on Personalized student learning using AI algorithms for AI
• Research Proposal on End-to-end Multi-modal Fusion for AI
• Research Proposal on Image Restoration and Enhancement for Low-Quality Images in AI
• Research Proposal on Multi-Agent System Security and Privacy in Multi-Agent Learning for AI
• Research Proposal on Game-Theoretic Approaches to Multi-Agent System Design in AI
• Research Proposal on Scalable Multi-Agent Reinforcement Learning in AI
• Research Proposal in AI for Scene Understanding and Context-Aware Vision
• Research Proposal on Non-monotonic Reasoning for Belief Revision in AI
• Research Proposal on Counterfactual Explanations for Credit Scoring with AI
• Research Proposal on Deep Reinforcement Learning for Autonomous Driving with AI
• Research Proposal on Deep Learning for Autonomous Vehicle Perception using AI
• Research Proposal on Human-Centered Explainable AI
• Research Proposal on Image Restoration and Enhancement for AI
• Research Proposal on Multi-Agent System Security and Privacy in Resource Constrained Systems for AI
• Research Proposal on AI-based Transfer Learning for Image Restoration and Enhancement
• Research Proposal on Planning and Scheduling with Constraints and Preferences for AI
• Research Proposal on Ensemble methods for enhanced cancer prediction and diagnosis using AI
• Research Proposal on Deep Learning for Autonomous Vehicles
• Research Proposal on Artificial intelligence for the integration of real-world data into clinical trial design
• Research Proposal on Image Super Resolution Using Deep Learning
• Research Proposal on AI-powered care pathway optimization and decision support
• Research Proposal on Action Recognition using Deep Learning
• Research Proposal on Multi-Agent Communication and Coordination in Multi-Agent Decision Making for AI
• Research Proposal on Human Motion Recognition using Deep Learning
• Research Proposal on International Cooperation on AI Governance
• Research Proposal on Object Detection using Deep Learning
• Research Proposal on Dropout as a method for uncertainty modeling in deep learning using AI
• Research Proposal on Image Segmentation using Deep Learning
• Research Proposal on Deep learning for early cancer diagnosis and prediction using AI
• Research Proposal on Knowledge Representation and Reasoning for AI
• Research Proposal on Predictive modeling for patient response to treatment in AI
• Research Proposal on Ethical issues of AI
• Research Proposal on Pattern Matching for AI
• Research Proposal on AI for supply chain optimization and management
• Research Proposal on Multi-Agent Systems for AI
• Research Proposal on Real-time student performance prediction with AI
• Research Proposal on Planning and Scheduling for AI
• Research Proposal on Multi-Agent Communication and Coordination in Multi-Agent Learning for AI
• Research Proposal on Multimodal Learning for Autonomous Driving
• Research Proposal on AI and Intellectual Property
• Research Proposal on Commonsense Reasoning
• Research Proposal on Human Pose Estimation using AI
• Research Proposal on Artificial Intelligence in Education
• Research Proposal on Non-monotonic Reasoning for Diagnosis and Debugging in AI
• Research Proposal on Artificial Intelligence in Agriculture
• Research Proposal on Planning and Scheduling under Uncertainty for AI
• Research Proposal on Artificial Intelligence in Business Management
• Research Proposal on Predictive models for educational policy and resource allocation in AI
• Research Proposal on Artificial Intelligence in Marketing
• Research Proposal on AI-based Machine learning for drug efficacy and safety prediction in clinical trials
• Research Proposal on Artificial Intelligence in Healthcare
• Research Proposal on AI-based Deep reinforcement learning for IoT network optimization
• Research Proposal on Artificial Intelligence in Internet of Things
• Research Proposal on Ensemble methods for improved disease prediction in AI
• Research Proposal on Uncertainty Modeling for AI
• Research Proposal on Development of pedagogical methods for learning with educational robots in AI
• Research Proposal on Explainable Artificial Intelligence
• Research Proposal on Adversarial Training for Image Restoration and Enhancement in AI
• Research Proposal on Non-monotonic Reasoning for Abduction in AI
• Research Proposal on Reinforcement Learning for Autonomous Vehicle Safety using AI
• Research Proposal on Multi-modal Sensor Fusion for Autonomous Vehicles with Deep Learning in AI
• Research Proposal on Contextual Reasoning for Healthcare with AI
• Research Proposal on AI and Human Rights
• Research Proposal on Multi-Agent System Security and Privacy in Dynamic Environments for AI
• Research Proposal on Transfer Learning for Image and Video Pattern Matching with AI
• Research Proposal on Human Pose Estimation from Single Images for AI
• Research Proposal on Predictive models for teacher effectiveness in AI
• Research Proposal in AI for Early detection of chronic diseases using machine learning
• Research Proposal on Predictive algorithms for clinical trial dropout prediction in AI
• Research Proposal on Uncertainty modeling for robust decision-making in AI
• Research Proposal on AI-powered personalized medicine for cancer treatment
• Research Proposal on AI-based Predictive modeling for resource allocation in healthcare
• Research Proposal on Representation learning for disease prediction in medical imaging using AI
• Research Proposal on Natural language processing for voice-controlled IoT devices using AI
• Research Proposal on Multi-Agent Coordination Mechanisms in AI
• Research Proposal on Trustworthy AI Systems
• Research Proposal on Scalable Multi-Agent System Architecture and Infrastructure in AI
• Research Proposal on Semi-Supervised and Weakly-Supervised Image and Video Pattern Matching for AI
• Research Proposal on Human Pose Estimation from Depth Data for AI
• Research Proposal on Multi-modal Deep Metric Learning for Autonomous Driving with AI
• Research Proposal on Multi-Agent Communication and Coordination in Resource Constrained Systems for AI
• Research Proposal on Contextual Reasoning for Predictive Maintenance with AI
• Research Proposal on Visual Explanations for Image Classification with AI
• Research Proposal on Deep Learning for Autonomous Vehicle Navigation in AI
• Research Proposal on AI for Uncertainty quantification in neural network interpretability
• Research Proposal on AI-powered student behavior analysis and prediction
• Research Proposal on Human-in-the-loop Multi-modal Perception for AI
• Research Proposal on Multi-Agent Communication and Coordination in Dynamic Environments for AI
• Research Proposal on AI-based predictive maintenance in IoT
• Research Proposal on Visual Explanations for Object Detection and Segmentation using AI
• Research Proposal in AI for Autonomous Vehicle Navigation with Reinforcement Learning
• Research Proposal on Counterfactual Explanations for Fairness and Bias in AI
• Research Proposal on AI and Competition Policy
• Research Proposal on Scalable Multi-Agent System Performance Evaluation in AI
• Research Proposal on Image and Video Pattern Matching in Complex Environments for AI
• Research Proposal on Multi-modal Imitation and Reinforcement Learning for AI
• Research Proposal on Predictive models for student engagement and motivation in AI
• Research Proposal on AI-powered teacher training and support
• Research Proposal on Natural Language Processing for Clinical Text in AI
• Research Proposal on Federated learning for privacy-preserving medical data analysis using AI
• Research Proposal on Deep uncertainty modeling in generative models for AI
• Research Proposal on AI-based Development of robotic tools for assessment and feedback in education
• Research Proposal on Predictive Health Analytics for AI
• Research Proposal on Ensemble methods for uncertainty modeling in AI
• Research Proposal on Machine learning for cancer prognosis and survival prediction in AI
• Research Proposal on Multi-Agent System Security and Privacy in Ad Hoc Networks for AI
• Research Proposal on The Impact of AI on Jobs and Employment
• Research Proposal on Contextual Reasoning for Fairness and Bias in AI
• Research Proposal on Counterfactual Explanations in Decision Making with AI
• Research Proposal on Autonomous Vehicle Control with Deep Learning for AI
• Research Proposal on Scalable Multi-Agent System Monitoring and Debugging in AI
• Research Proposal on Epistemic and aleatoric uncertainty in AI
• Research Proposal on AI-based Transfer learning for improved cancer prediction in under-resourced settings
• Research Proposal on Interactive Visual Explanations for AI
• Research Proposal on Reinforcement Learning for Autonomous Vehicle Control in AI
• Research Proposal on Contextual Reasoning in Decision Making with AI
• Research Proposal on AI for real-time monitoring and management of smart city resources
• Research Proposal on AI for security and privacy in IoT
• Research Proposal on AI-based student engagement monitoring and improvement
• Research Proposal on Artificial intelligence for real-time monitoring of adverse events in clinical trials
• Research Proposal on Model uncertainty in deep neural networks for AI
• Research Proposal on Representation learning in medical imaging for cancer prediction using AI
• Research Proposal on User-Centered Explanations for Deep Learning Models in AI
• Research Proposal on Transfer Learning for Computer Vision using AI
• Research Proposal on Scalable Multi-Agent System Deployment in AI
• Research Proposal on Multi-Agent Communication and Coordination in Ad Hoc Networks for AI
• Research Proposal on Robust Image and Video Pattern Matching under variations for AI
• Research Proposal on AI-based Scene Understanding and Context-Aware Vision for Robotics and Autonomous Systems
• Research Proposal on Student engagement and motivation for AI
• Research Proposal on AI-driven process automation in business operations
• Research Proposal in AI for customer behavior prediction and market trend analysis
• Research Proposal on Deep learning for imaging-based biomarker discovery in clinical trials using AI
• Research Proposal on AI-enabled Transfer learning for disease prediction in under-resourced settings
• Research Proposal on Uncertainty-aware reinforcement learning for AI
• Research Proposal on Deep learning-based image analysis for medical imaging diagnosis using AI
• Research Proposal in AI for enhancing HR processes
• Research Proposal on Game-based and gamified learning for AI
• Research Proposal on Fair and Ethical Decision Making with AI
• Research Proposal on Explainable Predictive Maintenance with AI
• Research Proposal on User-Centered Explanations for Fraud Detection with AI
• Research Proposal in AI for Object Detection and Segmentation
• Research Proposal on AI Risk Management
• Research Proposal on Scalable Multi-Agent System Design in AI
• Research Proposal on Real-time Human Pose Estimation for AI
• Research Proposal on Personalized feedback generation in automated essay grading with AI
• Research Proposal on Development of robotic platforms for STEM education with AI
• Research Proposal on Transfer learning for efficient deployment of AI models in IoT devices
• Research Proposal on Predictive modeling for personalized medicine in AI
• Research Proposal on AI-based Machine learning for clinical trial design and optimization
• Research Proposal on Ethics and bias in AI-powered business decisions
• Research Proposal on Contextual Explanations in Deep Learning for AI
• Research Proposal on Explainable AI for Trust and Confidence
• Research Proposal on Scalable Multi-Agent Decision Making in AI
• Research Proposal on Ethical AI and Governance
• Research Proposal on Mechanism Design for Multi-Agent Systems using AI
• Research Proposal on Content-Based Image and Video Retrieval for AI
• Research Proposal on Human Pose Estimation in Challenging Scenes for AI
• Research Proposal on Predictive models for student dropout and retention in AI
• Research Proposal on Automated classroom administration using AI
• Research Proposal in AI for content creation and digital advertising
• Research Proposal on AI-enabled drug discovery and development
• Research Proposal on Predictive modeling for patient response to treatment using AI
• Research Proposal on Natural language processing for electronic health records analysis using AI
• Research Proposal on Gaussian processes for uncertainty modeling in AI
• Research Proposal on Personalized public services using AI in smart cities
• Research Proposal on AI-based Multi-omics data integration for cancer prediction and diagnosis
• Research Proposal on Deep Learning for Autonomous Vehicle Safety using AI
• Research Proposal on Explainable Visual Analytics for AI
• Research Proposal on Counterfactual Explanations for Deep Learning Models using AI
• Research Proposal on User-Centered Explanations for Healthcare with AI
• Research Proposal on Human-centered AI for Trust
• Research Proposal on Scalable Multi-Agent Resource Allocation in AI
• Research Proposal on Multi-Agent System Security Threats and Attacks in AI
• Research Proposal on Multi-Person Pose Estimation for AI
• Research Proposal on Multi-modal Scene Graph Generation for AI
• Research Proposal on Stackelberg Equilibrium in Multi-Agent Systems for AI
• Research Proposal on Predictive modelling of cancer treatment response using AI
• Research Proposal in AI for personalized marketing and customer segmentation
• Research Proposal on AI-assisted grading and feedback generation
• Research Proposal on Handling multi-modal input in automated essay grading with AI
• Research Proposal on Multi-modal Generative Adversarial Networks for AI
• Research Proposal on Multi-Task Scene Understanding and Context-Aware Vision for AI
• Research Proposal on Multi-Agent System Security and Privacy in Distributed Systems for AI
• Research Proposal on Large-Scale Image and Video Pattern Matching for AI
• Research Proposal on Transfer Reinforcement Learning for Autonomous Vehicles in AI
• Research Proposal on Counterfactual Reasoning in Machine Learning using AI
• Research Proposal on User-Centered Explanations in Decision Making with AI
• Research Proposal on Contextual Reasoning in Recommender Systems for AI
• Research Proposal on Non-monotonic Logics for AI
• Research Proposal on Visual Explanations for Decision Making with AI
• Research Proposal on Deep Learning for Autonomous Vehicle Motion Planning with AI
• Research Proposal on Machine learning for personalized medicine and disease prediction in AI
• Research Proposal on Explainable AI for decision-making in cancer diagnosis and treatment using AI
• Research Proposal on Adversarial machine learning for robust medical AI systems
• Research Proposal on Predictive patient monitoring and triage for AI
• Research Proposal on Multi-modal learning with educational robots for AI
• Research Proposal on Multi-modal Attention Mechanisms for AI
• PhD Guidance and Support Enquiry
• Masters and PhD Project Enquiry
• PhD Research Guidance in Artificial Intelligence
• PhD Research Guidance in Machine Learning
• Research Topics in Artificial Intelligence
• Research Topics in Machine Learning
• PhD Research Proposal in Artificial Intelligence
• PhD Research Proposal in Machine Learning
• Latest Research Papers in Artificial Intelligence
• Latest Research Papers in Machine Learning
• Literature Survey in Artificial Intelligence
• Literature Survey in Machine Learning
• PhD Thesis in Artificial Intelligence
• PhD Thesis in Machine Learning
• PhD Projects in Artificial Intelligence
• PhD Projects in Machine Learning
• Leading Journals in Artificial Intelligence
• Leading Journals in Machine Learning
• Leading Research Books in Artificial Intelligence
• Leading Research Books in Machine Learning
• Research Topics in Federated Learning
• Research Topics in Medical Machine Learning
• Research Topics in Depression Detection based on Deep Learning
• Research Topics in Recent Advances in Deep Recurrent Neural Networks
• Research Topics in Multi-Objective Evolutionary Federated Learning
• Research Topics in Recommender Systems based on Deep Learning
• Research Topics in Computer Science
• PhD Thesis Writing Services in Computer Science
• PhD Paper Writing Services in Computer Science
• How to Write a PhD Research Proposal in Computer Science
• Ph.D Support Enquiry
• Project Enquiry
• Research Guidance in Artificial Intelligence
• Research Proposal in Artificial Intelligence
• Research Papers in Artificial Intelligence
• Ph.D Thesis in Artificial Intelligence
• Research Projects in Artificial Intelligence
• Project Titles in Artificial Intelligence
• Project Source Code in Artificial Intelligence

Global main menu

• School of Electronic Engineering and Computer Science
• PhD studentships

DeepMind PhD Studentship in AI or Machine Learning

About the Studentship

Queen Mary University of London is inviting applications for the DeepMind PhD Studentship for September 2023. 

The DeepMind PhD Studentship programme is established at Queen Mary University of London in partnership with leading British AI company, DeepMind. 

The PhD Studentship supports and encourages under-represented groups, namely female and Black researchers, to pursue postgraduate research in AI or Machine Learning. 

The PhD DeepMind Studentship will cover tuition fees and offer a London weighted stipend of £19,668 per year minimum together with an annual £2,200 travel and conference allowance and a one-off equipment grant of £1,700.

• 3-year fully-funded PhD Studentship
• Access to cutting-edge facilities and expertise in AI
• Partnership and mentorship with DeepMind employees working at the cutting edge of AI research and technologies.

Who can apply Queen Mary is on the lookout for the best and brightest students in the fields of AI and Machine Learning. 

Successful applicants will have the following profile:

• Identify as female and/or are of Black ethnicity, each being under-represented groups in the field of Artificial Intelligence and Computer Science
• Should hold, or is expected to obtain an MSc in Computer Science, Electronic Engineering, AI, Physics or Mathematics or a closely related discipline; or can demonstrate evidence of equivalent work experience
• Having obtained distinction or first-class level degree is highly desirable
• Programming skills are strongly desirable; however, we do not consider this to be an essential criterion if candidates have complementary strengths. 

We actively encourage applications from candidates who are ordinarily resident in the UK. The studentship is also open to International applicants. 

About the School of Electronic Engineering and Computer Science at Queen Mary

The PhD Studentship will be based in the School of Electronic Engineering and Computer Science (EECS) at Queen Mary University of London. As a multidisciplinary School, we are well known for our pioneering research and pride ourselves on our world-class projects. We are 8th in the UK for computer science research (REF 2021) and 7th in the UK for engineering research (REF 2021). The School is a dynamic community of approximately 350 PhD students and 80 research assistants working on research centred around a number of research groups in several areas, including Antennas and Electromagnetics, Computing and Data Science, Communication Systems, Computer Vision, Cognitive Science,  Digital Music, Games and AI, Multimedia and Vision, Networks, Risk and Information Management, Robotics and Theory

For further information about research in the school of Electronic Engineering and Computer Science, please visit: http://eecs.qmul.ac.uk/research/ .

How to apply

Queen Mary is interested in developing the next generation of outstanding researchers - whether in academia, industry or government – therefore the project undertaken under this Studentship is expected to fit into the wider research programme of School. Applicants should select a supervisor (a first and second choice) from the School at application stage. Visit our website for information about our research groups and supervisors:  eecs.qmul.ac.uk/phd/phd-opportunities/

Applicants should submit their interest by returning the following to  [email protected] by 12pm (noon), 10 April 2023:

• Indicate first and second choice academic supervisor 
• CV (max 2 pages) 
• Cover letter (max 4,500 characters)
• Research proposal (max 500 words) 
• 2 References 
• Certificate of English Language (for students whose first language is not English) 
• Other Certificates  

Application deadline: 10 April 2023

Applications will be reviewed by a panel of academic staff: May 2023

Interviews:  April/May 2023

Start date:  September 2023

PhD Assistance

Artificial intelligence research topics for phd manuscripts 2021, introduction.

Imagine a world where knowledge isn’t limited to humans!!! A world in which computers will think and collaborate with humans to create a more exciting universe. Although this future is still a long way off, Artificial Intelligence has made significant progress in recent years. In almost every area of AI, such as quantum computing, healthcare, autonomous vehicles, the internet of things, robotics, and so on, there is a lot of research going on. So much so that the number of annual Published Research Papers on Artificial Intelligence has increased by 90% since 1996.

Keeping this in mind, there are several sub-topics on which you can concentrate if you want to study and write a thesis on Artificial Intelligence. This article covers a few of these subjects and provides a short overview. Here some of the recent Research Topics ,

• Artificial Intelligence and Machine learning – Recent Trands
• How AI and ML can aid healthcare systems in their response to COVID-19
• Machine learning and artificial intelligence in haematology
• Tackling the risk of stranded electricity assets with machine learning and artificial intelligence

Deep Learning

Deep Learning is a type of machine learning that learns by simulating the internal workings of the human brain in order to process data and make decisions.Deep Learning is a form of machine learning that employs artificial neural networks. These neural networks are linked in a web-like structure, similar to the human brain’s networks (basically a condensed version of our brain!).

Artificial neural networks have a web-like structure that allows them to process data in a nonlinear manner, which is a major advantage over conventional algorithms that can only process data in a linear manner. Rank Brain, one of the variables in the Google Search algorithm, is an example of a deep neural network.

Recent research topics

• Artificial intelligence & deep learning : PET and SPECT imaging
• Hierarchical Deep Learning Neural Network (HiDeNN): A computational science and engineering in AI architecture.
• AI for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using Deep Learning
• Deep learning-enabled medical computer vision

Reinforcement Learning

Reinforcing Learning is an aspect of Artificial Intelligence in which a computer learns something in the same way as humans do. Assume the computer is a student, for example. Over time, the hypothetical student learns from its errors. As a outcome of trial and error, Reinforcement Machine Learning Algorithms learn optimal behaviour.

This means that the algorithm determines the next way to proceed by learning behaviours based on its current state that will increase the reward in the future. This also works for robots, just as it does for humans!

Google’s AlphaGo Computer Programme , for example, used Reinforcement Learning to defeat the world champion in the game of Go (a human!) in 2017.

• Experimental quantum speed-up in reinforcement learning agents
• Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety

Robotics is an area concerned with the creation of humanoid robots that can assist humans and perform several acts. In certain cases, robots can behave like humans, but can they think like humans as well?

Kismet, a social interaction robot developed at M.I.T.’s Artificial Intelligence Lab, is an example of this. It understands human body language as well as our voice and responds to them appropriately. Another example is NASA’s Robonaut, which was designed to assist astronauts in space.

• Regulating artificial intelligence and robotics: ethics by design in a digital society
• Regional anaesthesia :usages of artificial intelligence and robotics in
• Third Millennium Life Saving Smart Cyberspace Driven by AI and Robotics

Natural Language Processing

Humans can obviously communicate with each other by speech, but now machines can as well! This is known as Natural Language Processing, and it involves machines analysing and understanding language and expression as it is spoken (which means that if you speak to a computer, it might only respond!).  Speech recognition, natural language production, natural language translation, and other aspects of NLP are all concerned with language. NLP is recently very important in customer service applications, particularly chatbots. These chatbots use machine learning and natural language processing to communicate with users in textual form and respond to their questions. As a result, you get a personal touch in your customer service experiences without actually speaking with a human.

Here are several research papers in the field of Natural Language Processing that have been published. You can look at them to get more ideas for research and thesis topics on this subject.

• Natural Language Processing–Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study
• Sympathetic the temporal evolution of COVID-19 Research Through machine learning and natural language processing

Computer Vision

The internet is full of images! This is the selfie age, and taking and posting a photo has never been easier. Each day, millions of images are uploaded to the internet and viewed. It’s important for computers to be able to see and understand images in order to make the most of the vast amount of images available online. And, while humans can do this without thinking about it, computers find it more difficult! This is where Computer Vision enters the image.

To extract information from images, Computer Vision utilizes Artificial Intelligence. This knowledge may include object detection in the image, image content recognition to group images together, and so on. Navigation for autonomous vehicles using images of the surroundings is one use of computer vision, such as AutoNav, which was used in the Spirit and Opportunity rovers that landed on Mars.

• Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning
• An Open‐Source Computer Vision Tool for Automated Vocal Fold Tracking From Video endoscopy

Recommender Systems

Do you get movie and series recommendations from Netflix based on your previous choices or favourite genres? This is achieved by Recommender Systems, which offer you advice about what to do next from the vast array of options available online. Content-based Recommendation or even Collaborative Filtering may be used in a Recommender System.

The content of all the products is analysed in Content-Based Recommendation. For example, based on Natural Language Processing performed on the books, you might be recommended books that you may enjoy. Collaborative Filtering, on the other hand, analyses your past reading behaviour and then recommends books based on it.

• Artificial intelligence in recommender systems
• Deep Transfer Tensor Decomposition with Orthogonal Constraint for Recommender Systems.
• Recommender systems for configuration knowledge engineering

Internet Of Things

Artificial intelligence is concerned with the creation of systems that can learn to perform human-like tasks based on prior experience and without the need for human interaction. The Internet of Things, on the other hand, is a network of different devices linked to the internet and capable of collecting and exchanging data.

All of these IoT devices now generate a large amount of data, which must be collected and mined in order to produce actionable results. Artificial Intelligence enters the picture at this stage. The Internet of Things is used to collect and manage the massive amounts of data that Artificial Intelligence algorithms need.  As a consequence, these algorithms transform the data into useful actionable results that IoT devices can use.

• Enhanced Medical Systems by using Artificial Intelligence and Internet of Things
• Artificial Intelligence and Internet of Things in Instrumentation and Control in Waste Biodegradation Plants: Recent Developments
• AIoT-Artificial Intelligence of Things

In this blog discussed the recent enhancement for artificial intelligences and their sub field. This will help to the PhD scholar who are interested to research in artificial intelligences domain.

• Shouval, R., Fein, J. A., Savani, B., Mohty, M., & Nagler, A. (2021). Machine learning and artificial intelligence in haematology. British journal of haematology, 192(2), 239-250.
• van der Schaar, M., Alaa, A. M., Floto, A., Gimson, A., Scholtes, S., Wood, A., … & Ercole, A. (2021). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning, 110(1), 1-14.
• Nyangon, J. (2021). Tackling the risk of stranded electricity assets with machine learning and artificial intelligence. In Sustainable Energy Investment-Technical, Market and Policy Innovations to Address Risk. IntechOpen.
• Saha, S., Gan, Z., Cheng, L., Gao, J., Kafka, O. L., Xie, X., … & Liu, W. K. (2021). Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering. Computer Methods in Applied Mechanics and Engineering, 373, 113452.
• Mascagni, P., Vardazaryan, A., Alapatt, D., Urade, T., Emre, T., Fiorillo, C., … & Padoy, N. (2021). Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Annals of Surgery.
• Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., … & Socher, R. (2021). Deep learning-enabled medical computer vision. npj Digital Medicine, 4(1), 1-9.
• artificial intelligence phd topics
• artificial intelligence topics for research paper
• computer science research topics for phd
• PhD research topics in artificial intelligence
• recent phd research topics in artificial intelligence
• research topic for artificial intelligence
• research topics in computer science for phd
• research topics in computer vision

Quick Contact

• Adversial Attacks
• Artificial Intelligence
• Artificial Intelligence (AI) and ML ( Machine Learning )
• Big Data Analysis
• Business and Management
• Categories of Research methodology – PhDAssistance
• Category of Research Proposal Services
• coding & algorithm
• Computer Data Science
• Category of Machine Learning – PhDassistance
• Computer Science/Research writing/Manuscript
• Course Work Service
• Data Analytics
• Data Processing
• Deep Networks
• Dissertation Statistics
• economics dissertation
• Editing Services
• Electrical Engineering Category
• Engineering & Technology
• finance dissertation writing
• Gap Identification
• Healthcare Dissertation Writing
• Intrusion-detection-system
• journals publishing
• Life Science Dissertation writing services
• literature review service
• Machine Learning
• medical thesis writing
• Peer review
• PhD Computer Programming
• PhD Dissertation
• Phd Journal Manuscript
• Annotated Bibliography
• PhD Publication Support
• Phd thesis writing services
• Phd Topic Selection
• Categories of PhdAssistance Dissertation
• Power Safety
• problem identification
• Quantitative Analysis
• quantitative research
• Recent Trends
• Referencing and Formatting
• Research Gap
• research journals
• Research Methodology
• research paper
• Research Proposal Service
• secondary Data collection
• Statistical Consulting Services
• Uncategorized

We use cookies to help our site work, to understand how it is used, and to tailor ads that are more relevant to you and your interests.

By accepting, you agree to cookies being stored on your device. You can view details and manage settings at any time on our cookies policy page.

PhD on artificial intelligence for renewable energy and sustainability

Fully-funded PhD in the area of artificial intelligence for renewable energy and sustainability.

Application deadline

Funding information.

A stipend of £19,000 for 22/23, which will increase each year in line with the UK Research and Innovation (UKRI) rate, plus Home rate fee allowance of £4,596 (with automatic increase to UKRI rate each year). The studentship is offered for 3.5 years. For exceptional international candidates, there is the possibility of obtaining a scholarship to cover overseas fees.

Supervised by

Dr Erick Sperandio Nascimento

Prof Prashant Kumar

Renewable energy sources have gained increased attention and investments from the industries, governments and society, such as wind, solar, and hydrological sources, to enable a more sustainable and yet economically feasible development. However, the building and operationalization of renewable power plants face a series of challenges that must be tackled in order to improve their adoption. One of the main challenges resides in the ability to accurately predict the meteorological parameters that influence the generation of wind and solar energy from shorter to longer term, which becomes even more challenging in the face of climate change.

Therefore, this project aims at researching, developing and building AI-based solutions that can support the development of more reliable and accurate weather forecasting systems applied to the prediction of solar and wind energy generation, extreme weather events forecasting and their effects, air quality and sustainability. Historical data from publicly available sources will be used, like surface weather stations, GDAS/ECMWF/Era5 and satellite data, among others, along with information about wind turbines and photovoltaic cells.

We seek for exceptional candidates that are willing to develop AI-based clean air solutions by researching and building cutting-edge approaches and techniques in the fields of deep learning, physics-informed and graph neural networks, spatial-temporal modelling, model explainability and interpretability, time series foundation models, physical modelling and data-driven approaches, among others, applied to the challenges related to the fields of renewable energies and sustainability.

The applicant will be directly involved with research activities in the Global Centre for Clean Air Research (GCARE) and the People-Centred AI Institute, both in the University of Surrey, having access to an amazing set of resources, infrastructure and people engaged to deliver world-class researches and technologies with a focus on the well-being of people and on the scientific and technological development of the academia, industry and society.

Related links

Eligibility criteria.

This studentship is open to UK and international candidates.

All applicants should have (or expect to obtain) a first-class degree in a numerate discipline (mathematics, science or engineering) or MSc with distinction (or 70% average) and a strong interest in pursuing research in this field.

Additional experience which is relevant to the area of research is also advantageous.

English language requirements

IELTS minimum 6.5 overall with 6.0 in writing, or equivalent.

How to apply

Applications should be submitted via the PhD Vision, Speech and Signal Processing programme .

In place of a research proposal you should upload a document stating the title of the project that you wish to apply for and the name of the relevant supervisor.

Studentship FAQs

Read our  studentship FAQs  to find out more about applying and funding.

03 March 2023

Contact details

Erick giovani sperandio nascimento.

Studentships at Surrey

We have a wide range of studentship opportunities available.

• Visits and Open Days
• Jobs and vacancies
• Undergraduate
• Postgraduate
• Accommodation
• Student Guide
• Student email
• Library and IT services
• Staff Guide
• Staff email
• Timetabling

Artificial Intelligence and Data Science Group

Featured story.

Suggested PhD Projects

Here are some suggested topics for PhD projects from our group members. These projects are merely suggestions and illustrate the broad interests of the research group. Potential research students are encouraged and welcome to produce their own suggestions in these research areas or other research areas in the field. All applicants are invited to contact the academic associated with the project when making an application.

Machine Learning for the Pharmacology of Ageing

Contact:  alex freitas.

Recently, there has been a growing interest in ageing research, since the proportion of elderly people in the world’s population is expected to increase substantially in the next few decades. As people live longer, it becomes increasingly more common for a person to suffer from multiple age-related diseases. Since old age is the ultimate cause or the greatest risk factor for most of these diseases, progress in ageing research has the potential to lead to a more cost-effective treatment of many age-related diseases in a holistic fashion. In this context, researchers have collected a significant amount of data about ageing-related genes and medical drugs affecting an organism’s longevity – mainly about simpler model organisms, rather than humans. This data is often freely available on the web, which has facilitated the application of machine learning methods to the pharmacology or biomedicine of ageing, to try to discover some knowledge or patterns in such datasets. This project will focus on developing machine learning algorithms for analysing data about the pharmacology of ageing, i.e., data about medical drugs or chemical compounds that can be used as an intervention against ageing, mainly in model organisms. The broad type of machine learning method to be developed will be supervised machine learning (mainly classification), but the specific type of algorithm to be developed will be decided later, depending on the student’s interest and suitability to the target datasets. Note that, although this is an interdisciplinary project, this is a project for a PhD in Computer Science, so the student will be expected to develop a novel machine learning method. As examples of interdisciplinary papers on machine learning for ageing research, see e.g. (the first paper is particularly relevant for this project, whilst the second includes a broader discussion about machine learning for ageing research):

Relevant References:

D.G. Barardo, D. Newby, D. Thornton, T. Ghafourian, J.P. de Magalhaes and A.A. Freitas. Machine learning for predicting lifespan-extending chemical compounds. Aging (Albany NY), 9(7), 1721-1737, 2017.

Fabris, J.P. de Magalhaes, A.A. Freitas. A review of supervised machine learning applied to ageing research. Biogerontology, 18(2), 171-188, April 2017.

Machine Learning with Fairness-Aware Classification Algorithms

This project involves the classification task of machine learning, where an algorithm has to predict the class of an object (e.g. a customer or a patient) based on properties of that object (e.g. characteristics of a customer or patient). There are now many types of classification algorithms, and in general these algorithms were designed with the only (or main) goal of maximizing predictive performance. As a result, the application of such algorithms to real-world data about people often leads to predictions which have a good predictive accuracy but are unfair, in the sense of discriminating (being biased) against certain groups or types of people – characterized e.g. by values of attributes like gender or ethnicity. In the last few years, however, there has been a considerable amount of research on fairness-aware classification algorithms, which take into account the trade-off between achieving a high predictive accuracy and a high degree of fairness. The project will develop new classification algorithms to cope with this trade-off, focusing on classification algorithms that produce interpretable predictive models, rather than black box models.

[1] Friedler, A.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P. and Roth, D. A comparative study of fairness-enhancing interventions in machine learning. Proc. 2nd ACM Conf. on Fairness, Accountability and Transparency (FAT’19), 329-338. ACM Press, 2019.

[2] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. A survey on bias and fairness in machine learning. arXiv preprint: arXiv:1908.09635. 2019.

Cognition-enabled lifelong robot learning of behavioural and linguistic experience

Contact:  ioanna giorgi.

Present-day cognitive robotics models draw on a hypothesised developmental paradigm of human cognitive functions to devise low-order skills in robots, such as perception, manipulation, navigation and motor coordination. These methods exploit embodied and situated cognition theories that are rooted in motor behaviour and the environment. In other words, the body of a physical artefact (e.g., a robot) and its interactions with the environment and other organisms in it contribute to the robot’s cognition. However, it is not clear how these models can explain or scale up to the high-level cognitive competence observed in human behaviour (e.g., reasoning, categorisation, abstraction and voluntary control). One approach to model robot learning of behavioural and cognitive skills is in incremental and developmental stages that resemble child development. According to child psychology and behaviour, conceptual development starts from perceptual clustering (e.g., prelinguistic infants grouping objects by colour) and progresses to nontrivial abstract thinking, which requires a fair amount of language . Thus, to solve the problem of modelling high-level cognitive skills in robots, language, in interaction with the robot’s body, becomes inseparable from cognition. This project is aimed at following a cognitive and developmental approach to robot learning that will allow robots to acquire behavioural and linguistic skills at a high level of cognitive competence and adaptation as humans. This learning should be lifelong : humans apply earlier-learned skills to make sense of continuous novel stimuli, which allows them to develop, grow and adjust to more complex practices. One such cognitive robot can be used across various themes: human-robot interaction using theory of mind (ToM) skills for robots, social robots and joint human-robot collaboration.

Note: The Cognitive Robotics and Autonomous Systems (CoRAS) laboratory at the School of Computing has access to several humanoids (NAO) and socially interactive robot platforms (Buddy Pro, Q.BO One, Amy A1), mobile robots (Turtlebot Waffle Pi, Burger), pet-like companion robots and gadgets like AR Epson glasses and Microsoft HoloLens.

Attention model for agent social learning during human-robot interaction.

Successful human-robot interaction requires that robots learn by observing and imitating human behaviour. The theory of learning behaviour through observation is referred to as social learning . Behavioural learning can also be enhanced by the environment itself and through reinforcement (i.e., establishing and encouraging a pattern of behaviour). One important component of such learning is cognitive attention , which deals with the degree to which we notice a behaviour. Cognitive attention renders some inputs more relevant while diminishing others, with the motivation that more focus is needed for the important stimuli in the context of social learning. Attention brings forth positive reinforcement (reward) or negative reinforcement (punishment). If the reward is greater than the punishment, behaviour is more likely to be imitated and reciprocated. In human-robot interaction, attention is crucial for two reasons: 1) to respond or reciprocate the behaviour appropriately during the interaction, and 2) to learn or imitate that behaviour for contingencies. This project is aimed at devising a cognitive attention model of a robot for social learning. The model will include memory, reasoning, language and multi-sensory data processing, i.e., “natural” stimuli during the interaction such as from vision, speech and sensorimotor experience. It can be based on a cognitive architecture approach or alternative computational approaches. The solution should ideally be encompassing multiple aspects of interaction (verbal and non-verbal), but it can also focus on such specific aspects (e.g., visual attention or intention reading).

How can a robot learn skills from a human tutor

Contact:  giovanni masala.

The aim of this project is to enhance robot learning from a human tutor, similar to a child who learns from a human teacher. The agent will develop the ability to communicate through natural language from scratch, by interacting with a tutor, recognising their verbal and non-verbal inputs as well as emotions, and, finally, grounding the word meaning in the external environment. The project will start from an existing neuro-cognitive architecture under development [1], based on a Human-like approach to learning, progressively incrementing knowledge and language capabilities through experience and ample exposure, using a corpus based on early language lexicons (preschool literature). The robot will integrate with visuospatial information-processing mechanisms for embodied language acquisition, exploiting affective mechanisms of emotion detection for learning and cognition. The agent will be embodied into a humanoid robot as opposed to a computer or a virtual assistant, to enable real-world interactions with the humans and the external environment, to learn and refine its natural language understanding abilities guided or depending on the teacher’s emotions and visual input (object associations with the words, facial expression, and gestures). Emotions will influence the cognitive attention of the robotic agent, modulating the selectivity of attention on specific tasks, words, and objects, and motivating actions and behaviour.

[1] Golosio B, Cangelosi A, Gamotina O, MASALA GL, A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language. PLoS ONE 10(11): e0140866, 2015.

Note: The Cognitive Robotics and Autonomous Systems (CoRAS) laboratory at the School of Computing has access to several humanoids (NAO) and socially interactive robot platforms (Buddy Pro, Q.BO One, Amy A1), mobile robots (Turtlebot Waffle Pi, Burger), pet-like companion robots and devices like AR Epson glasses and Microsoft HoloLens.

Explainability and Interpretability of Machine/Deep learning techniques in medical imaging

In medicine is very important the acceptance of Machine Learning systems not only in terms of performance but also considering the degree to which a human can understand the cause of a decision. Nowadays, the application of Computer Aided Detection Systems in radiology is often based on Deep Learning Systems thanks to their high performance. In general, more accurate models are less explainable  and there is a scientific interest in the field of Explainable Artificial Intelligence, to develop new methods that explain and interpret ML models. There is not a concrete mathematical definition for interpretability or explainability, nor have they been measured by some metric; however, a number of attempts have been made in order to clarify not only these two terms but also related concepts such as comprehensibility. A possible target (but other medical diseases are allowed) of this research is a model to discover the severity of Breast Arterial Calcifications. Breast arterial calcification (BAC) is calcium deposition in peripheral arterioles. There is increasing evidence that BAC is a good indicator of the risk of cardiovascular disease. The accurate and automated detection of BACs in mammograms remains an unsolved task and the technology is far from clinical deployment. The challenging task is to develop an explainable model applicable to BAC detection, able to discriminate between severe and weak BACs in patients’ images.

Autonomous car makes me sick

Contact:  palaniappan ramaswamy.

With the rapid advancements in autonomous car technology, we will soon see cars driving on their own on the roads. While some may dread this lack of control in fear of safety, generally it is much safe and the real issue lies elsewhere. Do you know that many of us will feel sick – motion sickness will become a huge problem and there is not much ongoing work to mitigate this situation.  In this project, we will explore using transcutaneous auricular vagus nerve stimulation (taVNS) as an intervention technology. VNS is a medically approved technology for conditions such as epilepsy. But here we will study the non-invasive version of VNS in mitigating the effects of motion sickness. Functional near infra-red spectroscopy (fNIRS) will be utilised to assess the effect of the taVNS on motion sickness. Some prior signal processing knowledge will be required but knowledge on VNS and fNIRS can be gained from the project. 

Stress management

The fundamental aspect of human experience is awareness. Combined with the ability to think, imagine and understand it results into the beautiful cosmic play we experience. However, with it comes along a multitude of problems, often illusory in nature – such as stress, anxiety, anger, negativity, etc. It isn’t hard to guess that in such states our behaviour is significantly altered, usually in harmful ways for both – us and the environment. There are techniques such as meditation, music, humour which can help us come back to our “real” senses and feel happier/peaceful again. So the fundamental enquiry would be about what sort of things do help us achieve a happier state, and moreover what’s their impact on both short term and long term brain functioning. This project will study this aim using EEG.

Information Visualisation Directed by Graph Data Mining

Contact:  peter rodgers.

Data visualisation techniques are failing in the face of large data sets. This project attempts to increase the scale of graph data that can be visualised by developing data mining techniques to guide interactive visualisation. This sophisticated combining of information visualisation and data mining promises to greatly improve the size of data understandable by analysts, and will advance the state of the art in both disciplines. On successful completion, publications in high quality venues are envisaged. This project is algorithmically demanding, requiring good coding skills. The implementation language is negotiable, but Java, JavaScript or C++ are all reasonable target languages. Data will be derived from publicly available network intrusion or social network data sets. Tasks in this research project include: (1) implementing graph display software and interface. (2) developing project specific visualisation algorithms. (3) integrating graph pattern matching and other graph data mining systems into the visualisation algorithms.

Visual Analytics for Set Data

Visual Analytics is the process of gaining insights into data through combining AI and information visualization. At present, visual analytics for set based data is largely absent. There are a large number of sources for set based data, including social networks as well as medical and biological information. This project will look at producing set mining algorithms which can then be used to support set visualization methods such as Euler/Venn diagrams or Linear diagrams. Firstly, the use of existing data mining methods will produce useful information about sets and the data instances in them. After this effort, more complex algorithms for subset and set isomorphism will be developed to allow for pattern matching within set data. These set mining methods will be integrated into Euler diagram based exploratory set visualization techniques.

Using Soft Nanomembrane Electronics for Home-based Anxiety Monitoring

Contact:  jim ang.

Sensor-enhanced virtual reality systems for mental health care and rehabilitation. New immersive technologies, such as  virtual reality (VR) and augmented reality (AR) are playing an increasingly important role in the digital health revolution. Significant research has been carried out at University of Kent, in collaboration with medical scientists/practitioners, psychiatrists/psychologists, digital artists and material scientists (for novel sensor design and integration with VR). Such projects include designing VR for dementia care, eating disorder therapy, eye disorder therapy and VR-enabled brain-machine interactions. This PhD research can take on the following directions: (1) Co-design of VR for a specific healthcare domains, involving key stakeholders (e.g. patient representatives, clinicians, etc) to  understand the design and deployment opportunities and challenges in realistic health contexts. (2) Deploy and evaluate VR prototypes to study the impact of the technologies in the target groups. (3) Design and evaluate machine learning algorithms to analyse behavioural and physiological signals for clinical meaningful information, e.g. classification of emotion, detection of eye movement, etc. 

Relevant publications: 

[1] M Mahmood, S Kwon, H Kim, Y Kim, P Siriaraya, J Choi, B Otkhmezuri, K Kang, KJ Yu, YC Jang, CS Ang, W Yeo (2021) Wireless Soft Scalp Electronics and Virtual Reality System for Motor Imagery ‐ Based Brain–Machine Interfaces. Advanced Science. 8(19). 

[2] S Mishra, K Yu, Y Kim, Y Lee, M Mahmood, R Herbert, CS Ang, W Yeo, J Intarasirisawat, Y Kown, H Lim (2020). Soft, wireless periocular wearable electronics for real-time detection of eye vergence in a virtual reality toward mobile eye therapies. Science Advances. 6 (11), eaay1729. 

[3] L Tabbaa, CS Ang, V Rose, P Siriaraya, I Stewart, KG Jenkins, M Matsangidou (2019) Bring the Outside In: Providing Accessible Experiences Through VR for People with Dementia in Locked Psychiatric Hospitals, Proceedings of the CHI 2019 Conference on Human Factors in Computing Systems. 

[4] M Matsangidou, B Otkhmezuri, CS Ang, M Avraamides, G Riva, A Gaggioli, D Iosif, M Karekla (2020). “Now I can see me” designing a multi-user virtual reality remote psychotherapy for body weight and shape concerns. Human–Computer Interaction. 1-27.

Optimisation of Queries over Virtual Knowledge Graphs

Contact:  elena botoeva.

Virtual Knowledge Graphs (also known as Ontology-Based Data Access) provide user-friendly access to Big Data stored in (possibly multiple) data sources, which can be traditional relational ones or more novel ones such as document and triple stores. In this framework an ontology is used as a conceptual representation of the data, and is connected to the data sources by the means of a mapping. User formulates queries over the ontology using a high-level query language like SPARQL; user queries are then automatically translated into queries over the underlying data sources, and the latter are executed by the database engines. Efficiency of the whole approach is highly dependent on optimality of the data source queries. While the technology is quite developed when the underlying data sources are relational, there are still many open problems when it comes to novel data sources, such as MongoDB, graph databases etc. The objective of this PhD project is to design novel techniques for optimising data source queries arising in the context of Virtual Knowledge Graphs.

Heuristics for Scalable Verification of Neural Networks

Due to the success of Deep Learning neural networks are now being employed in a variety of safety-critical applications such as autonomous driving cars and aircraft landing. Despite showing impressive results at various tasks, neural networks are known to be vulnerable (hence, not robust) to adversarial attacks: imperceptible to human eye perturbations to an input can lead to incorrect classification. Robustness verification of neural networks is currently a very hot topic both in academia and industry as neural networks. One of the main challenges in this field is deriving efficient techniques that can verify networks with hundred thousands / millions of neurons in reasonable time, which is not trivial given that exact verification is not tractable (NP- or coNP-complete for ReLU-based neural networks depending on the exact verification problem). Incomplete approaches generally offer better scalability but at the cost of completeness. The aim of the proposed PhD project will be to learn heuristics for efficient verification of neural networks.

Understanding Spiking Neural Networks

Contact:  dominique chu.

Spiking Neural Networks (SNN) are brain-like neural networks. Unlike standard rate coding neural networks, signals are encoded in time. This makes them ideal for processing data that has a temporal component, such as time-series data, video or music. Another advantage of SNNs is that there exists neuromorphic hardware that can efficiently simulate SNNs. SNNs are generally thought to be “more powerful” than standard rate coding networks. However, it is not clear precisely in what sense they are more powerful, or what precisely it is that makes them more powerful. The idea of this project is to investigate this claim using a combination of mathematical and computational methods. As such the project will require an interdisciplinary research methodology at the interface between mathematics, computer science and neuroscience. The project would be suitable for a student who wishes to become and expert in an up-and-coming method in artificial intelligence. It has the scope for both theoretical investigations, but will also require implementing neural networks.

Training algorithms for spiking neural networks

Spiking neural networks encode information through the temporal order of the signals. They are more realistic models of the brain than standard artificial neural networks and they are also more efficient in encoding information. Spiking neural networks are therefore very popular in brain simulations. A disadvantage of spiking neural networks is that there are not many efficient training algorithms available. This project will be about finding novel training algorithms for spiking neural networks and to compare the trained networks with standard artificial neural networks on a number of benchmark AI tasks. An important part of this project will be not only to evaluate how well these spiking neural networks perform in relation to standard networks, but also to understand whether or not they are, as is often claimed, more efficient in the sense that they need smaller networks or fewer computing resources. The main approach of the model will be to gain inspiration from existing theories about how the how the human brain develops and learns. These existing theories will then be adapted so as to develop efficient training algorithms. This project will be primarily within AI, but it will also provide the opportunity to learn and apply techniques and ideas from computational neuroscience.

Machine learning systems to improve medical diagnosis

Contact:  daniel soria.

Research shows that machine learning methods are extremely useful to discover or identify patterns that can help clinicians to tailor treatments. However, the implementation of those data mining procedures may be challenging because of high dimensional data sets, and the choice of proper machine learning methods may be tricky. 

The aim of the research project will be to design and develop new intelligent machine learning systems with high degree of flexibility suitable for disease prediction/diagnosis, that are also easily understandable and explicable to non-experts in the field. Data will be sought from the UK Biobank, to examine whether the selected features are correlated with the occurrence of specific diseases (e.g., breast cancer), whether these relationships persist in the presence of covariates, and the potential role of comorbidities (e.g., obesity, diabetes and cardiovascular diseases) in the assessment of the developed models

How creative are crime-related texts and what does this tell us about cyber crime?

Contact:  shujun li ,   anna jordanous.

The main aim of the PhD project is to investigate if crime-related texts can be evaluated in terms of creativity using automatic metrics. Such a study will help understand how crime-related texts are crafted (by criminals and by automated tools, possibly via a hybrid human-machine teaming approach), how they have evolved over time, how they are perceived by human receivers, and how new methods can be developed to educate people about tactics of cyber criminals. The four tasks of the PhD project will include the following: (1) collecting a large datasets of crime-related texts; (2) developing some objective (automatable) creativity metrics using supervised machine learning, targeted towards evaluating the creativity of crime-related texts (e.g., phishing emails, online hate speech, grooming, cyber bullying, etc.); (3) applying the creativity metrics to the collected data to see how malevolent creativity has evolved over years and for different crimes; (4) exploring the use of generative AI algorithms to create more creative therefore more deceptive crime-related texts.

Computational creativity and automated evaluation

Contact:  anna jordanous.

In exploring how computers can perform creative tasks, computational creativity research has produced many systems that can generate creative products or creative activity. Evaluation, a critical part of the creative process, has not been employed to such a great extent within creative systems. Recent work has concentrated on evaluating the creativity of such computational systems, but there are two issues. Firstly, recent work in evaluation of computational creativity has consisted of the system(s) being evaluated by external evaluators, rather than by the creative system evaluating itself, or evaluation by other creative software agents that may interact with that system. Incorporation of self-evaluation into computational creativity systems *as part of guiding the creative process* is also under explored. In this project the candidate will experiment with incorporating evaluation methods into a creative system and analyse the results to explore how computational creativity systems can incorporate self-evaluation. The creative systems studied could be in the area of musical or linguistic creativity, or in a creative area of the student’s choosing. It is up to the student to decide whether to focus on evaluation methods for evaluating the quality of output from a creative system or the creativity of the system itself (or both). The PhD candidate would be required to propose how they would will explore the above scenarios, for a more specific project. Anna is happy to guide students in this and help them develop their research proposal.

Expressive musical performance software

Traditionally, when computational software performs music the performances can be criticised for being too unnatural, lacking interpretation and, in short, being too mechanical. However much progress has been made within the field of expressive musical performance and musical interpretation expression. Alongside these advances have been interesting findings in musical expectation (i.e. what people expect to hear when listening to a piece of music), as well as work on emotions that are present within music and on how information and meaning are conveyed in music. Each of these advances raises questions of how the relevant aspects could be interpreted by a musical performer. Potential application areas for computer systems that can perform music in an appropriately expressive manner include, for example, improving playback in music notation editors (like Sibelius), or the automated performance of music generated on-the-fly for ‘hold’ music (played when waiting on hold during phone calls). Practical work exploring this could involve writing software that performs existing pieces, or could be to write software that can improvise, interpreting incoming sound/music and generating an appropriate sonic/musical response to it in real time.

Brain-like Computer  

Contact:  frank wang.

The human brain consists of about one billion neurons. Each neuron forms about 1,000 connections to other neurons, amounting to more than a trillion connections. If each neuron could only help store a single memory, running out of space would be a problem. You might have only a few gigabytes of storage space, similar to the space in an iPod or a USB flash drive. Yet neurons combine so that each one helps with many memories at a time, exponentially increasing the brain’s memory storage capacity to something closer to around 2.5 petabytes (or a million gigabytes). The way our brain organizes data may help us manage continuously increasing data, especially in Cloud computing and Big Data. In this project, you are expected to simulate a brain-like computer. Such a computer should be categorised into the unconventional computer group, which is different from traditional Turing machine (with stored programmes) or Von Neumann computer (with an operating system).

My relevant papers: Adaptive Neuromorphic Architecture , Memristor Neural Networks , Grid-Oriented Storage (IEEE Transactions on Computers) .

My relevant keynote talk at Cambridge: Brain and Brain-Inspired Artificial Intelligence )

New Quantum Computer

Contact: frank wang.

Most recently, Frank Wang published an article on Quantum Information Processing (Springer Nature) to report a new quantum computer that can break Landauer’s Bound. Among a number of physical limits to computation, Landauer’s bound limits the minimum amount of energy for a computer to process a bit of information. In light of this study, we may have to presume the demise of this bound despite the many mysteries uncovered with it over the past 60 years.

My relevant papers: Breaking Landauer’s bound in a spin-encoded quantum computer (Springer Nature) , Can We Break the Landauer Bound in Spin-Based Electronics (IEEE Access) .

My relevant keynote talk at Cambridge: A New Quantum Computer Not Bound By Landauer’s Bound )

PhD Position - Artificial Intelligence: Power Asymmetries and Data Justice

Job Information

Offer description.

Are you looking for a challenging position in a dynamic setting? The Amsterdam School for Cultural Analysis (ASCA) currently has a vacant PhD position as part of the Artificial Intelligence: Power Asymmetries and Data Justice project led by principal investigators Dr Lonneke van der Velden and Dr Claudio Celis Bueno. ASCA is one of the five Research Schools within the Amsterdam Institute for Humanities Research (AIHR). ASCA is a research community devoted to the comparative and interdisciplinary study of culture (in all its forms and expressions) from a broad humanities perspective. ASCA is home to more than 120 scholars and 160 PhD candidates and is a world-leading international research school in Cultural Analysis. ASCA members share a commitment to working in an interdisciplinary framework and to maintaining a close connection with contemporary cultural and political debates. Candidates can work on a research of their own choosing within the broader scope of the project (critical AI studies, AI and power asymmetries, and data justice). Candidates are encouraged to think creatively in terms of research design. We welcome projects that are original, conceptually rigorous, and critical. We invite interested candidates to prepare short proposals (details below) that could, for instance, examine:

• The (invisible) labour behind artificial intelligence;
• Bias and discrimination in AI applications;
• AI and Epistemic Injustices;
• AI and educational equity;
• Environmental cost and extractivist logics of AI;
• AI, automation, and the transformation of labour;
• Feminist and/or decolonial approaches to AI.

What are you going to do? Your research will be part of the Artificial Intelligence: Power Asymmetries and Data Justice project, which explores AI technologies from a critical perspective. You will be working under the supervision of Prof Dr Stefania Milan, Dr Lonneke van der Velden, and Dr Claudio Celis Bueno. Special focus on asymmetric power relations and/or data justice is encouraged. Once appointed for the position, your final PhD proposal will be developed in a productive dialogue with the ongoing research of Dr Lonneke van der Velden and Dr Claudio Celis Bueno. Tasks and responsibilities:

• submission of a PhD thesis within the period of appointment;
• participating in meetings of the project research group;
• publishing of single-authored and/or co-authored outputs derived from your doctoral research, including peer-reviewed academic articles;
• presenting intermediate research results at workshops and conferences;
• organising knowledge dissemination activities within the research group and the research school;
• (co-)teaching courses at BA-level in the second and third year of the appointment (maximum 0,2 fte per year);
• Participating in the Research School and Faculty of Humanities PhD training programmes.

What do you have to offer? We are looking for candidates who are interested in the intersection between technology and politics, power relations and social justice, and capable of developing a critical, conceptually rigorous, interdisciplinary, and situated analysis of some form of AI application. The ideal candidate is able to formulate an original research question and contribute to ongoing debates around the social and political effects of AI. Your experience and profile: Candidates need to have the following qualifications:

• a completed Master's degree in Media Studies, Communication Science, Science and Technology Studies, Political Science, Philosophy of Technology, or related discipline. You may apply if you have not yet completed your Master's degree only if you provide a signed letter from your supervisor stating that you will graduate before 31 December 2024;
• excellent and independent research skills demonstrated by an outstanding Master's thesis and a demonstrable capacity to develop an outstanding publication record;
• a strong cooperative attitude and willingness to engage in collaborative research;
• enthusiasm for communicating academic research to non-academic audiences;
• excellent command of the English language.

Critical technical literacy and societal engagement will be considered of added value. Please note that if you already hold a doctorate/PhD or are working towards obtaining a similar degree elsewhere, you will not be admitted to a doctoral programme at the UvA. What can we offer you? We offer a temporary employment contract for the period of 48 months. The first contract will be for 16 months, with an extension for the following 32 months, contingent on a positive performance evaluation within the first 12 months. The employment contract is for 38 hours a week. The preferred starting date is 01 February 2025. Your salary, depending on relevant experience on commencement of the employment contract, ranges from € 2,770 up to a maximum of € 3,539 gross per month on the basis of a full working week of 38 hours. This sum does not include the 8% holiday allowance and the 8,3% year-end allowance. Favourable tax agreements may apply to applicants moving from abroad. The Collective Labour Agreement of Dutch Universities is applicable. What else do we offer?

• PhD candidates receive a tuition fee waiver;
• PhD candidates have free access to courses offered by the Graduate School of Humanities and the Dutch National Research Schools ;
• excellent possibilities for further professional development and education;
• an inspiring academic and international work environment in the heart of Amsterdam;
• an enthusiastic and professional academic team.

About us The University of Amsterdam is the largest university in the Netherlands, with the broadest spectrum of degree programmes. It is an intellectual hub with 42,000 students, 6,000 employees and 3,000 doctoral students who are all committed to a culture of inquiring minds. The Faculty of Humanities provides education and conducts research with a strong international profile in a large number of disciplines in de field of language and culture. Located in the heart of Amsterdam, the faculty maintains close ties with many cultural institutes in the capital city. Research and teaching staff focus on interdisciplinary collaboration and are active in several teaching programmes. Want to know more about our organisation? Read more about working at the University of Amsterdam. Questions? If you have any questions about the position or the department, please contact during office hours: For practical questions about the position and the application process, please contact Dr Eloe Kingma ( [email protected] ). For questions regarding the research proposal, you may contact Dr Lonneke van der Velden ( [email protected] ) or Dr Claudio Celis Bueno ( [email protected] ). Job application If you feel the profile fits you, and you are interested in the job, we look forward to receiving your application. You can apply online via the link below. We will accept applications until 08 September 2024. Your application should include the following information:

• a motivation letter and full academic CV;
• a research proposal of 800-1000 words (excluding references) that describes your topic and the main conceptual lens that would inform your project;
• a writing sample, for example, a chapter from your Master thesis or an article;
• a list of all Master-level modules you have taken, with an official transcript of grades;
• the names, phone numbers, and email addresses of two academic references, which includes preferably your supervisor, who may be approached by the selection committee.

Please submit the required information in 1 pdf by uploading in the required field ‘CV’. The first round of interviews will be held in October 2024. The UvA is an equal-opportunity employer. We prioritise diversity and are committed to creating an inclusive environment for everyone. We value a spirit of enquiry and perseverance, provide the space to keep asking questions, and promote a culture of curiosity and creativity. No agencies please.

Requirements

Additional information, work location(s), where to apply.

Meike Nauta wins Overijssel PhD Award 2023

The 2023 Overijssel PhD Award goes to Meike Nauta who researched explainable artificial intelligence for her PhD. The award will be presented during the Dies Natalis, the 62nd anniversary of the University of Twente.

Artificial intelligence has become an integral part of our society, but too often computer models still work like a so-called black box. Information goes in and information comes out, but what exactly happens in between - inside the model - is unclear. That is why Nauta conducted research into methods for artificial intelligence to explain decisions itself.

"For many applications, knowing whether the model uses the right reasoning to arrive at a particular prediction is important. With explainable AI, questions like 'What did the model learn?' and 'How does the model arrive at such a prediction?' can be answered," says Nauta. Nauta developed a new model called ProtoTree. ProtoTree is a computer model that recognises images of two hundred bird species.

What is new about the model is that it simultaneously explains how it came to a decision. The model looks at a picture as if playing a kind of "Guess Who?". It looks for matching physical characteristics of a bird species - for example, in the presence of a red breast, a black wing and a black stripe near the eye, it recognises a Vermilion flycatcher. This way, a human can also track how the model arrives at a particular prediction.

Her dissertation was full of good examples of explainable artificial intelligence and its necessity. Nauta summarised it during her defence: "Power to the people without losing the power of AI." Nauta made a promising start to her career with her PhD. She currently works for Datacation. During her PhD trajectory, she already won several awards and has been in the media several times.

Overijssel PhD award

The Overijssel PhD award is an annual award given to a thesis of exceptional academic quality. Each UT faculty and research institute can nominate a thesis. The prize is co-sponsored by the Province of Overijssel and confers a cash prize of €5000 and a certificate.

More recent news

Suggestions or feedback?

MIT News | Massachusetts Institute of Technology

• Machine learning
• Social justice
• Black holes
• Classes and programs

Departments

• Aeronautics and Astronautics
• Brain and Cognitive Sciences
• Architecture
• Political Science
• Mechanical Engineering

Centers, Labs, & Programs

• Abdul Latif Jameel Poverty Action Lab (J-PAL)
• Picower Institute for Learning and Memory
• Lincoln Laboratory
• School of Architecture + Planning
• School of Engineering
• School of Humanities, Arts, and Social Sciences
• Sloan School of Management
• School of Science
• MIT Schwarzman College of Computing

From steel engineering to ovarian tumor research

Press contact :.

Previous image Next image

Ashutosh Kumar is a classically trained materials engineer. Having grown up with a passion for making things, he has explored steel design and studied stress fractures in alloys.

Throughout Kumar’s education, however, he was also drawn to biology and medicine. When he was accepted into an undergraduate metallurgical engineering and materials science program at Indian Institute of Technology (IIT) Bombay, the native of Jamshedpur was very excited — and “a little dissatisfied, since I couldn’t do biology anymore.”

Now a PhD candidate and a MathWorks Fellow in MIT’s Department of Materials Science and Engineering, and a researcher for the Koch Institute, Kumar can merge his wide-ranging interests. He studies the effect of certain bacteria that have been observed encouraging the spread of ovarian cancer and possibly reducing the effectiveness of chemotherapy and immunotherapy.

“Some microbes have an affinity toward infecting ovarian cancer cells, which can lead to changes in the cellular structure and reprogramming cells to survive in stressful conditions,” Kumar says. “This means that cells can migrate to different sites and may have a mechanism to develop chemoresistance. This opens an avenue to develop therapies to see if we can start to undo some of these changes.”

Kumar’s research combines microbiology, bioengineering, artificial intelligence, big data, and materials science. Using microbiome sequencing and AI, he aims to define microbiome changes that may correlate with poor patient outcomes. Ultimately, his goal is to engineer bacteriophage viruses to reprogram bacteria to work therapeutically.

Kumar started inching toward work in the health sciences just months into earning his bachelor's degree at IIT Bombay.

“I realized engineering is so flexible that its applications extend to any field,” he says, adding that he started working with biomaterials “to respect both my degree program and my interests."

“I loved it so much that I decided to go to graduate school,” he adds.

Starting his PhD program at MIT, he says, “was a fantastic opportunity to switch gears and work on more interdisciplinary or ‘MIT-type’ work.”

Kumar says he and Angela Belcher, the James Mason Crafts Professor of biological engineering, materials science and of the Koch Institute of Integrative Cancer Research, began discussing the impact of the microbiome on ovarian cancer when he first arrived at MIT.

“I shared my enthusiasm about human health and biology, and we started brainstorming,” he says. “We realized that there’s an unmet need to understand a lot of gynecological cancers. Ovarian cancer is an aggressive cancer, which is usually diagnosed when it’s too late and has already spread.”

In 2022, Kumar was awarded a MathWorks Fellowship. The fellowships are awarded to School of Engineering graduate students, preferably those who use MATLAB or Simulink — which were developed by the mathematical computer software company MathWorks — in their research. The philanthropic support fueled Kumar’s full transition into health science research.

“The work we are doing now was initially not funded by traditional sources, and the MathWorks Fellowship gave us the flexibility to pursue this field,” Kumar says. “It provided me with opportunities to learn new skills and ask questions about this topic. MathWorks gave me a chance to explore my interests and helped me navigate from being a steel engineer to a cancer scientist.”

Kumar’s work on the relationship between bacteria and ovarian cancer started with studying which bacteria are incorporated into tumors in mouse models.

“We started looking closely at changes in cell structure and how those changes impact cancer progression,” he says, adding that MATLAB image processing helps him and his collaborators track tumor metastasis.

The research team also uses RNA sequencing and MATLAB algorithms to construct a taxonomy of the bacteria.

“Once we have identified the microbiome composition,” Kumar says, “we want to see how the microbiome changes as cancer progresses and identify changes in, let’s say, patients who develop chemoresistance.”

He says recent findings that ovarian cancer may originate in the fallopian tubes are promising because detecting cancer-related biomarkers or lesions before cancer spreads to the ovaries could lead to better prognoses.

As he pursues his research, Kumar says he is extremely thankful to Belcher “for believing in me to work on this project.

“She trusted me and my passion for making an impact on human health — even though I come from a materials engineering background — and supported me throughout. It was her passion to take on new challenges that made it possible for me to work on this idea. She has been an amazing mentor and motivated me to continue moving forward.”

For her part, Belcher is equally enthralled.

“It has been amazing to work with Ashutosh on this ovarian cancer microbiome project," she says. "He has been so passionate and dedicated to looking for less-conventional approaches to solve this debilitating disease. His innovations around looking for very early changes in the microenvironment of this disease could be critical in interception and prevention of ovarian cancer. We started this project with very little preliminary data, so his MathWorks fellowship was critical in the initiation of the project.”

Kumar, who has been very active in student government and community-building activities, believes it is very important for students to feel included and at home at their institutions so they can develop in ways outside of academics. He says that his own involvement helps him take time off from work.

“Science can never stop, and there will always be something to do,” he says, explaining that he deliberately schedules time off and that social engagement helps him to experience downtime. “Engaging with community members through events on campus or at the dorm helps set a mental boundary with work.”

Regarding his unusual route through materials science to cancer research, Kumar regards it as something that occurred organically.

“I have observed that life is very dynamic,” he says. “What we think we might do versus what we end up doing is never consistent. Five years back, I had no idea I would be at MIT working with such excellent scientific mentors around me.”

Share this news article on:

Related links.

• Ashutosh Kumar
• Department of Materials Science and Engineering
• MathWorks Fellowship

Related Topics

• Graduate, postdoctoral
• Health sciences and technology
• Artificial intelligence
• Bioengineering and biotechnology
• Materials science and engineering

Related Articles

A biomedical engineer pivots from human movement to women’s health

Computational imaging researcher attended a lecture, found her career

A. Michael West: Advancing human-robot interactions in health care

Previous item Next item

More MIT News

Understanding why autism symptoms sometimes improve amid fever

Read full story →

School of Engineering welcomes new faculty

Study explains why the brain can robustly recognize images, even without color

Turning up the heat on next-generation semiconductors

Sarah Millholland receives 2024 Vera Rubin Early Career Award

A community collaboration for progress

• More news on MIT News homepage →

Massachusetts Institute of Technology 77 Massachusetts Avenue, Cambridge, MA, USA

• Map (opens in new window)
• Events (opens in new window)
• People (opens in new window)
• Careers (opens in new window)
• Accessibility
• Social Media Hub
• MIT on Facebook
• MIT on YouTube
• MIT on Instagram