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Fellowship & Award Opportunities

There are a variety of internal and external fellowship and award opportunities for graduate students. Note:

• Always check the official websites for the most accurate deadlines, eligibility and benefits , as they tend to change each year (sometimes can shift by months) and likely do not fully synchronize with information shown on this page. The deadlines show below are the  typical  deadlines from previous years.
• Some opportunities require coordination within Georgia Tech (e.g., 2 total nominees from GT). We highlight such opportunities' deadlines in  yellow ; internal deadlines are typically one month earlier (e.g., suppose a fellowship's official deadline is 9/30 , assume 8/30 as its GT-internal deadline).
• This fellowship listing is centered on Computing students. Georgia Tech's Fellowships Office is available for ALL students.  

For questions, updates and corrections, email us .

We thank Carnegie Mellon University's Computer Science Department for their list of fellowships  that we have adapted, and the many Georiga Tech staff and faculty members who have contributed to the curation of this list.

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Georgia Institute of Technology North Avenue, Atlanta, GA 30332 Phone: 404-894-2000

Apple Scholars in AI/ML PhD Fellowship Program

The Apple Scholars in AI/ML PhD fellowship program recognizes the contributions of emerging leaders in computer science and engineering at the graduate and postgraduate level. The PhD fellowship in AI/ML was created as part of the Apple Scholars program to support the work of outstanding PhD students from around the world, who are pursuing cutting edge research in machine learning and artificial intelligence. 

Duke may nominate up to (3) students. Universities may nominate a maximum of (1) student to any of the research areas (see below).

Guidelines will be released in June. The password to access the guidelines will be available by request. Duke students interested in this opportunity can contact  [email protected] .

• Duke Internal Deadline: July 8, 2024
• Deadline for Nominations: Sep. 10, 2024

Each university may nominate up to (3) students. Universities may nominate a maximum of (1) student to any of the following research areas: 

• Privacy Preserving Machine Learning
• Human Centered AI
• AI for Ethics and Fairness
• AI for Accessibility
• AI for Health and Wellness
• ML Algorithms and Architectures
• Embodied ML
• Speech and Natural Language
• Computer Vision
• Information Retrieval and Knowledge
• Data-Centric AI

Nominees must meet the following criteria to be considered:

• Nominee must be enrolled full time at the nominating university at the start of Fall 2025, and expect to be enrolled through the end of the 2025/2026 academic year

• Nominee should be entering their last 2-3 years of study as of Fall 2025

• Nominee must not hold another industry-sponsored full fellowship while they are an Apple Scholar in AI/ML (Fall 2025 to Summer 2027)

Owing to the sponsor's restriction on the number of applications that may be submitted from Duke, anyone wishing to pursue nomination should submit the following materials as one PDF: 

• CV and publication list

• A one-page abstract describing your innovative research, your record as thought leaders and collaborators in your fields, and your unique commitment to take risks and push the envelope in machine learning and AI.

Please submit internal materials through My Research Proposal. (Code: ILN) https://www.grantinterface.com/sl/HQShLh

Instructions for creating an account (if needed) and submitting your materials:  https://ctsi.duke.edu/about-myresearchproposal

Machine Learning - CMU

Phd program in machine learning.

Carnegie Mellon University's doctoral program in Machine Learning is designed to train students to become tomorrow's leaders through a combination of interdisciplinary coursework, hands-on applications, and cutting-edge research. Graduates of the Ph.D. program in Machine Learning will be uniquely positioned to pioneer new developments in the field, and to be leaders in both industry and academia.

Understanding the most effective ways of using the vast amounts of data that are now being stored is a significant challenge to society, and therefore to science and technology, as it seeks to obtain a return on the huge investment that is being made in computerization and data collection. Advances in the development of automated techniques for data analysis and decision making requires interdisciplinary work in areas such as machine learning algorithms and foundations, statistics, complexity theory, optimization, data mining, etc.

The Ph.D. Program in Machine Learning is for students who are interested in research in Machine Learning.  For questions and concerns, please   contact us .

The PhD program is a full-time in-person committment and is not offered on-line or part-time.

PhD Requirements

Requirements for the phd in machine learning.

• Completion of required courses , (6 Core Courses + 1 Elective)
• Mastery of proficiencies in Teaching and Presentation skills.
• Successful defense of a Ph.D. thesis.

Teaching Ph.D. students are required to serve as Teaching Assistants for two semesters in Machine Learning courses (10-xxx), beginning in their second year. This fulfills their Teaching Skills requirement.

Conference Presentation Skills During their second or third year, Ph.D. students must give a talk at least 30 minutes long, and invite members of the Speaking Skills committee to attend and evaluate it.

Research It is expected that all Ph.D. students engage in active research from their first semester. Moreover, advisor selection occurs in the first month of entering the Ph.D. program, with the option to change at a later time. Roughly half of a student's time should be allocated to research and lab work, and half to courses until these are completed.

Master of Science in Machine Learning Research - along the way to your PhD Degree.

Other Requirements In addition, students must follow all university policies and procedures .

Rules for the MLD PhD Thesis Committee (applicable to all ML PhDs): The committee should be assembled by the student and their advisor, and approved by the PhD Program Director(s).  It must include:

• At least one MLD Core Faculty member
• At least one additional MLD Core or Affiliated Faculty member
• At least one External Member, usually meaning external to CMU
• A total of at least four members, including the advisor who is the committee chair

Financial Support

Application Information

For applicants applying in Fall 2023 for a start date of August 2024 in the Machine Learning PhD program, GRE Scores are REQUIRED. The committee uses GRE scores to gauge quantitative skills, and to a lesser extent, also verbal skills.

Proof of English Language Proficiency If you will be studying on an F-1 or J-1 visa, and English is not a native language for you (native language…meaning spoken at home and from birth), we are required to formally evaluate your English proficiency. We require applicants who will be studying on an F-1 or J-1 visa, and for whom English is not a native language, to demonstrate English proficiency via one of these standardized tests: TOEFL (preferred), IELTS, or Duolingo.  We discourage the use of the "TOEFL ITP Plus for China," since speaking is not scored. We do not issue waivers for non-native speakers of English.   In particular, we do not issue waivers based on previous study at a U.S. high school, college, or university.  We also do not issue waivers based on previous study at an English-language high school, college, or university outside of the United States.  No amount of educational experience in English, regardless of which country it occurred in, will result in a test waiver.

Submit valid, recent scores:   If as described above you are required to submit proof of English proficiency, your TOEFL, IELTS or Duolingo test scores will be considered valid as follows: If you have not received a bachelor’s degree in the U.S., you will need to submit an English proficiency score no older than two years. (scores from exams taken before Sept. 1, 2021, will not be accepted.) If you are currently working on or have received a bachelor's and/or a master's degree in the U.S., you may submit an expired test score up to five years old. (scores from exams taken before Sept. 1, 2018, will not be accepted.)

Graduate Online Application

• Early Application Deadline – November 29, 2023 (3:00 p.m. EST)
• Final Application Deadline - December 13, 2023 (3:00 p.m. EST)

Computer Science & Engineering

DeepMind Fellowships

Computer science and engineering is proud to offer two deepmind fellowships for fall 2023  (two masters fellowships and one phd fellowship to be awarded for ay23-24).

These fellowships were made possible by a recent generous gift from DeepMind , a London-based leader in artificial intelligence (AI) research and how it’s applied in the real world. UC San Diego is currently one of just three universities in the United States, and one of five in North America, selected to participate in this program.

Masters Fellowship

The Master's Fellowships are designed to support machine learning (ML) masters students. Students from cultural, racial, linguistic, geographic and socioeconomic backgrounds and genders who are underrepresented in ML graduate education are encouraged to apply. DeepMind Fellows receive two-year fellowships that cover tuition along with a stipend, a travel grant, funding to use towards housing, and access to a DeepMind mentor.  

See current DeepMind Fellows here .

Who’s eligible: Admitted MS students specializing in Machine Learning who have demonstrated meaningful contributions to diversity and inclusion will be considered for DeepMind Fellowships.

How to apply: To be considered for a DeepMind Fellowship, applicants must (1) submit an MS application by the application deadline, (2) Complete the “ Additional Educational Experiences ” section of the application  (highlighting how the applicant overcame barriers, and/or demonstrating a commitment to diversity) , and (3) Select AI/Machine Learning as an Interest Area. 

The Fall 2023 MS Application Deadline is December 21, 2022. Fellowship awardees will be selected and contacted by March or early April.

DeepMind Fellowship Details (Masters):

• The award provides full tuition and fees (including nonresident supplemental tuition, if applicable) and funding to use towards housing for five quarters.  
• Additional funding for living expenses, required books, equipment, and transportation costs recommended by the university
• A one-time travel grant of $2,250 to be used for relevant conference attendance

Contact [email protected]

PhD Fellowship

The Fall 2023 PhD Application Deadline is April 30, 2023. The fellowship awardee will be selected and contacted in Spring 2023.

This one-year fellowship is designed to support a machine learning (ML) PhD student. Students from cultural, racial, linguistic, geographic and socioeconomic backgrounds and genders who are underrepresented in ML graduate education are encouraged to apply.  The DeepMind PhD Fellow will receive a one-year fellowships that covers partial tuition along with a travel grant and funding to use towards housing.  

Who’s eligible:  Admitted PhD students specializing in Machine Learning who have demonstrated meaningful contributions to diversity and inclusion will be considered.

How to apply:  Please have your advisor submit your application packet here:  https://forms.gle/wp6d99UJUnNnTtFh8 no later than April 30, 2023 . The application packet should include the following:

• A copy of your CV
• A 2-page statement of research that also addresses any ways your candidacy fulfills the EDI preference for this fellowship
• Two professional letters of recommendation

DeepMind Fellowship Details (PhD):

• The one-year fellowship provides funding towards tuition, fees and equipment, and funding to use towards housing.

Contact [email protected]   or [email protected]

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The Apple Scholars in AI/ML PhD fellowship program  recognizes the contributions of emerging leaders in computer science and engineering at the graduate and postgraduate level. The PhD fellowship in AI/ML was created as part of the Apple Scholars program to support the work of outstanding PhD students from around the world, who are pursuing cutting edge research in machine learning and artificial intelligence. 

Duke may nominate up to (3) students. Universities may nominate a maximum of (1) student to any of the research areas (see below).

Guidelines will be released in June. The password to access the guidelines will be available by request. Duke students interested in this opportunity can contact  [email protected] .

• Duke Internal Deadline:  July 8, 2024
• Deadline for Nominations:  Sep. 10, 2024

Areas of Interest

Each university may nominate up to (3) students. Universities may nominate a maximum of (1) student to any of the following research areas: 

• Privacy Preserving Machine Learning
• Human Centered AI
• AI for Ethics and Fairness
• AI for Accessibility
• AI for Health and Wellness
• ML Algorithms and Architectures
• Embodied ML
• Speech and Natural Language
• Computer Vision
• Information Retrieval and Knowledge
• Data-Centric AI

Eligibility Requirements

Nominees must meet the following criteria to be considered:

• Nominee must be enrolled full time at the nominating university at the start of Fall 2025, and expect to be enrolled through the end of the 2025/2026 academic year

• Nominee should be entering their last 2-3 years of study as of Fall 2025

• Nominee must not hold another industry-sponsored full fellowship while they are an Apple Scholar in AI/ML (Fall 2025 to Summer 2027)

Internal Nomination

Owing to the sponsor’s restriction on the number of applications that may be submitted from Duke, anyone wishing to pursue nomination should submit the following materials as one PDF: 

• CV and publication list

• A one-page abstract describing your innovative research, your record as thought leaders and collaborators in your fields, and your unique commitment to take risks and push the envelope in machine learning and AI.

Please submit internal materials through My Research Proposal. (Code: ILN)  https://www.grantinterface.com/sl/HQShLh

Instructions for creating an account (if needed) and submitting your materials:  https://ctsi.duke.edu/about-myresearchproposal

Google PhD Fellowship recipients

Previous years:, algorithms, optimizations and markets.

Brice Huang, Massachusetts Institute of Technology

Debajyoti Kar, Indian Institute of Science

Jamie Tucker-Foltz, Harvard University

Joakim Blikstad, KTH Royal Institute of Technology

Mahdieh Labani, Macquarie University

Rehema Hamis Mwawado, University of Rwanda

Uddalok Sarkar, Indian Statistical Institute Kolkata

Computational Neural and Cognitive Sciences

Gizem Özdil, École polytechnique fédérale de Lausanne

Sreejan Kumar, Princeton University

Bridget Chak, University of Chicago

Li-Wen Chiu, National Yang Ming Chiao Tung University

Md. Saiful Islam, University of Rochester

Rutendo Jakachira, Brown University

Tsai-Min Chen, National Taiwan University

Wenhao Gao, Massachusetts Institute of Technology

Human Computer Interaction

Brianna Wimer, University of Notre Dame

Emily Kuang, Rochester Institute of Technology

Eunkyung Jo, University of California - Irvine

Georgianna Lin, University of Toronto

Gustavo Pacheco Santiago, Universidad Nacional Autónoma de México

Marcelo Marques da Rocha, Universidade Federal Fluminense

Yulia Goldenberg, Ben Gurion University

Zixiong Su, The University of Tokyo

Machine Learning

Berivan Isik, Stanford University

Blake Bordelon, Harvard University

Cristhian Delgado Fajardo, University of Otago

Denish Azamuke, Makerere University

Fuzhao Xue, National University of Singapore

Heinrich Pieter van Deventer, University of Pretoria

Imane Araf, Mohammed VI Polytechnic University

Itamar Franco Salazar Reque, Pontificia Universidad Católica del Perú

Jihoon Tack, Korea Advanced Institute of Science and Technology

Julliet Chepngeno Kirui, Strathmore University

Krystal Dacey, Charles Sturt University

Laura Smith, University of California - Berkeley

Marcos Paulo Silva Gôlo, ​​Universidade de São Paulo

Melisa Yael Vinograd, Universidad de Buenos Aires

Miriam Rateike, Saarland University

Mitchell Wortsman, University of Washington

Natalia Gil Canto, Universidade Estadual de Campinas

Nicolás Esteban Valenzuela Figueroa, Universidad de Chile

Omprakash Chakraborty, Indian Institute of Technology Kharagpur

S. Durga, Indian Institute of Technology Bombay

Strato Angsoteng Bayitaa, C.K. Tedam University of Technology and Applied Sciences

Yiding Jiang, Carnegie Mellon University

Yifan Zhang, National University of Singapore

Machine Perception, Speech Technology and Computer Vision

Antoine Yang, National Institute for Research in Digital Science and Technology (Inria)

Astitva Srivastava, International Institute of Information Technology Hyderabad

Chen Yu, National University of Singapore

Ethan Tseng, Princeton University

Matheus Viana da Silva, Universidade Federal de São Carlos

Sunghwan Hong, Korea University

Sungyeon Kim, Pohang University of Science and Technology

Vincent Milimo Masilokwa Punabantu, University of Cape Town

Yanxi Li, The University of Sydney

Yosef Gandelsman, University of California - Berkeley

Ziqi Huang, Nanyang Technological University

Mobile Computing

Ke Sun, University of California - San Diego

Kyungjin Lee, Seoul National University

Natural Language Processing

Allahsera Auguste Tapo, Rochester Institute of Technology

Cheng-Han Chiang, National Taiwan University

Liunian Li, University of California - Los Angeles

Sarah Masud, Indraprastha Institute of Information Technology Delhi

Sumanth Doddapaneni, Indian Institute of Technology Madras

Zhiqing Sun, Carnegie Mellon University

Privacy and Security

Jiayuan Ye, National University of Singapore

Miranda Wei, University of Washington

Neha Jawalkar, Indian Institute of Science

Yihui Zeng, Arizona State University

Programming Technology and Software Engineering

Aaditya Naik, University of Pennsylvania

Thanh Le-Cong, The University of Melbourne

Quantum Computing

Diego Hernando Useche Reyes, Universidad Nacional de Colombia

Elies Gil-Fuster, Free University of Berlin

Juan David Nieto García, Universidade Estadual de Campinas

Lia Yeh, University of Oxford

Structured Data and Database Management

Zezhou Huang, Columbia University

Systems and Networking

Jennifer Switzer, University of California - San Diego

Jiaxin Lin, University of Texas at Austin

Jinhyung Koo, Daegu Gyeongbuk Institute of Science & Technology

Maurine Chepkoech, University of Cape Town

Qinghao Hu, Nanyang Technological University

Anjali Gupta, Indian Institute of Technology Delhi

Shunhua Jiang, Columbia University

Shyam Sivasathya Narayanan, Massachusetts Institute of Technology

Venkata Naga Sreenivasulu Karnati, Indian Institute of Science

Yang P. Liu, Stanford University

Aditi Jha, Princeton University

Klavdia Zemlianova, New York University

Devon Jarvis, University of the Witwatersrand

Emily Schwenger, Albert Einstein College of Medicine

Reihaneh Torkzadehmahani, TU Munich

Xin Liu, University of Washington

Qian Niu, Kyoto University

Karthik Mahadevan, University of Toronto

Meena Muralikumar, University of Washington

Nika Nour, University of California - Irvine

Pang Suwanaposee, University of Canterbury

Ryan Louie, Northwestern University

Tiffany Li, University of Illinois - Urbana-Champaign

Zhongyi Zhou, The University of Tokyo

Eunji Kim, Seoul National University

Hayeon Lee, Korea Advanced Institute of Science and Technology

Julius von Kügelgen, Max Planck Institute for Intelligent Systems

Kaloma Usman Majikumna, Euromed University of Fes, Morocco

Lily Xu, Harvard University

Maksym Andriushchenko, EPFL

Pierre Marion, Fondation Sciences Mathématiques de Paris

Shashank Rajput, University of Wisconsin - Madison

Sheheryar Zaidi, University of Oxford

Sindy Löwe, University of Amsterdam

Tan Wang, Nanyang Technological University

Xiaobo Xia, University of Sydney

Yixin Liu, Monash University

Efthymios Tzinis, University of Illinois - Urbana-Champaign

Elizabeth Ndunge Mutua, Strathmore University

Haipeng Xiong, National University of Singapore

Jianyuan Guo, University of Sydney

Jiawei Ren, Nanyang Technological University

Juhong Min, Pohang University of Science and Technology

Liliane Momeni, University of Oxford

Qianqian Wang, Cornell University

Shuo Yang, University of Technology Sydney

Tahir Javed, Indian Institute of Technology Madras

Wei-Ting Chen, National Taiwan University

Yuming Jiang. Nanyang Technological University

Yu-Ying Yeh, University of California - San Diego

Binbin Xie, University of Massachusetts - Amherst

Clara Isabel Meister, ETH Zurich

Julia Mendelsohn, University of Michigan

Sachin Kumar, Carnegie Mellon University

Saley Vishal Vivek, Indian Institute of Technology Delhi

Swarnadeep Saha, University of North Carolina - Chapel Hill

Shuyi Wang, The University of Queensland

Thong Nguyen, National University of Singapore

Ussen Kimanuka, Pan African University Institute For Basic Sciences, Technology and Innovation

Amy Elizabeth Gooden, University Kwazulu-Natal

Promise Ricardo Agbedanu, University of Rwanda

Alexander Bienstock, New York University

Daniel De Almeida Braga, Universite Rennes 1

Gaurang Bansal, National University of Singapore

Nicolas Huaman Groschopf, Leibniz University of Hanover

Simon Spies, Max Planck Institute for Software Systems

Ilkwon Byun, Seoul National University

Margaret Fortman, University of Wisconsin - Madison

Oscar Higgott, University College London

Sam Gunn, University of California - Berkeley

Recommender Systems

Jessie J. Smith, University of Colorado - Boulder

Wenjie Wang, National University of Singapore

Nikolaos Tziavelis, Northeastern University

Humphrey Owuor Otieno, University of Cape Town

Jiarong Xing, Rice University

Shweta Pandey, Indian Institute of Science

Sunil Kumar, Indraprastha Institute of Information Technology Delhi

Yang Zhou, Harvard University

Yujeong Choi, Korea Advanced Institute of Science and Technology

Daniel Mutembesa, Makerere University

Kevin Tian, Stanford University

Prerona Chatterjee, Tata Institute of Fundamental Research

Sampson Wong, The University of Sydney

Santhoshini Velusamy, Harvard University

Sruthi Gorantla, Indian Institute of Science

Wenshuo Guo, University of California, Berkeley

Malvern Madondo, Emory University

Steffen Schneider, University of Tübingen

Nalini Singh, Massachusetts Institute of Technology

Roman Koshkin, Okinawa Institute of Science and Technology

Vishwali Mhasawade, New York University

Anupriya Tuli, Indraprastha Institute of Information Technology - Delhi

Chia-Hsing Chiu, National Taiwan University of Science and Technology

Dennis Makafui Dogbey, University of Cape Town

George Hope Chidziwisano, Michigan State University

Harmanpreet Kaur, University of Michigan

Srishti Palani, University of California, San Diego

Amir-Hossein Karimi, Max Planck Institute for Intelligent Systems

Anastasia Koloskova, EPFL, Lausanne

Anirudh Goyal, University of Montreal

Daniel Kang, Stanford University

Elena Fillola, University of Bristol

Emmanuel Chinyere Echeonwu, Nnamdi Azikiwe University, Nigeria

Gal Yona, Weizmann Institute of Science

Hae Beom Lee, KAIST

Jaekyeom Kim, Seoul National University

Logan Engstrom, Massachusetts Institute of Technology

Piyushi Manupriya, Indian Institute of Technology - Hyderabad

Qinbin Li, National University of Singapore

Shen Li, National University of Singapore

Shubhada Agrawal, Tata Institute of Fundamental Research

Theekshana Dissanayake, Queensland University of Technology

Tianyuan Jin, National University of Singapore

Yun Li, The University of New South Wales

Andrea Burns, Boston University

Fangzhou Hong, Nanyang Technological University

Hai-Bin Wu, National Taiwan University

Jogendra Nath Kundu, Indian Institute of Science

Kelvin C.K. Chan, Nanyang Technological University

Sanghyun Woo, KAIST

Sara El-Ateif, National School For Computer Science (ENSIAS)

Soo Ye Kim, KAIST

Tewodros Amberbir Habtegebrial, Technical University of Kaiserslautern

Xinlong Wang, The University of Adelaide

Xueting Li, University of California, Merced

Zhiqin Chen, Simon Fraser University

Byungjin Jun, Northwestern University

Soundarya Ramesh, National University of Singapore

Derguene Mbaye, Universite Cheikh Anta Diop

Eya Hammami, LARODEC

Haoyue Shi, Toyota Technological Institute at Chicago

Kalpesh Krishna, University of Massachusetts Amherst

Peter Hase, University of North Carolina at Chapel Hill

Rochelle Choenni, University of Amsterdam

Chandan Kumar, Indian Institute of Technology - Kharagpur

Kevin Loughlin, University of Michigan

Teodora Baluta, National University of Singapore

Yuqing Zhu, University of California, Santa Barbara

Aishwarya Sivaraman, University of California, Los Angeles

Jenna Wise, Carnegie Mellon University

Alicja Dutkiewicz, Leiden University

Hsin-Yuan Huang, California Institute of Technology

Mykyta Onizhuk, The University of Chicago

Sayantan Chakraborty, Tata Institute of Fundamental Research

Brian Kundinger, Duke University

Yiru Chen, Columbia University

Yu Meng, University of Illinois at Urbana-Champaign

Zheng Wang, Nanyang Technological University

Aishwariya Chakraborty, Indian Institute of Technology - Kharagpur

Alireza Farshin, KTH Royal Institute of Technology

Erika Hunhoff, University of Colorado Boulder

S. VenkataKeerthy, Indian Institute of Technology - Hyderabad

Soroush Ghodrati, University of California, San Diego

Yejin Lee, Seoul National University

Jan van den Brand, KTH Royal Institute of Technology

Mahsa Derakhshan, University of Maryland, College Park

Sidhanth Mohanty, University of California, Berkeley

Computational Neuroscience

Connor Brennan, University of Pennsylvania

Abdelkareem Bedri, Carnegie Mellon University

Brendan David-John, University of Florida

Hiromu Yakura, University of Tsukuba

Manaswi Saha, University of Washington

Muratcan Cicek, University of California, Santa Cruz

Prashan Madumal, University of Melbourne

Alon Brutzkus, Tel Aviv University

Chin-Wei Huang, Universite de Montreal

Eli Sherman, Johns Hopkins University

Esther Rolf, University of California, Berkeley

Imke Mayer, Fondation Sciences Mathématique de Paris

Jean Michel Sarr, Cheikh Anta Diop University

Lei Bai, University of New South Wales

Nontawat Charoenphakdee, The University of Tokyo

Preetum Nakkiran, Harvard University

Sravanti Addepalli, Indian Institute of Science

Taesik Gong, Korea Advanced Institute of Science and Technology

Vihari Piratla, Indian Institute of Technology - Bombay

Vishakha Patil, Indian Institute of Science

Wilson Tsakane Mongwe, University of Johannesburg

Xinshi Chen, Georgia Institute of Technology

Yadan Luo, University of Queensland

Benjamin van Niekerk, University of Stellenbosch

Eric Heiden, University of Southern California

Gyeongsik Moon, Seoul National University

Hou-Ning Hu, National Tsing Hua University

Nan Wu, New York University

Shaoshuai Shi, The Chinese University of Hong Kong

Yifan Liu, University of Adelaide

Yu Wu, University of Technology Sydney

Zhengqi Li, Cornell University

Xiaofan Zhang, University of Illinois at Urbana-Champaign

Anjalie Field, Carnegie Mellon University

Mingda Chen, Toyota Technological Institute at Chicago

Shang-Yu Su, National Taiwan University

Yanai Elazar, Bar-Ilan

Julien Gamba, Universidad Carlos III de Madrid

Shuwen Deng, Yale University

Yunusa Simpa Abdulsalm, Mohammed VI Polytechnic University

Adriana Sejfia, University of Southern California

John Cyphert, University of Wisconsin-Madison

Amira Abbas, University of KwaZulu-Natal

Mozafari Ghoraba Fereshte, EPFL

Yanqing Peng, University of Utah

Huynh Nguyen Van, University of Technology Sydney

Michael Sammler, Saarland University, MPI-SWS

Sihang Liu, University of Virginia

Yun-Zhan Cai, National Cheng Kung University

Aidasadat Mousavifar, EPFL Ecole Polytechnique Fédérale de Lausanne

Peilin Zhong, Columbia University

Siddharth Bhandari, Tata Institute of Fundamental Research

Soheil Behnezhad, University of Maryland at College Park

Zhe Feng, Harvard University

Caroline Haimerl, New York University

Mai Gamal, German University in Cairo

Catalin Voss, Stanford university

Hua Hua, Australian National University

Zhanna Sarsenbayeva, University of Melbourne

Abdulsalam Ometere Latifat, African University of Science and Technology Abuja

Adji Bousso Dieng, Columbia University

Anshul Mittal, IIT Delhi

Blake Woodworth, Toyota Technological Institute at Chicago

Diana Cai, Princeton University

Francesco Locatello, ETH Zurich

Ihsane Gryech, International University Of Rabat, Morocco

Jaemin Yoo, Seoul National University

Maruan Al-Shedivat, Carnegie Mellon University

Ousseynou Mbaye, Alioune Diop University of Bambey

Rendani Mbuvha, University of Johannesburg

Shibani Santurkar, Massachusetts Institute of Technology

Takashi Ishida, University of Tokyo

Chenxi Liu, Johns Hopkins University

Kayode Kolawole Olaleye, Stellenbosch University

Ruohan Gao, The University of Texas at Austin

Tiancheng Sun, University of California San Diego

Xuanyi Dong, University of Technology Sydney

Yu Liu, Chinese University of Hong Kong

Zhi Tian, University of Adelaide

Naoki Kimura, University of Tokyo

Abigail See, Stanford University

Ananya Sai B, IIT Madras

Byeongchang Kim, Seoul National University

Daniel Patrick Fried, UC Berkeley

Hao Peng, University of Washington

Reinald Kim Amplayo, University of Edinburgh

Sungjoon Park, Korea Advanced Institute of Science and Technology

Ajith Suresh, Indian Institute of Science

Itsaka Rakotonirina, Inria Nancy

Milad Nasr, University of Massachusetts Amherst

Sarah Ann Scheffler, Boston University

Caroline Lemieux, UC Berkeley

Conrad Watt, University of Cambridge

Umang Mathur, University of Illinois at Urbana-Champaign

Amy Greene, Massachusetts Institute of Technology

Leonard Wossnig, University College London

Yuan Su, University of Maryland at College Park

Amir Gilad, Tel Aviv University

Nofar Carmeli, Technion

Zhuoyue Zhao, University of Utah

Chinmay Kulkarni, University of Utah

Nicolai Oswald, University of Edinburgh

Saksham Agarwal, Cornell University

Emmanouil Zampetakis, Massachusetts Institute of Technology

Manuela Fischer, ETH Zurich

Pranjal Dutta, Chennai Mathematical Institute

Thodoris Lykouris, Cornell University

Yuan Deng, Duke University

Ella Batty, Columbia University

Neha Spenta Wadia, University of California - Berkeley

Reuben Feinman, New York University

Human-Computer Interaction

Gierad Laput, Carnegie Mellon University

Mike Schaekermann, University of Waterloo

Minsuk (Brian) Kahng, Georgia Institute of Technology

Niels van Berkel, The University of Melbourne

Siqi Wu, Australian National University

Xiang Zhang, The University of New South Wales

Abhijeet Awasthi, Indian Institute of Technology - Bombay

Aditi Raghunathan, Stanford University

Futoshi Futami, University of Tokyo

Lin Chen, Yale University

Qian Yu, University of Southern California

Ravid Shwartz-Ziv, Hebrew University

Shuai Li, Chinese University of Hong Kong

Shuang Liu, University of California - San Diego

Stephen Tu, University of California - Berkeley

Steven James, University of the Witwatersrand

Xinchen Yan, University of Michigan

Zelda Mariet, Massachusetts Institute of Technology

Machine Perception, Speech Technology, and Computer Vision

Antoine Miech, INRIA

Arsha Nagrani, University of Oxford

Arulkumar S, Indian Institute of Technology - Madras

Joseph Redmon, University of Washington

Raymond Yeh, University of Illinois - Urbana-Champaign

Shanmukha Ramakrishna Vedantam, Georgia Institute of Technology

Lili Wei, Hong Kong University of Science & Technology

Rizanne Elbakly, Egypt-Japan University of Science and Technology

Shilin Zhu, University of California - San Diego

Anne Cocos, University of Pennsylvania

Hongwei Wang, Shanghai Jiao Tong University

Jonathan Herzig, Tel Aviv University

Rotem Dror, Technion - Israel Institute of Technology

Shikhar Vashishth, Indian Institute of Science - Bangalore

Yang Liu, University of Edinburgh

Yoon Kim, Harvard University

Zhehuai Chen, Shanghai Jiao Tong University

Imane khaouja, Université Internationale de Rabat

Aayush Jain, University of California - Los Angeles

Gowtham Kaki, Purdue University

Joseph Benedict Nyansiro, University of Dar es Salaam

Reyhaneh Jabbarvand, University of California - Irvine

Victor Lanvin, Fondation Sciences Mathématiques de Paris

Erika Ye, California Institute of Technology

Lingjiao Chen, University of Wisconsin - Madison

Andrea Lattuada, ETH Zurich

Chen Sun, Tsinghua University

Lana Josipovic, EPFL

Michael Schaarschmidt, University of Cambridge

Rachee Singh, University of Massachusetts - Amherst

Stephen Mallon, The University of Sydney

Chiu Wai Sam Wong, University of California, Berkeley

Eric Balkanski, Harvard University

Haifeng Xu, University of Southern California

Motahhare Eslami, University of Illinois, Urbana-Champaign

Sarah D'Angelo, Northwestern University

Sarah Mcroberts, University of Minnesota - Twin Cities

Sarah Webber, The University of Melbourne

Aude Genevay, Fondation Sciences Mathématiques de Paris

Dustin Tran, Columbia University

Jamie Hayes, University College London

Jin-Hwa Kim, Seoul National University

Ling Luo, The University of Sydney

Martin Arjovsky, New York University

Sayak Ray Chowdhury, Indian Institute of Science

Song Zuo, Tsinghua University

Taco Cohen, University of Amsterdam

Yuhuai Wu, University of Toronto

Yunhe Wang, Peking University

Yunye Gong, Cornell University

Avijit Dasgupta, International Institute of Information Technology - Hyderabad

Franziska Müller, Saarland University - Saarbrücken GSCS and Max Planck Institute for Informatics

George Trigeorgis, Imperial College London

Iro Armeni, Stanford University

Saining Xie, University of California, San Diego

Yu-Chuan Su, University of Texas, Austin

Sangeun Oh, Korea Advanced Institute of Science and Technology

Shuo Yang, Shanghai Jiao Tong University

Bidisha Samanta, Indian Institute of Technology Kharagpur

Ekaterina Vylomova, The University of Melbourne

Jianpeng Cheng, The University of Edinburgh

Kevin Clark, Stanford University

Meng Zhang, Tsinghua University

Preksha Nama, Indian Institute of Technology Madras

Tim Rocktaschel, University College London

Romain Gay, ENS - École Normale Supérieure

Xi He, Duke University

Yupeng Zhang, University of Maryland, College Park

Programming Languages, Algorithms and Software Engineering

Christoffer Quist Adamsen, Aarhus University

Muhammad Ali Gulzar, University of California, Los Angeles

Oded Padon, Tel-Aviv University

Amir Shaikhha, EPFL CS

Jingbo Shang, University of Illinois, Urbana-Champaign

Ahmed M. Said Mohamed Tawfik Issa, Georgia Institute of Technology

Khanh Nguyen, University of California, Irvine

Radhika Mittal, University of California, Berkeley

Ryan Beckett, Princeton University

Samaneh Movassaghi, Australian National University

Google Australia PhD Fellowships

Chitra Javali, Security, The University of New South Wales

Dana McKay, Human Computer Interaction, The University of Melbourne

Kwan Hui Lim, Machine Learning, The University of Melbourne

Weitao Xu, Machine Perception, The University of Queensland

Google East Asia PhD Fellowships

Chungkuk YOO, Mobile Computing, Korea Advanced Institute of Science and Technology

Hong ZHANG, Systems and Networking, The Hong Kong University of Science and Technology

Quanming YAO, Machine Learning, The Hong Kong University of Science and Technology

Tian TAN, Speech Technology, Shanghai Jiao Tong University

Woosang LIM, Machine Learning, Korea Advanced Institute of Science and Technology

Ying CHEN, Systems and Networking, Tsinghua University

Google India PhD Fellowships

Arpita Biswas, Algorithms, Indian Institute of Science

Aniruddha Singh Kushwaha, Networking, Indian Institute of Technology Bombay

Anirban Santara, Machine Learning, Indian Institute of Technology Kharagpur

Gurunath Reddy, Speech Technology, Indian Institute of Technology Kharagpur

Google North America, Europe and the Middle East PhD Fellowships

Cameron, Po-Hsuan Chen, Computational Neuroscience, Princeton University

Grace Lindsay, Computational Neuroscience, Columbia University

Martino Sorbaro Sindaci, Computational Neuroscience, The University of Edinburgh

Koki Nagano, Human-Computer Interaction, University of Southern California

Arvind Satyanarayan, Human-Computer Interaction, Stanford University

Amy Xian Zhang, Human-Computer Interaction, Massachusetts Institute of Technology

Olivier Bachem, Machine Learning, Swiss Federal Institute of Technology Zurich

Tianqi Chen, Machine Learning, University of Washington

Emily Denton, Machine Learning, New York University

Yves-Laurent Kom Samo, Machine Learning, University of Oxford

Daniel Jaymin Mankowitz, Machine Learning, Technion - Israel Institute of Technology

Lucas Maystre , Machine Learning, École Polytechnique Fédérale de Lausanne

Arvind Neelakantan, Machine Learning, University of Massachusetts, Amherst

Ludwig Schmidt, Machine Learning, Massachusetts Institute of Technology

Shandian Zhe, Machine Learning, Purdue University, West Lafayette

Eugen Beck, Machine Perception, RWTH Aachen University

Yu-Wei Chao, Machine Perception, University of Michigan, Ann Arbor

Wei Liu, Machine Perception, University of North Carolina at Chapel Hill

Aron Monszpart, Machine Perception, University College London

Thomas Schoeps, Machine Perception, Swiss Federal Institute of Technology Zurich

Chia-Yin Tsai, Machine Perception, Carnegie Mellon University

Hossein Esfandiari, Market Algorithms, University of Maryland, College Park

Sandy Heydrich, Market Algorithms, Saarland University - Saarbrucken GSCS

Rad Niazadeh, Market Algorithms, Cornell University

Sadra Yazdanbod, Market Algorithms, Georgia Institute of Technology

Lei Kang, Mobile Computing, University of Wisconsin

Tauhidur Rahman, Mobile Computing, Cornell University

Yuhao Zhu, Mobile Computing, University of Texas, Austin

Tamer Alkhouli, Natural Language Processing, RWTH Aachen University

Jose Camacho Collados, Natural Language Processing, Sapienza - Università di Roma

Kartik Nayak, Privacy and Security, University of Maryland, College Park

Nicolas Papernot, Privacy and Security, Pennsylvania State University

Damian Vizar, Privacy and Security, École Polytechnique Fédérale de Lausanne

Xi Wu, Privacy and Security, University of Wisconsin

Marcelo Sousa, Programming Languages and Software Engineering, University of Oxford

Xiang Ren, Structured Data and Database Management, University of Illinois, Urbana-Champaign

Andrew Crotty, Systems and Networking, Brown University

Ilias Marinos, Systems and Networking, University of Cambridge

Kay Ousterhout, Systems and Networking, University of California, Berkeley

Bahar Salehi, Natural Language Processing, University of Melbourne

Siqi Liu, Computational Neuroscience, University of Sydney

Qian Ge, Systems, University of New South Wales

Bo Xin, Artificial Intelligence, Peking University

Xingyu Zeng, Computer Vision, The Chinese University of Hong Kong

Suining He, Mobile Computing, The Hong Kong University of Science and Technology

Zhenzhe Zheng, Mobile Networking, Shanghai Jiao Tong University

Jinpeng Wang, Natural Language Processing, Peking University

Zijia Lin, Search and Information Retrieval, Tsinghua University

Shinae Woo, Networking and Distributed Systems, Korea Advanced Institute of Science and Technology

Jungdam Won, Robotics, Seoul National University

Palash Dey, Algorithms, Indian Institute of Science

Avisek Lahiri, Machine Perception, Indian Institute of Technology Kharagpur

Malavika Samak, Programming Languages and Software Engineering, Indian Institute of Science

Google Europe and the Middle East PhD Fellowships

Heike Adel, Natural Language Processing, University of Munich

Thang Bui, Speech Technology, University of Cambridge

Victoria Caparrós Cabezas, Distributed Systems, Swiss Federal Institute of Technology Zurich

Nadav Cohen, Machine Learning, The Hebrew University of Jerusalem

Josip Djolonga, Probabilistic Inference, Swiss Federal Institute of Technology Zurich

Jakob Julian Engel, Computer Vision, Technische Universität München

Nikola Gvozdiev, Computer Networking, University College London

Felix Hill, Language Understanding, University of Cambridge

Durk Kingma, Deep Learning, University of Amsterdam

Massimo Nicosia, Statistical Natural Language Processing, University of Trento

George Prekas, Operating Systems, École Polytechnique Fédérale de Lausanne

Roman Prutkin, Graph Algorithms, Karlsruhe Institute of Technology

Siva Reddy, Multilingual Semantic Parsing, The University of Edinburgh

Immanuel Trummer, Structured Data Analysis, École Polytechnique Fédérale de Lausanne

Margarita Vald, Security, Tel Aviv University

Google United States/Canada PhD Fellowships

Waleed Ammar, Natural Language Processing, Carnegie Mellon University

Justin Meza, Systems Reliability, Carnegie Mellon University

Nick Arnosti, Market Algorithms, Stanford University

Osbert Bastani, Programming Languages, Stanford University

Saurabh Gupta, Computer Vision, University of California, Berkeley

Masoud Moshref Javadi, Computer Networking, University of Southern California

Muhammad Naveed, Security, University of Illinois at Urbana-Champaign

Aaron Parks, Mobile Networking, University of Washington

Kyle Rector, Human Computer Interaction, University of Washington

Riley Spahn, Privacy, Columbia University

Yun Teng, Computer Graphics, University of California, Santa Barbara

Carl Vondrick, Machine Perception,, Massachusetts Institute of Technology

Xiaolan Wang, Structured Data, University of Massachusetts Amherst

Tan Zhang, Mobile Systems, University of Wisconsin-Madison

Wojciech Zaremba, Machine Learning, New York University

Guosheng Lin, Machine Perception, University of Adelaide

Kellie Webster, Natural Language Processing, University of Sydney

• Internal wiki

PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at http://mlg.eng.cam.ac.uk .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.

FURTHER INFORMATION ABOUT COMPLETING THE ADMISSIONS FORMS:

The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.

INFORMATION ABOUT THE CAMBRIDGE-TUEBINGEN PROGRAMME:

We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

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Fully Funded Swansea University PhD Scholarship: Enhancing Cardiomyocyte Dynamic Network Analysis with Machine Learning (ECIDNA-ML)

Funding providers: Swansea University's Faculty of Science and Engineering Subject areas: Computer Science ( Machine Learning /Pattern Recognition applied to molecular cardiology) Project start date

Computer Science Fully Funded Swansea University PhD Scholarship: Enhancing Cardiomyocyte Dynamic Network Analysis with Machine Learning (Ecidna-ML)

? Not funded by an EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Computer Science Fully Funded Swansea University PhD Scholarship: Enhancing

PhD Studentship: Fully Funded PhD Scholarship: Forecasting Future Environmental Impacts of Photovoltaic (PV) Manufacture Using Machine Learning Techniques

to leverage innovative machine learning (ML) techniques adept at handling sparse information. The impact of data pre-processing techniques will be assessed with the aim of improving the accuracy of predictions

Science and Engineering Fully Funded PhD Scholarship: Forecasting Future Environmental Impacts of Photovoltaic (PV) Manufacture Using Machine Learning Techniques

Research Framework Programme? Not funded by an EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Science and Engineering: Fully Funded PhD Scholarship

Fully Funded PhD Scholarship. Networks and Data Science for Advancing Healthcare Analytics: Machine Learning Applications to Model Health Outcomes in Social Care Settings

part of a major investment, Northeastern University London (NU London) has multiple, fully - funded PhD studentships available to accelerate its interdisciplinary research in the humanities, social

Civil Engineering: Fully Funded EPSRC and Swansea PhD Scholarship: Physics Informed Machine Learning in High-speed Railway infrastructure safety analysis

the development of machine learning , data-driven method has become a powerful tool for civil engineers. Especially, Physics Informed Machine Learning (PIMM) can be used as a comprehensive analysis considering both

Fully Funded EPSRC and Swansea PhD Scholarship: Physics Informed Machine Learning in High-speed Railway Infrastructure Safety Analysis

learning , data-driven method has become a powerful tool for civil engineers. Especially, Physics Informed Machine Learning (PIMM) can be used as a comprehensive analysis considering both physics-based

PhD Studentship: Big Data and Artificial Intelligence for Urban Mobility

We are excited to offer a fully funded PhD position at the intersection of big data, artificial intelligence, and urban mobility. The rapid development of big data analytics, along with advances in

The Universities for Nottingham and the Centre for Sustainable Chemistry - PhD Scholarships available

the areas of: Sustainable/resilient chemical supply chains Artificial Intelligence / Machine Learning for the prediction of bulk properties in performance molecules and materials Synthetic electrochemical

PhD Studentship: Enhancing Adaptive Control System Performance Using Advanced Machine Learning Techniques

Overview Qualification type: PhD Subject area: Control and Machine Learning Location/Campus: College Lane, Hatfield Closing application date: 10 June 2024 Start date: July 2024 or as soon as

Searches related to phd machine learning fully funded

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Unlocking Intelligence

We seek to understand the basis of intelligence in natural and artificial systems.

Deeper Learning

Our research blog highlights the newest research, findings and code from members of the Kempner community.

Our Community

We seek to build a community of scholars who work across boundaries and fields and are poised to solve some of the most compelling mysteries of our time. Coming together to learn, collaborate, and discover, Kempner community members are scientists who seek to fundamentally and radically advance our understanding of natural and artificial intelligence.

Our Mission

The Kempner's core mission is to innovate and advance the field of intelligence research and devise solutions to complex problems for the benefit of humanity.

Upcoming Events

Constructing spiking networks as a foundation for the theory of manifolds as computational substrate, transmission versus truth: ai models as cultural technologies and epistemic agents, recent news, a space for researchers to meet, and ai and natural intelligence to do the same, unlocking intelligence: the kempner institute.

Harvard Machine Learning Foundations Group

We are a research group focused on some of the foundational questions in modern machine learning. We are interested in both experimental and theoretical approaches that advance our understanding. Our group contains ML practitioners, theoretical computer scientists, statisticians, and neuroscientists, all sharing the goal of placing machine and natural learning on firmer foundations, and elucidating their fundamental capabilities and limitations.

Our group organizes the Kempner Seminar Series - a research seminar on the foundations of both natural and artificial learning. See mailing list , Google calendar , and list of talks .

Opportunities: We are looking for graduate students and postdocs . See opportunities section below. Announcements on positions will also be posted on social media .

Researchers

Gustaf ahdritz, phd student, alex atanasov, blake bordelon, chi-ning chou, ben edelman, lucas janson, samy jelassi, postdoctoral fellow, sham kakade, anat kleiman, depen morwani, costin-andrei oncescu, cengiz pehlevan, aayush karan, david brandfonbrener, yonadav shavit, nikhil vyas, sara kangaslahti, roy rinberg, natalie abreu, clara mohri, hanlin zhang, jonathan geuter, david alvarez melis, masters student, jacob zavatone-veth, runyu (cathy) zhang, finale doshi-velez, hima lakkaraju, michael mitzenmacher, morgane austern, preetum nakkiran, dimitris kalimeris, yamini bansal, sharon qian, tristan yang, undergraduate, recent publications.

By our group and its members.

(This list is not comprehensive. Also, we’re sometimes slow in updates—see individual homepages and the arXiv for the latest publications.)

Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit

NeurIPS 2022

Contrasting random and learned features in deep Bayesian linear regression

Manuscript 2022

Deconstructing Distributions: A Pointwise Framework of Learning

Depth induces scale-averaging in overparameterized linear bayesian neural networks.

55th Asilomar Conference 2021

Neural Networks as Kernel Learners: The Silent Alignment Effect

Inductive biases and variable creation in self-attention mechanisms, capacity of group-invariant linear readouts from equivariant representations: how many objects can be linearly classified under all possible views, revisiting model stitching to compare neural representations.

NeurIPS 2021

Learning Curves for SGD on Structured Features

Out-of-distribution generalization in kernel regression, asymptotics of representation learning in finite bayesian neural networks, for self-supervised learning, rationality implies generalization, provably, the deep bootstrap: good online learners are good offline generalizers, distributional generalization: a new kind of generalization.

Manuscript 2020.

Learning From Strategic Agents: Accuracy, Improvement, and Causality

Deep double descent: where bigger models and more data hurt, sgd on neural networks learns functions of increasing complexity.

NeurIPS 2019 spotlight talk (top 15% of accepted papers) .

More Data Can Hurt for Linear Regression: Sample-wise Double Descent

Manuscript. 2019.

Computational Limitations in Robust Classification and Win-Win Results

Minnorm training: an algorithm for training over-parameterized deep neural networks, adversarial robustness may be at odds with simplicity, on the information bottleneck theory of deep learning, recent & upcoming talks, brian depasquale, kim stachenfeld - predictive models for representation learning and simulation, stefano ermon - score entropy discrete diffusion models, andrea montanari - solving overparametrized systems of nonlinear equations, tom griffiths - using the tools of cognitive science to understand the behavior of large language models, larry abbott - modeling the navigational circuitry of the fly, rajesh rao - active predictive coding: a sensory-motor theory of the neocortex and a unifying framework for ai, noam brown - cicero: human-level performance in the game of diplomacy by combining language models with strategic reasoning, carsen stringer - unsupervised pretraining in biological neural networks, emmanuel abbe - logic reasoning and generalization on the unseen, denny zhou - teach language models to reason, tom goldstein - dataset security issues in generative ai, yann lecun - towards machines that can learn, reason, and plan., timothy lillicrap - model-based reinforcement learning and the future of language models, yejin choi - common sense: the dark matter of language and intelligence, seminar calendar.

Below is the calendar of events in the Kempner ML Foundations seminar. Join the mailing list for talk announcements.

Opportunities

We are looking for undergraduate researchers , graduate students and postdocs in the ML foundations group.

For undergraduate students , we are only able to work with students at Harvard or MIT (with preference to the former). If you are a Harvard or MIT student interested in collaborating, informally or formally, with us, please fill out the following google form . Students might also be interested in taking Boaz’s Spring 2023 seminar on the foundations of deep learning .

For graduate students we have openings in Computer Science , Electrical Engineering , applied mathematics or statistics degrees. New: Kempner Institute Graduate Fellowship: See more details here

If you are applying for graduate studies in CS and are interested in machine learning foundations, please mark both “Machine Learning” and “Theory of Computation” as areas of interest. Please also list the names of faculty you want to work with on your application. ML foundations group faculty include Demba Ba (Electrical Engineering and Bioengineering), David Alvarez-Melis , Boaz Barak , Sitan Chen , Jonathan Frankle , Sham Kakade (Computer Science), Cengiz Pehlevan (Applied Mathematics), and Lucas Janson (Statistics). There are also ML foundations affiliated faculty in all of the above departments and more. All of us are also open to the possibilities of co-advising students, including across different departments and schools.

Postdoc opportunities for 2024-2025 Academic year:

There are a number of opportunities at Harvard for postdoc positions. Applying to multiple positions is not just allowed but encouraged, and we urge you to apply to any of those that are of interest to you.

Kempner Institute Fellows - a three-year prestigious position for postdocs in AI/ML/related areas interested in “fundamentally advancing our understanding of natural and artificial intelligence.” Apply by October 9 2023

Computer science postdocs Postdocs in the ML foundations, Rabin Fellowship, Privacy Tools, Theory of Society. Coming soon.

Postdoctoral positions at Harvard Data Science initiative

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PhD Scholarship: Machine Learning at Luleå University, Sweden

PhD Scholarship: Machine Learning: Luleå University of Technology invites applications for a PhD scholarship in Machine Learning, based in Luleå, Sweden. This opportunity is part of the university’s commitment to cutting-edge research and its contribution to the development of a sustainable society. The successful candidate will join the Machine Learning Research Laboratory, contributing to a project focused on document analysis using machine learning techniques.

PhD Scholarship Opportunity: Machine Learning Research at Luleå University

Summary Table:

Study Area: Machine Learning

Location: Luleå, Sweden

Eligibility/Qualification: We seek a highly motivated individual with a Master’s degree in computer science or a related field. Applicants should possess experience in machine learning, Deep Learning, and statistics. Proficiency in English, both spoken and written, is essential. The successful candidate should demonstrate the ability to work independently as well as in collaborative research environments.

Description: The selected PhD student will become part of a project focused on the application of machine learning methods for document analysis. This involves working with historical and modern documents, ranging from blueprints to circuit diagrams, and employing innovative techniques to enhance document recognition. Collaboration with regional and national archives in Sweden will enrich the research experience, providing opportunities for interdisciplinary engagement.

Duties: As a PhD student, responsibilities include conducting experimental and theoretical research, presenting findings at national and international conferences, and publishing in scientific journals. While the primary focus is on research, there may be opportunities for involvement in teaching activities.

How to Apply: Interested candidates are encouraged to apply using the provided application button. Applications must include a CV, a personal letter outlining research interests and motivations, and copies of verified diplomas from previous educational institutions. All materials must be submitted in English or Swedish and marked with the reference number: 2011-2024.

Last Date to Apply: August 30, 2024

For further inquiries, please contact Professor Elisa Barney at [email protected] or by phone at +46 920 49 2194.

Union representatives: SACO-S Kjell Johansson (+46)920-49 1529 [email protected] OFR-S Lars Frisk, (+46)920-49 1792 [email protected]

In case of discrepancies between the English and Swedish versions of this announcement, the Swedish version prevails.

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Postdoctoral Associate in DNA Damage, University of Oxford, UK

Apply Now: IRSA Postdoctoral Fellowship

The School of Statistics and the Institute for Research in Statistics and Its Applications (IRSA) at the University of Minnesota invites applications for the IRSA Faragher Distinguished Postdoctoral Fellowship . This endowed fellowship seeks to bring exceptional scientists of outstanding potential to the university. This two year fellowship, with no teaching or service obligations, is to begin on August 28, 2023. The Fellow will hold the rank of postdoctoral research scientist. A competitive annual salary will be supplemented with generous funding for relocation, conference travel, and research support.

Applications in all areas of statistics or related data sciences will be considered: the primary selection criterion will be the candidates’ exceptional promise to produce high quality and visible research. Fellows will be expected to pursue a vigorous research agenda and to participate actively in the intellectual life of the school.

The school, like the university itself, is an extraordinarily vibrant academic community. We are especially interested in candidates who will contribute to the diversity and excellence of the academic community. Women and minorities are especially encouraged to apply.

Applications must be submitted online. See links below for full details. You may also visit the University of Minnesota Office of Human Resources site and search for Job ID 351577.

Internal Candidates

See full job description for internal candidates .

External Candidates

See full job description for external candidates .

The University of Minnesota shall provide equal access to and opportunity in its programs, facilities, and employment without regard to race, color, creed, religion, national origin, gender, age, marital status, disability, public assistance status, veteran status, sexual orientation, gender identity, or gender expression.

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PhD Proposal: Speeding up Density Functional Theory with Machine Learning

IRB IRB-4105

The Kohn-Sham (KS) equations are a nonlinear eigenvalue problem of the form $H[\rho]\Psi = E\Psi$, where $H$ is a real symmetric matrix called the \textit{Hamiltonian}, $\Psi$ is an eigenvector called the \textit{wave function}, $E$ is an eigenvalue called the \textit{energy}, and $\rho(\mathbf{r}) = \sum_i |\Psi_i(\mathbf{r})|^2$ is an real-valued field called the \textit{charge density}, which is unknown a priori. The KS equations are nonlinear in the sense that the matrix $H$ depends on the charge density $\rho$, which in turn depends on the eigenvectors $\Psi$ of $H$. Typically it is solved by fixed-point iteration, where an initial guess for $\rho$ is made and then a sequence of \textit{linear} eigenvalue problems are solved until convergence. The cost of the iteration is dominated by the eigenvalue problem. Consequently methods of reducing the number of requisite iterations are of great interest.

In this thesis, we investigate the use of machine-learning models for solving the KS equations. Our strategy is to develop machine-learning models which approximate numerical quantities in existing algorithms for electronic structure. In particular we use equivariant graph-neural-networks to predict the Kohn-Sham charge density. We show such methods obtain an average savings of $13\%$ in the number of iterations needed to reach convergence. Our methods are general, but we focus on learning data from catalysis, an application with potentially large environmental impact.

PhD Position Multivariate Dependence Modelling and Statistical Machine Learning Algorithms for Patient Risk Profiling

Job Information

Offer description.

The Applied Probability research group within the Delft Institute of Applied Mathematics at TU Delft (see https://www.tudelft.nl/ewi/over-de-faculteit/afdelingen/applied-mathema… ) is offering a full-time PhD position in the area of Multivariate dependence modelling and statistical machine learning algorithms for patient risk profiling.

In partnership with RECENTRE (see https://www.4tu.nl/recentre/ ), a project dedicated to helping people take charge of their health, we're working on mathematical approaches to quantify health-related risks. As a PhD researcher, you'll be at the forefront of this exciting journey, focusing on multivariate dependence models and statistical machine learning algorithms at its core to create personalized individual risk profiles. Along with developing novel methods and investigating their theoretical properties, you will also apply your methods and validate them using the latest available datasets.

The project will be co-supervised by Özge Şahin and Tina Nane in Applied Probability from TU Delft and Annemieke Witteveen in Biomedical Signals and Systems from the University of Twente.

Besides carrying out mathematical research and working on scientific articles, you will be expected to actively participate in seminars and discussions with colleagues from the department, as well as to present the findings at (inter)national conferences or workshops. Furthermore, you are supposed to carry out minor teaching tasks (at most 15% of the contract time). Teaching in English will be possible. Communication will be in English. If desired, there is the opportunity to attend Dutch language courses offered by the Language Center for Languages and Academic Skills at TU Delft, see https://www.tudelft.nl/en/tpm/itav/education/dutch-courses .

Requirements

• Curious Learner: You're eager to learn and explore, holding a Master's degree (MSc) in Statistics, Applied Mathematics, or a related field. You're passionate about using statistical, probabilistic, and mathematical principles to solve real-world problems in healthcare. While prior experience with copula methods is not required, it is a plus. A willingness to learn and apply new techniques in healthcare research is highly valued
• Analytical Thinker: With a strong background in statistics, probability theory, and mathematics, you excel at analyzing complex datasets to derive meaningful insights.
• Tech-Savvy: You're comfortable navigating through datasets, especially in healthcare, using your technical skills. You are proficient in programming languages in R or Python.
• Collaborative Team Player: You engage with stakeholders across academia, healthcare, and industry, fostering inclusive partnerships to drive innovation in healthcare.

Female candidates are strongly encouraged to apply for this position.

Doing a PhD at TU Delft requires English proficiency at a certain level to ensure that the candidate is able to communicate and interact well, participate in English-taught Doctoral Education courses, and write scientific articles and a final thesis. For more details please check the Graduate Schools Admission Requirements .

Additional Information

Doctoral candidates will be offered a 4-year period of employment in principle, but in the form of 2 employment contracts. An initial 1,5 year contract with an official go/no go progress assessment within 15 months. Followed by an additional contract for the remaining 2,5 years assuming everything goes well and performance requirements are met.

Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2770 per month in the first year to € 3539 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.

The TU Delft offers a customisable compensation package, discounts on health insurance, and a monthly work costs contribution. Flexible work schedules can be arranged.

For international applicants, TU Delft has the Coming to Delft Service . This service provides information for new international employees to help you prepare the relocation and to settle in the Netherlands. The Coming to Delft Service offers a Dual Career Programme for partners and they organise events to expand your (social) network.

Interested in this opportunity? To apply, click the application button and submit the following documents:

• Cover letter outlining your background and the motivation to pursue the PhD project (max. 1 page).
• A comprehesive Curriculum Vitae.
• Contact information of at least one academic referee.
• Transcripts and Master Degree / Diploma (if already available).
• Copy of Master thesis (or writing sample).

The position will remain open until June 23, and applications will be reviewed biweekly. The aim is to plan the interviews before the summer holidays (so until July 15th).

• A pre-employment screening can be part of the selection procedure.
• You can apply online. We will not process applications sent by email and/or post.
• Please do not contact us for unsolicited services.

For more information about this vacancy, please get in touch with Özge Şahin: [email protected] .

Work Location(s)

Where to apply.

Announcing the 2022 Apple Scholars in AIML

Apple is proud to announce the 2022 recipients of the Apple Scholars in AIML PhD fellowship.

We are committed to supporting the academic research community by amplifying emerging leaders in their field and their cutting-edge machine learning research.

The Apple Scholars in AIML PhD fellowship recognizes the contributions of emerging leaders in computer science and engineering at the graduate and postgraduate level. Each Scholar receives funding as they pursue their PhD, internship opportunities, and mentorship with an Apple researcher in their field. Apple Scholars in AIML are selected based on their innovative research, record as thought leaders and collaborators, and commitment to advance their respective fields.

Sushmita Bhattacharya

Yichen Jiang

Omar Khattab

Aviral Kumar

Venkatesh Potluri

Aahlad Puli

Elizabeth Salesky

Ortal Senouf

Moshe Shenfeld

Prune Truong

Eric Wallace

Serena Wang

Guodong Zhang

The Apple Scholars in AIML PhD fellowship program is currently open to invited institutions. For more information, please email [email protected].

Learn more about the 2021 cohort of Apple Scholars in AIML

Learn more about the inaugural class of Apple Scholars in AIML

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Announcing the 2024 apple scholars in aiml.

Apple is proud to announce the 2024 recipients of the Apple Scholars in AIML PhD fellowship.

Announcing the 2023 Apple Scholars in AIML

Apple is proud to announce the 2023 recipients of the Apple Scholars in AIML PhD fellowship.

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