Andrew
Gordon Wilson
It is safe to assume I am taking PhD students and postdocs, unless I explicitly specify otherwise on this page. At the same time, admissions into our group is exceptionally competitive. The best way to improve your chances of admission is to make a detailed genuine case in your formal application about why our group, and me as an advisor, are the best fit for your interests and plans, and how you expect to contribute to the group. In order to make that case, you will need to meaningfully engage with the work we are doing, and understand our vision.
Fundamentally, we aim to develop a prescriptive approach to building autonomous intelligent systems. This effort involves a variety of different research initiatives, which cumulatively work together towards achieving this vision. A major theme that unifies many of these initiatives is progress towards an actionable understanding, so that we can select for particular properties aligned with human goals, including safe and reliable decision making. These areas, and some example papers, include:
• Understanding deep learning models, including LLMs and vision models
[e.g., 1, 2, 3, 4, 29, 30, 31]
• Uncertainty representation, Bayesian methods, online decision making
[e.g., 1, 5, 6, 7]
• Distribution shifts, spurious correlations [e.g., 8, 9, 10, 11]
• Encoding and learning inductive biases (e.g., equivariances) [e.g., 12, 13, 14, 15]
• Numerical linear algebra for scalable inference [e.g., 16, 17, 18, 19, 20]
• Machine learning for physics, and physics for ML [e.g., 21, 22, 13, 20, 15]
• Simple practical methods [e.g., 23, 24, 25, 26, 4]
• Scientific discovery (protein engineering, materials design) [e.g., 27, 28, 32]
If you wish to apply, I recommend reading some of our papers carefully, and describing how your interests connect to our work in your application. Try to pick out at least one paper and read every detail. A full list of papers is available at my Google Scholar. I also strongly recommend checking out some of my talks and interviews for a sense of my approach, and an overview of some of our work (note that much of our work is not covered in those talks). I advise students in Courant Computer Science (Dec 12 deadline), Mathematics (Dec 18 deadline), and the Center for Data Science (Dec 6 deadline).
For Fall 2024 admission, I will primarily be considering applications through Courant Computer Science. If you apply to math, feel welcome to let me know you applied. In general, you are free to e-mail me, but please only do so if you have carefully read some of my papers and believe there is a particularly compelling and specific connection with my group. I will not be able to reply to any generic messages. In general, do not be discouraged by a lack of response, as I receive many more e-mails than I can respond to. It is most important to simply list my name in your formal application.
This year I am particularly interested in generalization theory for an actionable understanding of foundation models (involving analysis rooted in math, physics, information theory), numerical methods as a foundation for machine learning (see CoLA and compute better spent), AI alignment (especially weak-to-strong generalization, truthfulness, mechanistic interpretability, and disentangled representations), and AI for science (biological sequence design, materials engineering, and discovering scientific theories and insights). However, I am open to virtually any direction that is aiming to answer foundational questions towards building intelligent systems, as long as you can make a good case for it.
Postdoctoral Fellow Applicants: E-mail me your CV and a short description of how your interests and plans connect to my group. The best way to work with me as a postdoc is through the CDS faculty fellowship program. But be mindful of the deadlines! The Courant CS faculty fellowship is also a nice option. I'd recommend applying to both. Your chances of something working out are best if you reach out directly as early as possible; for example, if you wish to start in Fall 2025, it's best to reach out to me in early Fall 2024.
Undergraduates: If you're at NYU, and you have done courses in machine learning, probability, and linear algebra, you can send me an e-mail with your CV.
Fundamentally, we aim to develop a prescriptive approach to building autonomous intelligent systems. This effort involves a variety of different research initiatives, which cumulatively work together towards achieving this vision. A major theme that unifies many of these initiatives is progress towards an actionable understanding, so that we can select for particular properties aligned with human goals, including safe and reliable decision making. These areas, and some example papers, include:
• Understanding deep learning models, including LLMs and vision models
[e.g., 1, 2, 3, 4, 29, 30, 31]
• Uncertainty representation, Bayesian methods, online decision making
[e.g., 1, 5, 6, 7]
• Distribution shifts, spurious correlations [e.g., 8, 9, 10, 11]
• Encoding and learning inductive biases (e.g., equivariances) [e.g., 12, 13, 14, 15]
• Numerical linear algebra for scalable inference [e.g., 16, 17, 18, 19, 20]
• Machine learning for physics, and physics for ML [e.g., 21, 22, 13, 20, 15]
• Simple practical methods [e.g., 23, 24, 25, 26, 4]
• Scientific discovery (protein engineering, materials design) [e.g., 27, 28, 32]
If you wish to apply, I recommend reading some of our papers carefully, and describing how your interests connect to our work in your application. Try to pick out at least one paper and read every detail. A full list of papers is available at my Google Scholar. I also strongly recommend checking out some of my talks and interviews for a sense of my approach, and an overview of some of our work (note that much of our work is not covered in those talks). I advise students in Courant Computer Science (Dec 12 deadline), Mathematics (Dec 18 deadline), and the Center for Data Science (Dec 6 deadline).
For Fall 2024 admission, I will primarily be considering applications through Courant Computer Science. If you apply to math, feel welcome to let me know you applied. In general, you are free to e-mail me, but please only do so if you have carefully read some of my papers and believe there is a particularly compelling and specific connection with my group. I will not be able to reply to any generic messages. In general, do not be discouraged by a lack of response, as I receive many more e-mails than I can respond to. It is most important to simply list my name in your formal application.
This year I am particularly interested in generalization theory for an actionable understanding of foundation models (involving analysis rooted in math, physics, information theory), numerical methods as a foundation for machine learning (see CoLA and compute better spent), AI alignment (especially weak-to-strong generalization, truthfulness, mechanistic interpretability, and disentangled representations), and AI for science (biological sequence design, materials engineering, and discovering scientific theories and insights). However, I am open to virtually any direction that is aiming to answer foundational questions towards building intelligent systems, as long as you can make a good case for it.
Postdoctoral Fellow Applicants: E-mail me your CV and a short description of how your interests and plans connect to my group. The best way to work with me as a postdoc is through the CDS faculty fellowship program. But be mindful of the deadlines! The Courant CS faculty fellowship is also a nice option. I'd recommend applying to both. Your chances of something working out are best if you reach out directly as early as possible; for example, if you wish to start in Fall 2025, it's best to reach out to me in early Fall 2024.
Undergraduates: If you're at NYU, and you have done courses in machine learning, probability, and linear algebra, you can send me an e-mail with your CV.