Summer School 2025
The mission of the IAIFI PhD Summer School is to leverage the expertise of IAIFI researchers, affiliates, and partners toward promoting education and workforce development.
- August 4–8, 2025
- Harvard, Cambridge, MA
The Summer School will be followed by the IAIFI Summer Workshop, which is open to researchers of all career stages.
Agenda Lecturers Tutorial Leads Accommodations Costs Sponsors FAQ Past Schools
About
The Institute for Artificial Intelligence and Fundamental Interactions (IAIFI) is enabling physics discoveries and advancing foundational AI through the development of novel AI approaches that incorporate first principles, best practices, and domain knowledge from fundamental physics. The Summer School will include lectures and events that illustrate interdisciplinary research at the intersection AI and Physics, and encourage global networking. Hands-on code-based tutorials that build on foundational lecture materials help students put theory into practice, and a hackathon project provides an opportunity for students to collaborate and apply what they’ve learned.
Apply
Applications are now closed for the 2025 IAIFI Summer School. Subscribe to our mailing list to receive updates on future opportunities.
Accommodations
Students for the Summer School have the option to reserve dorm rooms at Harvard University. IAIFI will reimburse the cost of 5 nights in the dorms after the Summer School, contingent upon attendance. Instructions for this will be provided to students upon acceptance.
Costs
- There is no registration fee for the Summer School. Costs of dorm accommodations will be reimbursed by IAIFI, contingent upon attendance. Students for the Summer School are expected to cover the cost of travel.
- Lunch each day, as well as coffee and snacks at breaks, will be provided during the Summer School, along with at least one dinner during the Summer School.
- Students who wish to stay for the IAIFI Summer Workshop will be able to book the same rooms through the weekend and the Workshop if they choose (at their own expense).
Lecturers
Topic: Reinforcement Learning
Lecturer: Sasha Rakhlin, Professor, MIT
Topic: Domain Shift Problem: Building Robust AI Models with Domain Adaptation
Lecturer: Aleksandra Ćiprajanović, Wilson Fellow Associate Scientist, Fermilab
Topic: Physics-Motivated Optimization
Lecturer: Gaia Grosso, IAIFI Fellow
Topic: Representation/Manifold Learning: Geometric Deep Learning
Lecturer: SueYeon Chung, Assistant Professor of Neural Science, NYU
Tutorial Leads
Topic: Reinforcement Learning
Tutorial Lead: Margalit Glasgow, Postdoc, MIT
Topic: Robust/Interpretable AI: Domain Adaptation
Tutorial Lead: Sneh Pandya, PhD Student, Northeastern/IAIFI
Topic: Physics-Motivated Optimization: Simulation Intelligence
Tutorial Lead: Sean Benevedes, PhD Student, MIT and Jigyasa Nigam, Postdoc, MIT
Topic: Representation/Manifold Learning: Geometric Deep Learning
Tutorial Lead: Sam Bright-Thonney, IAIFI Fellow
Agenda
Agenda is subject to change.
Monday, August 4, 2025
9:00–9:30 am ET
Welcome/Introduction
9:30 am–12:00 pm ET
Lecture 1: Reinforcement Learning (Sasha Rakhlin, MIT)
12:00–1:00 pm ET
Lunch
1:00–3:30 pm ET
Tutorial 1: Reinforcement Learning (TBA)
3:30–4:30 pm ET
Hackathon Introduction
5:00–5:30 pm ET
Break
5:30–7:30 pm ET
Welcome Dinner
Tuesday, August 4, 2025
9:00–9:30 am ET
Lightning Talks
9:30 am–12:00 pm ET
Lecture 2: Physics-Motivated Optimization (Gaia Grosso, IAIFI Fellow)
12:00–1:00 pm ET
Lunch
1:00–3:30 pm ET
Tutorial 2: Physics-Motivated Optimization (Sean Benevedes, MIT and Jigyasa Nigam, MIT)
3:30–4:30 pm ET
Breakout Sessions with Lecturers and Tutorial Leads
4:30–6:00 pm ET
Group work for hackathon
Wednesday, August 6, 2025
9:00–9:30 am ET
Lightning Talks
9:30 am–12:00 pm ET
Lecture 3: Geometric Deep Learning (SueYeon Chung, Harvard)
12:00–1:00 pm ET
Lunch
1:00–3:30 pm ET
Tutorial 3: Geometric Deep Learning (Sam Bright-Thonney, IAIFI Fellow)
3:30–4:30 pm ET
Career Panel
4:30–5:30 pm ET
Break
5:30–6:30 pm ET
Fireside Chat with Boris Hanin (Princeton University) sponsored by PDT Partners
6:30–8:00 pm ET
Networking event with IAIFI sponsored by PDT Partners
Thursday, August 7, 2025
9:00–9:30 am ET
Lightning Talks
9:30 am–12:00 pm ET
Lecture 4: Domain Shift Problem: Building Robust AI Models with Domain Adaptation (Aleksandra Ciprajanovic, Fermilab)
Artificial Intelligence (AI) is revolutionizing physics research—from probing the large-scale structure of the Universe to modeling subatomic interactions and fundamental forces. Yet, a major challenge persists: AI models trained on simulations or old experiment / astronomical survey often perform poorly when applied to new data—exposing issues of dataset (domain) shift, model robustness, and uncertainty in predictions. This summer school session will introduce students to common challenges in applying AI across domains and present solutions based on domain adaptation—a set of techniques designed to improve model generalization under domain shift. We will cover foundational ideas, practical strategies, and current research frontiers in this area. Through examples in astrophysics, we’ll explore how domain adaptation can help bridge the gap between synthetic and real-world data, improve trust in model outputs, and advance scientific discovery. The concepts discussed are broadly applicable across physics and other scientific disciplines, making this a valuable topic for anyone interested in building robust, transferable AI models for science.
Resources:
12:00–1:00 pm ET
Lunch
1:00–3:30 pm ET
Tutorial 4: Domain Shift Problem: Building Robust AI Models with Domain Adaptation (Sneh Pandya, Northeastern)
3:30–4:30 pm ET
Breakout Sessions with Lecturers and Tutorial Leads
4:30–5:30 pm ET
Group work for hackathon
Friday, August 9, 2024
9:00–9:30 am ET
Lightning Talks
9:30 am–12:00 pm ET
Hackathon
Projects
Project details to come.12:00–1:00 pm ET
Lunch
1:00–3:00 pm ET
Hackathon
Projects
Project details to come.3:00–3:45 pm ET
Hackathon presentations
3:45–4:00 pm ET
Closing
Financial Supporters
The Summer School is funded primarily by support from the National Science Foundation under Cooperative Agreement PHY-2019786. Computing resources are provided by the NSF ACCESS program.
We extend a sincere thank you to the following financial supporters of this year’s IAIFI Summer School:
If you are interested in supporting the 2025 IAIFI Summer School, email iaifi-summer@mit.edu
2025 Organizing Committee
- Fabian Ruehle, Chair (Northeastern University)
- Bill Freeman (MIT)
- Cora Dvorkin (Harvard)
- Thomas Harvey (IAIFI Fellow)
- Sam Bright-Thonney (IAIFI Fellow)
- Sneh Pandya (Northeastern)
- Yidi Qi (Northeastern)
- Manos Theodosis (Harvard)
- Marshall Taylor (MIT)
- Marisa LaFleur (IAIFI Project Manager)
- Thomas Bradford (IAIFI Project Coordinator)
FAQ
- Who can apply to the Summer School? Any PhD students or early career researchers working at the intersection of physics and AI may apply to the summer school.
- What is the cost to attend the Summer School? There is no registration fee for the Summer School. Students for the Summer School are expected to cover the cost of travel.
- Is there funding available to support my attendance at the Summer School? IAIFI is covering the cost of the Summer School, including lunch each day. There is no support available for travel costs.
- If I come to the Summer School, can I also attend the Workshop? Yes! We encourage you to stay for the IAIFI Summer Workshop and you can stay in the dorms for both events if you choose (at your own expense). Information about the Summer Workshop will be provided in early 2025.
- Will the recordings of the lectures be available? We expect to share recordings of the lectures after the Summer School.
- Will there be an option for virtual attendance? Yes, there is an option for virtual attendance.
- How can I book a dorm for the IAIFI Summer School? Information will be shared with accepted students about booking the dorms.
- What if I need childcare in order to attend the Summer School? We are prepared to work with attendees to help coordinate child care as needed. Please contact iaifi-summer@mit.edu and/or indicate it in your application if you would like to discuss.