CS 839: AI for Scientific Computing

Spring 2026 • University of Wisconsin-Madison

📍 Course Logistics

👨 Instructor: Misha Khodak (khodak@wisc.edu)

📍 Location: Morgridge Hall 2538

🕘 Time: Tuesdays & Thursdays 1:00–2:15

🔗 Course Links

📋 Course Policies

📊 Grading

The final grade will be calculated based on:

Participation

There will be four types of lectures in this class: background lectures (by the instructor), research lectures (by the instructor or guests), paper presentations (by students), and project presentations (by students). During research lectures, paper presentations, and project presentations (but not background lectures), students will be expected to think of and submit questions to the speaker(s) via a Canvas quiz. Students do not need to ask these questions out loud if they do not wish to do so. All questions must be thought of independently. If someone else asks the same or similar question out loud before you can ask your own, you must submit the speaker's answer in addition to your own version of the question.


Questions can only be submitted to Canvas during class time. Each question will be assigned a binary grade based on relevance and significance (e.g. typo or basic clarification questions do not count). A third of the participation grade will be determined by taking the seven top-graded questions from across all research lectures, but only the first two questions during each lecture will be counted. The remaining thirds of the participation grade will be determined in the same manner based on the paper presentations and the project presentations, respectively. As there are expected to be more than four lectures of each type during the semester, this will allow flexibility in case a student is not able to attend some of the classes; this also means that requests to make up these points in some manner are extremely unlikely to be granted.

Presentations

Students will be expected to give two presentations, each accounting for half of the presentation grade. The first presentations will be of an existing paper or a cohesive line of work; this will be given in groups of 2-3 and will be assessed based on clarity of presentation, understanding of the material, and analysis of the consequences of the research. Topics should be determined in consultation with the instructor but can draw upon the students' own research backgrounds. The second presentation will be a progress report on the final project; this will be given in groups of 1-4 and will be assessed based on clarity of presentation, assessment of research progress and roadblocks, and analysis of next steps. A tentative timeline of when the presentations will occur is in the course schedule below.

Final Project

Students will complete a final project in groups of 3-4. Topics should be determined in consultation with the instructor but can draw upon the students' own research backgrounds, so long as they are sufficiently self-contained and relevant to the course topic. A two-page project proposal is due by midnight on March 27th; an eight-page project report is due by midnight on May 4th. Both may use unlimited room for references and appendices, should be typeset in LaTeX with 11pt font and 1-inch margins, and must be emailed to the instructor with all team members cc'd.

📅 Tentative Schedule

Date Lecture Slides Readings Notes
20 January Background lecture: Course overview & logistics slides
22 January Background lecture: Machine learning basics slides
27 January Background lecture: Advanced machine learning slides
29 January Background lecture: Scientific computing basics slides
3 February Background lecture: Neural operators slides
5 February Background lecture: Physics-informed neural networks slides
10 February Research lecture: Mariel Pettee
Invisible Cities: Imagining the next era of AI-enabled fundamental physics research [+ abstract]
Abstract: Some of the most exciting fundamental physics discoveries in recent years emerged thanks to large-scale experimental collaborations that radically differed from conventional scientific practices a century ago. The recent success of large-scale AI models trained on highly diverse data sources begs the question: could our scientific conventions yet again be restricting our access to major discoveries? In this talk, I propose that broadening our analyses across datasets, detectors, and even scientific disciplines could be critical to finally answering the grand mysteries of our Universe that have thus far eluded our usual strategies. To achieve this vision, AI methods can help us publish detector-agnostic datasets, construct richer embeddings of our data, and highlight connections across varied domains -- but we also need to take care to ensure that we design these tools to uphold our highest priorities as scientists.
12 February Background lecture: Symbolic regression
17 February Research lecture: Qin Li
19 February Research lecture: Misha Khodak
24 February Research lecture: Rogerio Jorge
26 February Research lecture: Xuhui Huang & Zige Liu
3 March Paper presentation
5 March Research lecture: Wenxiao Pan
10 March Paper presentation
12 March Paper presentation
17 March Paper presentation
19 March Paper presentation
24 March Paper presentation
26 March Paper presentation
7 April Paper presentation
9 April Paper presentation
14 April Project presentation
16 April Project presentation
21 April Project presentation
23 April Project presentation
28 April Project presentation
30 April Project presentation