AI Methods for Science
Boston University · Spring 2026
About the Course
AI methods are increasingly central to how science gets done, spanning simulation, experiment, theory, and observation. This course aims to equip students with the methods to understand and carry out research at the intersection of AI and the natural sciences. Topics include probabilistic inference, neural networks that encode physical symmetries and domain knowledge, generative models for scientific data, and simulation-based inference. While framed in terms of scientific applications, the methods discussed extend well beyond scientific research, with broad applicability across industry and general AI R&D.
A major focus of the course is on large language models and their emerging role in science. As LLMs become more capable of scientific reasoning and operating autonomously, understanding how to evaluate, adapt, and collaborate with these systems is becoming essential. We explore what it means to work alongside AI scientists, and how to critically assess their capabilities as well as limitations.
Applications are drawn from domains including physics, materials science, and biology. The course involves two assignments emphasizing method design and critical analysis in collaboration with AI tools, plus two projects: a midterm applying AI methods to a scientific problem, and a final project finetuning an LLM to elicit a scientific capability.
Learning Objectives
By the end of this course, students will be able to:
- Apply probabilistic inference and sampling methods (e.g., MCMC) to scientific problems
- Design neural networks that encode scientific domain knowledge
- Train generative models (e.g., diffusion models) to emulate scientific data distributions
- Use simulation-based inference to connect simulators with observations
- Evaluate the edges and limitations of LLM capabilities for scientific reasoning
- Develop intuitions for how to collaborate effectively with AI systems on research tasks
- Read and understand AI-for-science research papers
Logistics
- Lecture
- Mon/Wed 12:20–1:35pm, CAS 218
- Discussion
- Tue 11:15am–12:05pm, MUG 205
- Instructor
- Siddharth Mishra-Sharma (smishras@bu.edu)
- TF
- Wanli Cheng (cwl1997@bu.edu)
- Office Hours
- Tue 3–5pm or by appointment, CDS 1528
- TF Office Hours
- Mon/Wed 2:15–3:15pm or by appointment, CDS 14th floor Green Corner
Resources
- Syllabus: PDF
- Discussion: Ed Discussion
- Assignment/lab submission: GitHub Classroom
- Computing: GPU access through the Shared Computing Cluster (SCC) and LLM API/finetuning credits will be provided for project work
- Coding agents: Getting free subscriptions
There is no required textbook. Many readings reference Understanding Deep Learning by Simon J.D. Prince (MIT Press, 2023); the PDF is available on the website. Other readings are drawn from research papers and online resources.
Schedule and Optional Reading
This schedule will change as the course progresses.
Discussion Sections
Tuesdays 11:15am–12:05pm in MUG 205.
| Week | Date | Topic | Notes |
|---|---|---|---|
| — | Tue Jan 20 | No discussion | First day of classes |
| 2 | Tue Jan 27 | Lab 1: JAX and Bayesian Inference | Starter Due Wed Jan 28 |
| 3 | Tue Feb 3 | Lab 2: Hamiltonian Monte Carlo | Starter Due Wed Feb 4 |
| 4 | Tue Feb 10 | Lab 3: Training Neural Networks | Starter Due Wed Feb 11 |
| — | Tue Feb 17 | No discussion | Substitute Monday schedule |
| 6 | Tue Feb 24 | Lab 4: Variational Autoencoders | Starter Due Wed Feb 25 |
| 7 | Tue Mar 3 | Lab 5: Diffusion | Due Wed Mar 4 |
| — | Mar 7–15 | No discussion | Spring Recess |
| 8 | Tue Mar 17 | Lab 6: Differentiable Programming | Due Wed Mar 18 |
| 9 | Tue Mar 24 | Lab 7: Reinforcement Learning | Due Wed Mar 25 |
| 10 | Tue Mar 31 | Lab 8: Simulation-Based Inference | Due Wed Apr 1 |
| 11–14 | Apr | Final project work | Proposal due Apr 17, Report due May 4 |
Topics Not Covered
Due to time constraints, this course does not cover several areas in AI for science, including neural operators, physics-informed learning, surrogate modeling, symbolic regression, causal inference, interpretability methods, experimental design, active learning, and recent AI-for-math developments (e.g., LLM-guided theorem proving). Some of these may be covered in later weeks as the course evolves.
Assessment
Discussion Labs
Weekly in-class labs reinforce lecture material through hands-on programming. Students work through a notebook during discussion, exploring implementations and comparing results. Graded on participation and completion. Labs are due end of day Wednesday.
Assignments
Three assignments develop skills in method design and critical analysis. AI tools may be used freely, but the analysis and interpretation require critically engaging with what was produced. The discussion labs build foundational skills for these assignments.
- Assignment 1: Sampler Synthesis Starter: Design and stress-test a novel sampling or variational inference method
- Assignment 2: Ablation Archaeology Starter: Systematically ablate components of a geometric neural network to understand what each design choice contributes and why
- Assignment 3: TBA
Final Project
Teams of 2–3 identify a scientific capability that current large language models struggle with, then finetune a language model to improve that capability. This is a two-stage project:
- Proposal: Demonstrate that LLMs struggle at a specific scientific capability by curating a dataset or benchmark
- Final report (~6 pages, NeurIPS format): Fine-tuned model and evaluation showing improvement, including artifacts used for fine-tuning (datasets, code, reinforcement learning environments)
Timeline
| Deliverable | Out | Due |
|---|---|---|
| Discussion Labs | Tuesdays | Wednesday following lab |
| Assignment 1 | Wed Feb 4 | Wed Feb 18 |
| Assignment 2 | Wed Feb 18 | Wed Mar 4 |
| Assignment 3 | Wed Mar 4 | Wed Mar 25 |
| Final Project | Mon Mar 30 | Proposal: Fri Apr 17 Report: Mon May 4 |
Policies
Attendance
Regular attendance in lectures is expected. Please notify the instructor of planned absences.
Late Work
Late submissions are not accepted without prior arrangement. Extensions may be granted for documented emergencies.
Collaboration
Discussion of concepts and approaches is encouraged. However, all submitted code and written work must be your own. When collaborating, you must acknowledge your collaborators.
AI Tools
Learning to work effectively with AI is itself a course objective. Use AI tools freely to explore ideas, debug code, and deepen understanding. Focus on building genuine competence—understanding why something works, not just that it works. Disclose AI assistance in submissions, including its form and extent. See also the CDS GAIA policy.
Academic Conduct
All students are expected to read and abide by the BU Academic Code of Conduct. Plagiarism includes copying or restating work or ideas of another person or AI software without citing the source. In computing coursework, this includes sharing code, reusing code across courses without permission, and uploading assignments to external sites. Please review the examples of plagiarism provided by the BU Computer Science department. All suspected cases of plagiarism will be reported to the Academic Dean.
Accommodations
Boston University is committed to providing reasonable accommodations to students with documented disabilities. Students seeking accommodations should contact Disability & Access Services (25 Buick Street, Suite 300; 617-353-3658) as early as possible in the semester. A new Faculty Accommodation Letter (FAL) must be requested each semester; DAS will send this directly to instructors.
Religious Observance
Students observing a religious holiday during regularly scheduled class time are entitled to an excused absence. Please notify the instructor in advance to make arrangements for any missed work.
Recordings
Recording of lectures requires instructor permission. Students approved for recording as an accommodation must limit use to personal study and may not share recordings.