MSAILMichigan Student AI Lab
Reading groups, build-from-scratch projects, and a 37-talk archive, run by students at the University of Michigan since 2008.
The M is a live rectified flow, the sampler inside Stable Diffusion 3 and Flux. Random dots are matched to spots in the M by optimal transport, then each walks its straight line: x_t = (1 − t)·x₀ + t·x₁. The shimmer after it lands is Langevin noise keeping the dots alive.
We strive to spread our passion for AI throughout the University of Michigan student body, regardless of demographic or academic standing.
MSAIL was founded in 2008. During the semester we run reading groups and project teams; since 2020 the speaker series has hosted University of Michigan faculty, PhD researchers, and engineering teams from Bloomberg, ProQuest, and Datature.
Initiatives
All initiatives and the archiveCompetitive Build Initiative
Team up with other MSAIL members for AI competitions and long-term hackathons.
Industry Project Team
Join a five-person team of student consultants and a project manager, working with industry professionals at the automotive supplier KPIT to build a full-scale automation tool from planning to deployment. Projects run at least a semester and focus on automating testing, safety, or company processes, with extensive full-stack development alongside agentic AI integration. Python experience is required. React, Next.js or Node.js, and database experience are preferred; industry experience is not required. We're looking for balanced teams with both frontend and backend experience.
Model Mining: Alignment & Interp
Hands-on work with language models: extracting the behaviors you want, then using evals and mechanistic interpretability to check alignment and understand what is going on inside. Tentative topics include SFT, DPO, RL, causal steering, evals, and patching. Neural network experience required; LLM or math background helps.
Building a Convolutional Neural Network
Learn how to build a convolutional neural network from the ground up over the course of the semester. This initiative is designed for everyone from beginners with no programming experience to students who already code but have never applied their skills to AI. We'll work through the fundamentals of Python, neural networks, image classification, model training, and evaluation, with each session building toward a final CNN of your own.
ML in the News
Come sit in as we break down AI and machine learning concepts through research paper summaries and deep dives of major news stories. Learn more about the technology you either can't get rid of or can't get enough of. Each week, we'll work to better understand the methods, claims, and real-world implications behind new developments in AI.
37 recorded talks, 2020 to 2024
- AlphaFold 2 and the Protein Folding Problem
- Concrete Problems in AI Safety
- Scaling Neural Tangent Kernels via Sketching and Random Features
- Faculty Talk: Cognitive Architecture
- The Trend Towards Large Language Models
- Fairness in Machine Learning
Find us
MSAIL is open to every University of Michigan student. These are our channels; Slack is the most active.