Things I have/plan to participated in
- 2026 Triangle Computational and Applied Mathematics Symposium (TriCAMS) [link here] [Fall 2026, Duke]
- 2026 Duke-ORNL Workshop on Advanced Microscopy & Data-Driven Materials Science [link here] [Fall 2026, Duke]
- Rhodes IID Adventures in Sampling [link here] [Fall 2026, Duke]
- Duke Math Graduate Summer School in Stochastics, Dynamics, and Machine Learning [link here] [Summer 2026, Duke]
- Gene Golub SIAM Summer School on Fault-tolerant Algorithms in Quantum Computing [link here] [Summer 2026, Duke]
- QuEra Neutral Atom and Quantum Error Correction Workshop [Summer 2026, Oak Ridge National Lab]
- Rhodes IID Algorithmic Advances in Generative AI Conference [Spring 2026, Duke]
Some perspectives on LLMs in math
- “Happy, those able to know the causes of things”; by Nestor Guillen [blog post here]
- “Mathematics in the age of AI”; by Terence Tao, public lecture at ICM 2026 [slides here]
- “A Severe Misalignment of AI in Mathematics” [declaration here]
Resources I like
- MSRI Summer School on Electronic Structure Theory; by Lin Lin and Jianfeng Lu; 2016 [recordings on MSRI here]
- Handbook of Convergence Theorems for (Stochastic) Gradient Methods; by Guillaume Garrigos and Robert M. Gower; last revised 2024 [arxiv here]
- An Introduction to Flow Matching and Diffusion Models; by Peter Holderrieth and Ezra Erives; last revised 2026 [arxiv here]
- An Introduction to Variational Autoencoders; by Durk Kingma and Max Welling; last revised 2019 [arxiv here]