Email: t.chaipat AT columbia.edu
I am a senior at Columbia University, studying astrophysics (and mathematical probability). Recently, I have been thinking a lot about interpretability, representation learning, and agentic systems for science.
I have used tools from statistics and physics to build robust, trustworthy ML systems for scientists. My work with the Learning the Universe collaboration focuses on developing a robust parameter-sampling algorithm and a degeneracy detection framework for simulation-based inference.
Aside from research, I have a Substack where I write about AI, philosophy, and the anthropology of progress.
Learning at the Edge: Tailed-Uniform Sampling for Robust Simulation-Based Inference
Chaipat Tirapongprasert and Matthew Ho
[ Paper | Conference Paper ]
DegenDetector: Symbolic Recovery of Parameter Degeneracies in Bayesian Posteriors
Chaipat Tirapongprasert and Matthew Ho
[ Conference Paper]
Build Your Own AI Tutors and Research Agents
Detecting Degeneracies for Simulation-based Inference [ Slides | Poster ]
Tailed-Uniform Simulations for Simulation-Based Inference [ Slides | Poster ]