Assistant Professor Alex Wein’s research is a mix of theoretical computer science, statistics, probability, and data science. More specifically:

• Math of data science: what are the optimal algorithms for finding a hidden structure buried in random noise, and under what conditions is this even possible?
• Computational complexity of statistical inference, particularly in the low-degree polynomial model of computation
• Connections between Bayesian inference and statistical physics
• Problems involving group actions (e.g. determining 3-dimensional molecular structure via cryo-electron microscopy), including connections to representation theory and invariant theory