Alex Wein

Alex Wein is an Assistant Professor of Mathematics at the University of California, Davis. His research bridges theoretical computer science, statistics, and probability, with a focus on the mathematical foundations of data science. Key areas include understanding optimal algorithms for signal detection in noise, computational complexity of statistical inference (especially via the low-degree polynomial framework), tensor analysis, and applications of group actions in computational problems.

Research Interests:

• Mathematics of data science: optimal algorithms for hidden structure detection
• Computational-statistical gaps via low-degree polynomials
• Tensors: computational challenges and applications
• Bayesian inference and connections to statistical physics
• Group actions in molecular structure determination and representation theory

Recent Talks:

• Banff International Research Station (2024): 'Optimality of AMP Among Low-Degree Polynomials'
• Bernoulli-IMS Symposium (2020): 'Low-Degree Framework for Statistical Inference'

Professional Service:

• Program committee member for COLT, STOC, FOCS
• Organizer of workshops on computational complexity and statistical inference