معرفی
Michael Celentano is a Research Fellow (Postdoctoral Miller Fellow) at the University of California, Berkeley's Department of Statistics, and a Member of Technical Staff at OpenAI. He holds a PhD in Statistics from Stanford University (2021), advised by Andrea Montanari, alongside master's (2016) and bachelor's (2014) degrees in Mathematics/Physics and Electrical Engineering from Stanford. His research focuses on high-dimensional data algorithms, bias correction in complex models, and first-order methods' statistical analysis. He collaborates with leading researchers like Yun Song and Martin Wainwright, and his work bridges semiparametric theory, causal inference, and biological applications. Celentano has organized the Online Causal Inference Seminar and contributed to the Simons Institute's Computational Complexity of Statistical Inference program. His honors include the Theodore W. Anderson Theory of Statistics Award and an NSF Graduate Research Fellowship.
Research Interests:
- Algorithmic bias mitigation in high-dimensional prediction
- Average-case analysis of non-convex optimization
- Variational Bayesian inference
- High-dimensional causal inference
- Phylodynamic modeling in evolutionary biology
Labs/Teams: Collaborates with the labs of Yun Song (UC Berkeley) and Martin Wainwright, contributing to interdisciplinary projects at the Simons Institute and OpenAI.
