"Six Years Of Information Theory, Probability, And Statistical Learning" by Cynthia Rush

Join us for an insightful lecture by Cynthia Rush, Associate Professor of Statistics at Columbia University, delivered as part of the workshop honoring Andrew Barron at Yale University.

Speaker Bio:
Cynthia Rush is an Associate Professor of Statistics in the Department of Statistics at Columbia University. She earned her Ph.D. in Statistics from Yale University in May 2016, working under the guidance of Andrew Barron. She completed her undergraduate studies at the University of North Carolina at Chapel Hill, where she obtained a B.S. in Mathematics. Dr. Rush's research interests lie at the intersection of information theory, statistical physics, and applied probability. Her work focuses on high-dimensional inference, estimation problems, and complex machine learning tasks. She aims to address fundamental questions about the data required to solve complex statistical problems, optimize data utilization, and characterize the limitations of statistical insights. She also develops and analyzes computationally efficient algorithms for statistical inference in challenging settings.

Abstract:
In this talk, Cynthia Rush discusses her experiences and what she learned while working under the guidance of Andrew Barron as a Ph.D. student at Yale University. She focuses on her studies of sparse regression codes and approximate message passing algorithms.

🔗 Related Links: fds.yale.edu

📅 Event: Workshop Honoring Andrew Barron: Forty Years at the Interplay of Information Theory, Probability, and Statistical Learning

📍 Location: Yale University, Kline Tower

🗓 Date: April 26-28, 2024