Jason Altschuler

Jason Altschuler is an Assistant Professor of Statistics and Data Science at the University of Pennsylvania's Department of Statistics and Data Science. He also holds secondary appointments in Computer and Information Science and Electrical and Systems Engineering. His research interests include optimization, probability, machine learning, mathematics of data science, and optimal transport. Altschuler's work has been published in top-tier journals and conferences, including the Journal of the ACM, SIAM Journal on the Mathematics of Data Science, and COLT. He is affiliated with the UPenn Optimization Seminar and maintains a personal website at https://statistics.wharton.upenn.edu/profile/alts.

Jason M. Altschuler is a researcher whose work focuses on Optimization, Machine Learning, Mathematics of Data Science, and Optimal Transport. His research explores efficient algorithms for solving complex problems in these areas, including approximation algorithms for optimal transport, privacy-preserving methods in stochastic gradient descent, and scalable techniques for computing Wasserstein barycenters. His publications highlight the intersection of theoretical and applied aspects of data science, emphasizing algorithmic efficiency and convergence properties. Altschuler's contributions span both foundational and applied research, with a focus on developing practical solutions for large-scale data analysis and machine learning.