Ben’s research focuses on scalable computational tools for large-scale data analysis, statistical signal processing, and machine learning. He explores the intersections of convex optimization, mathematical statistics, and randomized algorithms. He is particularly interested in simplifying the analysis and manipulation of noisy and incomplete data by exploiting domain-specific knowledge and prior information about structure. Ben is the recipient of an NSF Career Award, an Alfred P. Sloan Research Fellowship, and the 2012 SIAM/MOS Lagrange Prize in Continuous Optimization. He is currently on the Editorial Boards of Mathematical Programming and the Journal for Machine Learning Research.