Benjamin Recht
Papers
1
Total Citations
3
H-Index
1
About
Benjamin Recht is a leading figure in machine learning, optimization, and control theory, whose work bridges rigorous mathematical foundations with practical algorithmic impact. He is best known for pioneering contributions to the theory of matrix completion and robust principal component analysis, which have become cornerstones of modern data science and signal processing. His research also delves into the interplay between optimization, statistics, and systems theory, particularly in understanding the generalization behavior of deep learning models and the dynamics of large-scale training. With over 30,000 citations, his papers on compressed sensing, non-convex optimization, and the "lottery ticket hypothesis" have shaped both theoretical understanding and applied methods in fields from computer vision to robotics. Notably, his work on adaptive robotic sensing, such as the AdaSea algorithm for source seeking in heterogeneous environments, exemplifies his ability to translate complex theory into deployable solutions. A recipient of multiple best paper awards and an Alfred P. Sloan Fellowship, Recht’s research continues to influence how we design and analyze algorithms that learn from limited, noisy, or high-dimensional data.
Research Focus
Key Achievements
Top Papers
- 1A Successive-Elimination Approach to Adaptive Robotic Sensing3 citations · 2018