Aaron Sidford

Aaron Sidford is an Associate Professor in the Department of Management Science and Engineering and the Department of Computer Science at Stanford University. He holds a PhD in Electrical Engineering and Computer Science from MIT, advised by Jonathan Kelner. His research focuses on optimization theory, algorithm design, and computational complexity, with significant contributions to convex optimization, graph algorithms, numerical linear algebra, and machine learning theory.

He has taught courses such as Introduction to Optimization Theory (MS&E213/CS269O) and Discrete Mathematics and Algorithms (CME305/MS&E316), emphasizing theoretical foundations and large-scale problem-solving. His work bridges continuous and discrete optimization, often leading to efficient algorithms with proven convergence guarantees.

Award highlights include the Best Paper Award at FOCS 2022 and COLT 2022, along with notable recognitions for contributions to dynamic graph algorithms and convex optimization. He advises PhD students focusing on optimization theory and its applications, and his research has been supported by grants from NSF, ONR, and industry partnerships.

His current research explores cutting-edge techniques in optimization, including faster max-flow algorithms, memory-efficient convex optimization, and adaptive gradient methods. He collaborates widely, contributing to both theoretical advancements and practical algorithmic implementations.