Sahil Singla

Sahil Singla is an Assistant Professor in the School of Computer Science at Georgia Institute of Technology (Georgia Tech). He holds affiliations with the Algorithms and Randomness Center (ARC). His research focuses on algorithms under uncertainty, including online algorithms, algorithmic game theory, stochastic optimization, and learning theory. Singla earned his Ph.D. in Computer Science from Carnegie Mellon University in 2018, advised by Anupam Gupta and Manuel Blum. Before joining Georgia Tech, he was a Research Instructor (postdoc) at Princeton University and the Institute for Advanced Study (2018–2021).

Education:

• Ph.D. in Computer Science, Carnegie Mellon University (2018)
• M.Math in Computer Science, University of Waterloo (2013)
• B.Tech in Computer Science, IIT Delhi (2011)

Research Interests:

His work centers on designing optimal algorithms for uncertain inputs, exploring uncertainty models from online algorithms, game theory, stochastic optimization, and bandit learning. Key themes include prophet inequalities, secretary problems, and combinatorial auctions. He has contributed to foundational advances in discrepancy theory and adaptive algorithms.

Publications:

Recent work emphasizes robust online resource allocation, supermodular norms, and prophet inequalities under graphical dependencies. His research bridges theoretical computer science with applications in economics and operations research.

Advising & Grants:

• Advises PhD students including Siddharth Meenachi Sundaram, Zhimeng Gao, and Kalen Patton
• Recipient of grants from NSF and Simons Foundation
• Organizes workshops on online algorithms and resource allocation (Dagstuhl, STOC)