Lorenzo Orecchia

Lorenzo Orecchia is an Assistant Professor in the Department of Computer Science at the University of Chicago. His research focuses on designing efficient algorithms for computational challenges in machine learning and combinatorial optimization, leveraging convex optimization and first-order methods. He holds a PhD from UC Berkeley (2011) and was a postdoctoral instructor at MIT (2014). Notable awards include the 2014 SODA Best Paper Award and the 2020 NSF CAREER Award. He co-leads the Orecchia Research Group, which collaborates with the Theoretical Computer Science and Machine Learning Groups at UChicago.

Education: PhD in Computer Science, UC Berkeley (2011); Postdoctoral work at MIT (2011-2014).

Research interests include algorithm design, optimization frameworks combining continuous and discrete mathematics, and applications in machine learning. Recent work addresses BNN training via SDP relaxations, hypergraph algorithms, and fair resource allocation. He teaches courses like Introduction to Optimization in Computing and Machine Learning (CS 507) and CMSC 25460.

Funding: NSF CAREER Award (2020-2024) and prior grants from NSF CISE and CCF. Active in program committees for ITCS, FOCS, and NeurIPS.

Labs/Groups: Orecchia Group (focusing on algorithmic foundations), Theoretical Computer Science Group (bridging CS with mathematical sciences), and Machine Learning Group (advancing foundational ML techniques).