Lorenzo Orecchia is an assistant professor in the Department of Computer Science at the University of Chicago. Lorenzo’s research focuses on the design of efficient algorithms for fundamental computational challenges in machine learning and combinatorial optimization. His approach is based on combining ideas from continuous and discrete optimization into a single framework for algorithm design. Lorenzo obtained his PhD in computer science at UC Berkeley under the supervision of Satish Rao in 2011, and was an applied mathematics instructor at MIT under the supervision of Jon Kelner until 2014. He was a recipient of the 2014 SODA Best Paper award and a co-organizer of the Simons semester “Bridging Continuous and Discrete Optimization” in Fall 2017.
Focus Areas: Combinatorial Optimization, Machine Learning, Numerical Analysis
My research aims to design simple, efficient algorithms for foundational computational challenges arising in a variety of applications, spanning Theoretical Computer Science, Machine Learning and Mathematical Optimization.
The unifying thread behind my works is the use of convex optimization and first-order methods as a generic design framework for both combinatorial and continuous problems. This view allows us to combine techniques from discrete and continuous mathematics to yield faster, more interpretable algorithms.