Traditional algorithmic research has mainly been focused on worst-case guarantees. Although it has discovered numerous algorithmic ideas of fundamental importance, unfortunately the resulting algorithms don't often perform the best in practice. This is because they are designed to handle all instances equally, including pathological ones that are hardly observed. To bridge this gap, there have been strong efforts to go beyond worst-case algorithmics. Machine learning augmented algorithms is a fascinating and emerging line of work in the beyond-worst-case algorithmics aiming to strengthen traditional algorithms with machine-learned predictions. In this talk, I will give a high-level overview of ML augmented algorithms and present some of my recent works in the area.

Sungjin Im is an associate professor at the University of California, Merced, and is also a consulting research scientist for RelationalAI. He obtained his BS and MS in computer science and engineering at Seoul National University. He received his PhD in computer science from the University of Illinois at Urbana-Champaign in 2012. His PhD studies were mainly supported by a Samsung fellowship. He was a co-winner of the Best Student Paper Award at the ACM-SIAM Symposium on Discrete Algorithms (SODA), 2010. He is interested in the design and analysis of algorithms and their applications. He has worked on various discrete optimization problems, with an emphasis on resource allocation and scheduling problems. His research encompasses approximation algorithms, online algorithms, scheduling algorithms, combinatorial optimization, game theory, big data algorithms, and learning theory. His research has been supported by NSF and ONR grants, including an NSF Career Award.