Sungjin Im is an associate professor in the Department of Computer Science and Engineering at the University of California, Santa Cruz. He earned his BS and MS in computer science at Seoul National University and his PhD in computer science from the University of Illinois at Urbana-Champaign. Following a postdoctoral position at Duke University, he joined UC Merced in 2014. He joined UC Santa Cruz in 2025. 

His area of expertise lies in the design and analysis of algorithms, along with their various applications. His current research focuses on two main areas. Firstly, he is interested in strengthening traditional worst-case algorithms by incorporating machine learning predictions. This involves exploring how machine learning can improve the performance of algorithms in situations where the worst-case scenario might not be the most common. Secondly, he is passionate about developing meta-algorithms that can be easily adapted to solve a wide range of problems. This research has a particular emphasis on scheduling and resource allocation. In addition to these current interests, he also maintains a strong interest in database theory and the development of big data algorithms, particularly those that leverage massively parallel processing. Overall, he is driven by a passion for tackling fundamental algorithmic problems. His research encompasses a broad spectrum of areas, including approximation algorithms, online algorithms, combinatorial optimization, scheduling algorithms, game theory, machine learning, and database theory.