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Sungjin Im

Associate Professor, Computer Science & Engineering, UC Santa Cruz; learning-augmented algorithms researcher
University of California, Santa Cruz · Merced, California, United States · academic

Sungjin Im is an associate professor in the Department of Computer Science and Engineering at the University of California, Santa Cruz, where he moved in 2025 after more than a decade at UC Merced. He earned his BS and MS in computer science from Seoul National University and his PhD in computer science from the University of Illinois at Urbana-Champaign, advised by Chandra Chekuri, followed by a postdoctoral position at Duke University. His research centers on the design and analysis of algorithms, particularly learning-augmented algorithms that use machine-learning predictions to beat worst-case bounds, and on scheduling and resource allocation. He also serves as a Consulting Research Scientist at RelationalAI. As a high-school student he won a silver medal representing the Republic of Korea at the 1996 International Mathematical Olympiad.

LinkedIn connections125
Details
LocationMerced, California, United States
Company siteucsc.edu
UniversityUniversity of Illinois at Urbana-Champaign
Approximation algorithmsOnline algorithmsCombinatorial optimizationSchedulingAlgorithm design and analysisLearning-augmented algorithmsScheduling and resource allocationDatabase theoryMassively parallel algorithmsAlgorithmsTheory of computingMachine learning augmented algorithms
Notes
  • Moved from UC Merced to UC Santa Cruz in 2025; his Merced homepage now names UCSC as his institution, so his primary affiliation is UCSC.2025
  • Won an NSF CAREER award in 2019 for scheduling/resource-allocation algorithm research while at UC Merced, signaling early-career standing in the algorithms community.May 6, 2019
  • Has been a Consulting Research Scientist at RelationalAI since February 2019, an industry tie that connects his scheduling/optimization work to enterprise decision-intelligence products.Feb 2019
  • Silver medal at IMO 1996 for Republic of Korea (team score 94, 78.1%); the mathematics foundation that led him into theoretical computer science research.
  • Current research pivots on learning-augmented / algorithms-with-predictions (e.g. online scheduling via gradient descent, learned BSTs, faster matching via learned duals), a deliberately forward-leaning niche versus classical worst-case scheduling.
  • Earned PhD at UIUC under Chandra Chekuri, then a postdoc at Duke with Kamesh Munagala, then joined UC Merced in 2014 -- a theory-community lineage that explains his approximation/online-algorithms focus.
  • Google Scholar: 2286 citations, h-index 26, i10-index 59; verified email at ucsc.edu, confirming the UCSC affiliation.
  • Publicly states he is not taking a PhD student for Fall 2026, useful for anyone considering reaching out about doctoral openings.2026
  • Held research internships at IBM T. J. Watson and Microsoft Research Asia during doctoral studies, a common path into industrial research labs.
  • Uses the publication alias 'S. Im' and maintains ORCID 0000-0001-5994-7280.

Competition record

Republic of Korea · IMO

1996 · Silver · Rank 94