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  • SIGMETRICS is the flagship conference of the SIGMETRICS community.
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Yige Hong 2026 SIGMETRICS Doctoral Dissertation Award

Portrait of Dr. Yige Hong ACM SIGMETRICS is pleased to announce the selection of Dr. Yige Hong as the recipient of the 2026 SIGMETRICS Doctoral Dissertation Award. This award recognizes the outstanding contributions of his doctoral thesis to developing new algorithms and techniques for large stochastic systems, including stochastic bin packing with departures, restless bandits, weakly-coupled Markov decision processes, and multiserver queues.

Yige Hong is the Ronald J. and Carol T. Beerman Postdoctoral Fellow at Georgia Tech's School of Industrial and Systems Engineering. He will join the School of Science and Engineering at The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen) as an assistant professor in summer 2027. He earned his Ph.D. in Computer Science at Carnegie Mellon University, advised by Professor Weina Wang, and his B.S. in Applied Mathematics at CUHK-Shenzhen. His honors include the IFIP Performance 2023 Best Paper Award, the Stochastic Networks Conference 2026 Outstanding Student Poster Award, and spotlight posters at NeurIPS and ICML.

Yige's thesis, Structural Methods for Large Stochastic Systems, studies large stochastic systems by identifying shared structures and developing techniques adapted to each structure. It advances understanding of basic models, studies new models motivated by emerging applications including data center scheduling, and develops mathematical tools for performance analysis and policy design.

Part I studies the weak-coupling structure in stochastic bin packing, restless bandits, and weakly coupled Markov decision processes. It develops efficiently computable policies that are provably near-optimal at scale under weaker, more easily verifiable conditions than previously required.

Part II studies the one-dimensional structure in multiserver queues. For G/G/n queues under First-Come-First-Served (FCFS), it proves universal bounds on mean queue length. For G/G/n queues with setup times, it establishes heavy-traffic optimality of Gittins policy. For multiserver jobs, it characterizes mean waiting time under FCFS and develops an order-wise optimal policy under large-system scaling.