IORA - Institute of Operations Research and Analytics

DAO-ISEM-IORA Seminar Series: George Shanthikumar
2026-05-22 10:00 - 11:30 (Asia/Singapore)
Name of Speaker

George Shanthikumar

Schedule

22 May 2026, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

BIZ1-0302

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/nhLjyspHTpuCwf5OfrR7rg

Title

Full Space (FS) Relative Valuations, Risk Measures and Valuations of Risky Prospects

Abstract

This paper proposes a unified mathematical infrastructure for the valuation of risky prospects:the Full-Space (FS) Relative Valuation framework. This framework generalizes and synthesizes disparate valuation paradigms in economics, finance, and insurance into a single, coherent structure. We demonstrate that the FS framework is both general and tractable. Its core representation is characterized by necessary and sufficient conditions (i.e., modularity on the lattice of quantile or distribution functions) and we show it can be generalized to represent any valuation functional as an infimum over FS-type kernels. The framework also possesses desirable theoretical properties (e.g., monotonicity, concave order preservation) and admits practical representations, including certainty-equivalent construction and a computationally appealing Fenchel-type supremum form. By integrating these features, FS valuations offer a powerful and versatile foundation for modeling and optimization across disciplines.

About the Speaker

Professor Shanthikumar joined the Krannert faculty in 2009. Prior to coming to Purdue, he was a Chancellor’s Professor of Industrial Engineering and Operations Research at the University of California, Berkeley. His research interests are in integrated interdisciplinary decision making, model uncertainty and learning, production systems modeling and analysis, queueing theory, reliability, scheduling, semiconductor yield management, simulation stochastic processes, and sustainable supply chain management. He has written or co-written more than 250 papers on these topics. He is a co-author (with John A. Buzacott) of the book Stochastic Models of Manufacturing Systems and a co-author (with Moshe Shaked) of the books Stochastic Orders and Their Applications and Stochastic Orders.
He was a co-editor of Flexible Services & Manufacturing Journal and is (or was) a member of the editorial boards of the Asia-Pacific Journal of Operations Research, IEEE Transactions on Automation Sciences and Engineering, IIE Transactions, International Journal of Flexible Management Systems, Journal of Discrete Event Dynamic Systems, Journal of the Production and Operations Management Society, Operations Research, Operations Research Letters, OPSEARCH, Probability in the Engineering and Information Sciences, and Queueing Systems: Theory and Applications.
Professor Shanthikumar has extensively consulted for various companies, including Applied Materials (AMAT), Bellcore, IBM, KLA-Tencor, NTT (Japan), Intel, Intermolecular, ReelSolar, Safeway, and Southern Pacific. Through KLA-Tencor, he has worked on joint development projects for Advanced Micro Devices, IBM, Intel, LSI, Motorola, Texas Instruments, Toshiba, Fujitsu, Taiwan Semiconductor Manufacturing Company, and UMC.

DAO-ISEM-IORA Seminar Series: Jiheng Zhang
2026-06-04 10:00 - 11:30
Name of Speaker

Jiheng Zhang

Schedule

4 Jun 2026, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

BIZ1-0302

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/wv9WK1alQ6SrTblAvPtglw

Title

AI for OR and OR for AI

Abstract

This talk explores the interplay between Operations Research (OR) and Large Language Models (LLMs) through two complementary research directions.

In the first part, we study the scheduling of LLM inference workloads across large GPU clusters. LLM inference involves two phases: a compute-intensive prefill phase that processes user input, and a memory-bound decode phase that generates output tokens. When these phases share GPU resources, prefill tasks throttle concurrent decodes, creating state-dependent contention that is further complicated by workload heterogeneity across applications. We formulate this as a multiclass many-server queueing network with state-dependent service rates, grounded in empirical iteration-time measurements. We analyze the fluid approximation and solve steady-state linear programs that characterize optimal resource allocation. We design gate-and-route policies that regulate prefill admission and decode routing, and prove their asymptotic optimality in the many-GPU limit. We further extend the framework to incorporate service level indicators such as latency and fairness. Numerical experiments demonstrate that our policies outperform standard serving heuristics.

In the second part, we consider the reverse direction: using LLMs to automate OR analysis. Formulating optimization models from natural language and generating executable solver code could reduce reliance on scarce expert knowledge, but LLMs suffer from probabilistic inconsistency and existing methods face a data efficiency dilemma. We propose OR-R1, a framework that integrates supervised fine-tuning with Test-Time Group Relative Policy Optimization (TGRPO). TGRPO extracts reliable training signals from unlabeled data by generating multiple candidate solutions and treating the solver-verified consensus as the ground truth. We provide theoretical guarantees for gradient convergence and show that this voting-based proxy consistently maximizes true solution accuracy. OR-R1 requires only 10% of the training data used by prior methods, yet achieves state-of-the-art accuracy across eight OR benchmarks.

About the Speaker

Jiheng Zhang is a Professor in the Department of Industrial Engineering and Decision Analytics at HKUST, where he also holds a joint appointment in the Department of Mathematics. His research interests include stochastic modeling and optimization, statistical learning, numerical methods, and algorithms, with applications in operations management, large communication networks, and financial technology. He serves as an associate editor for several top journals, including Operations Research, Stochastic Systems, and Probability in the Engineering and Informational Sciences. Since 2018, he has been the Director of the EPI-One Lab, leading various applied projects with industry partners such as Huawei and Webank. He holds several patents in areas such as large-scale production planning and blockchain consensus mechanism design. He earned his Ph.D. in Operations Research from the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology in 2009. He also holds an M.S. in Mathematics from Ohio State University and a B.S. in Mathematics from Nanjing University.
