DAO-ISEM-IORA Seminar Series: Diwakar Gupta
2026-05-20 10:00 - 11:30 (Asia/Singapore)
Name of Speaker

Diwakar Gupta

Schedule

20 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/Wne_hIgXQ-qf4uJZ1Frn5Q

Title

The Impact of List Diving on Post-Transplant Outcomes

Abstract

Transplant programs often turn down deceased-donor kidney offers for their top-ranked potential transplant recipients (PTRs) and utilize them for lower-ranked PTRs. This practice is known as list diving. PTRs in the US are ranked based on an evidence- and consensus-based policy that reflects the collective judgment of stakeholders in the kidney transplant ecosystem. Because list diving upends the consensus ranking, concerns have been raised about its impact on the national system. In this talk, I will present an empirical analysis of the Organ Procurement and Transplantation Network (OPTN) data to quantify the impact of list diving decisions on short- and long-term survival of PTRs after accounting for the endogeneity of such decisions. I will also illustrate the impact of list diving on skipped candidates. The analysis shows that list diving worsens 1 and 3-year survival for transplants performed by centers with significant within-DSA competition. A significant minority of first-ranked skipped candidates do not receive transplants and either die or are removed from waitlist. Their average wait time is 8 months longer. The study highlights the need for greater oversight over transplant programs’ utilization decisions.

About the Speaker

Diwakar Gupta holds an appointment as the INBA–Stuart Centennial Professor of Information, Risk, and Operations Management at the McCombs School of Business of The University of Texas at Austin. His research spans healthcare operations, supply chain finance and risk management, and inventory and revenue management. He serves as the departmental editor of healthcare operations department at the Naval Research Logistics journal and as senior editor of healthcare operations management department at the POMS journal. He teaches healthcare analytics at UT Austin.

DAO-ISEM-IORA Seminar Series: George Shanthikumar
2026-05-22 10:00 - 11:30
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.

DAO-ISEM-IORA Seminar Series: Zi Yang Kang
2026-08-06 10:00 - 11:30
ame of Speaker

Zi Yang Kang

Schedule

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

Venue

BIZ1 0204

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/fs48EzhDRsWzBkChYW84dg

Title

Topping Up and Optimal Redistribution (joint with Mitchell Watt)

Abstract

This paper studies how topping up—allowing recipients of in-kind transfers to supplement subsidized consumption in a private market—affects optimal redistribution. Consumers can access a competitive private market, while a social planner offers an alternative nonlinear price schedule. We show that the effect of topping up depends on the correlation between redistributive priority and demand. When the correlation is positive, topping up does not affect the optimal mechanism. When the correlation is negative, topping up weakens screening and reduces redistribution. At the extensive margin, topping up reduces the set of environments in which intervention is optimal. At the intensive margin, topping up weakly reduces both the scope of a free public option and the mass of consumers served, and shifts redistribution away from the consumers with the highest redistributive priority. We characterize the optimal mechanisms and show how topping up changes the comparative statics of optimal redistribution with respect to redistributive priorities.

About the Speaker

I am an Assistant Professor of Economics at the University of Toronto. I work on mechanism design, market design, and industrial organization. My research applies a theoretical lens to policy design and evaluation. I am particularly interested in “partial mechanism design,” where a policymaker can design only part of the market.

DAO-ISEM-IORA Seminar Series: Safak Yucel
2026-08-12 10:00 - 11:30
Name of Speaker

Safak Yucel

Schedule

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

Venue

BIZ2-413C

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/XSWqNnAUScaE9tpYkW5BhQ

Title

Delivery Terms for Voluntary Carbon Offsets

Abstract

In addition to abating their emissions, companies purchase carbon offsets for voluntary decarbonization targets, such as reaching net-zero emissions. A carbon offset represents emissions reduction achieved by a developer through investments in projects, such as forestry and renewable energy. Companies purchase offsets under two delivery terms. The first is forward delivery, where the buyer orders a certain quantity before sellers invest and yield uncertainty realizes. The second is prompt delivery, where sellers invest and uncertainty realizes, before the buyer orders offsets. Motivated by the importance of choosing the right delivery term for buyers and developers, in this paper, we investigate economic and environmental implications of these delivery terms. Our results offer several managerial insights: A buyer should prefer forward delivery if investment cost is low, but prompt delivery otherwise. Interestingly, the delivery term that leads to a lower cost for the buyer may actually require higher abatement. Second, developers should prefer forward delivery for low-cost projects as well. One would expect that the delivery term that enables developers to make higher investment results in higher profit. However, we show that prompt delivery may lead to higher profit despite lower investment. Finally, environmental groups should promote forward delivery only for a project with a low investment cost. We find that the delivery term that leads to a higher abatement by the buyer may not result in a greater reduction in emissions. This then suggests that, contrary to the common practice, focusing only on the buyer’s abatement may be misguided, and environmental groups should instead also take developers’ investment into account in determining which delivery term to promote

About the Speaker

Safak Yucel is an associate professor of Operations Management (with tenure) at the McDonough School of Business, Georgetown University. Prof. Yucel is also the associate director of the Business of Sustainability Initiative. His main research interests are in sustainable operations, with a focus on renewable energy. He is also interested in the economic and environmental implications of new business models. Prof. Yucel’s research has appeared in leading journals, including Management Science and Manufacturing and Service Operations Management. He has worked at the National Renewable Energy Laboratory of the U.S. Department of Energy, prior to receiving his Ph.D. in Operations Management from Duke University’s Fuqua School of Business.

DAO-ISEM-IORA Seminar Series: Xiao Lei
2026-08-21 10:00 - 11:30
Name of Speaker

Xiao Lei

Schedule

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

Venue

HSS-405

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/P408Y8QvSXaLCSZdXPSrtw

Title

Adaptive Design for In-App Advertising Games: A Data-Driven Methodology Validated with Field Experiments

Abstract

Rewarded ads, in which players voluntarily watch videos in exchange for in-game benefits, have become an important monetization format in both mobile gaming and digital advertising. Yet they create an operational tension: designs that increase immediate ad usage may also frustrate players and reduce future retention. We study this intertemporal trade-off in level-based puzzle games and develop a data-driven framework to maximize players’ expected lifetime rewarded-ad views through dynamic difficulty control. The problem is challenging because firms observe only limited early behavior, while player response exhibits heterogeneity in skill, ad tolerance, and churn propensity, as well as path dependence driven by difficulty reference and prior ad exposure. We formulate the firm’s decision problem as a partially observable Markov decision process with latent player types and instantiate it with a parsimonious behavioral model of win probability, ad watching, and churn. For estimation, we exploit the model structure to develop a multi-step procedure based on recursive logistic regressions. For deployment, we compute policies offline using partially observable Monte Carlo planning and compile them into compact lookup tables that satisfy sub-second latency requirements in live game systems. In collaboration with Tencent, China’s largest digital advertising network, we implement the framework in commercial games and evaluate it through two large-scale randomized field experiments. Relative to the industry baseline, the deployed policy increases long-run rewarded-ad views by 19.23% and 13.58%, while also improving engagement and retention. These results show how dynamic, data-driven experience design can serve as an implementable revenue-management lever in ad-supported digital platforms.

About the Speaker

Xiao Lei is an Assistant Professor at HKU Business School. He earned his Ph.D. in Operations Research from Columbia University. His research focuses on digital platform design, exploring how platforms can both engage and protect their users. His work has been recognized with several prestigious awards, including the Revenue Management and Pricing (RMP) Section Prize, an honorable mention for the George Dantzig Dissertation Award, and Best Paper Awards from the INFORMS Service Science and Behavioral Operations Management societies. Beyond academia, his research drives real-world impact in collaboration with industry leaders such as Tencent and Activision.

DAO-ISEM-IORA Seminar Series: Maxim Afanasyev
2026-08-26 10:00 - 11:30
Name of Speaker

Maxim Afanasyev

Schedule

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

Venue

HSS-405

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/gNT75qF8RbeM66kRmp-lgA

Title

The Consumer Strikes Back: How Agentic AI Rewrites Market Competition

Abstract

Traditional economics often models firms as strategic actors and consumers as relatively passive responders. Agentic AI may reverse this asymmetry. Unlike many earlier enterprise technologies, agentic AI has been adopted rapidly by consumers, while firms remain constrained by organizational inertia, legacy systems, compliance requirements, and slow process redesign. This creates a setting in which consumers may become strategically more sophisticated than the enterprises serving them.

We develop a model of this consumer first mover advantage and study how agentic AI increases consumers’ ability to self serve, search, negotiate, substitute, and strategically bypass firm controlled processes. The model shows how this shift can reduce demand for some services and alter consumer behavior in ways that differ sharply from the pre agentic AI environment. These effects are particularly relevant in industries such as legal services, wealth advisory, education technology, e commerce, banking, and insurance.

The framework also helps explain several counterintuitive industry observations: AI may shift demand from services toward physical goods, create new financial stability risks, and reduce rather than increase consumers’ available leisure time. The results suggest that firms should move beyond viewing AI primarily as a productivity tool and instead treat agentic AI as a source of market structure change, demand disruption, and strategic consumer empowerment.

About the Speaker

Senior Executive in AI, Solutions and Product Management, with 20+ years of experience and track record in academic research, management consulting, technology and financial services. Advisor to C-levels of major companies and prominent entrepreneurs on Applied AI, Industry trends and emerging technologies.
Joined DAO Team as Adj Assoc Prof in June, in addition to current role at Google Cloud, where provide partners, sales and engineering teams with insights on industry needs to ensure Google Cloud brings novel AI capabilities which are relevant to its clients, and monitor adoption and benefits from these capabilities in Asia Pacific and Japan. Before Google, have been leading large global AI engineering and transformation teams (200+ people and budgets of US$100M+) to build and deploy innovative AI solutions, deliver AI adoption strategies and unlock business value with new technologies across industries, both as an employee and as a consultant. Managed multibillion public and private equity technology investments portfolios before joining McKinsey to establish its AI Transformation capabilities. Stanford University PhD in Operations, Information and Technology. Started career as AI Researcher and worked at leadership roles for major companies in Asia, Europe and North America.
At NUS, Maxim’s research focuses on AI economics, Human-AI and AI Transformations. Maxim is teaching Applied AI use case factory (BSM5213) for Master and MBA students and various Executive Education courses on Applied AI Transformation. Maxim is co-author of “Designing the AI Ready Organization” book which is contracted to be published in 1H 2027. Maxim in his NUS capacity is one of two key contributors named at SAFR, Agentic Governance framework, released by Monetary Authority of Singapore in July 2026.
