IORA - Institute of Operations Research and Analytics

DAO-ISEM-IORA Seminar Series: Paat Rusmevichientong
2025-04-04 10:00 - 11:30 (Asia/Singapore)
Name of Speaker
Paat Rusmevichientong

Schedule
4 April 2025, 10am – 11.30am

Venue
BIZ1 03-02

Link to Register
https://nus-sg.zoom.us/meeting/register/Lapg-26pTU620yd5j7DDAw

Title
An Endogenous Multinomial Logit Model with Population-Based Purchase Feedback

Abstract
We develop a choice model that incorporates feedback from a population of customers making a purchase within the assortment of offered products. Each customer posts feedback on the product that she purchases within an offered product assortment. The feedback is obtained by applying a feedback function to the utility that the customer derives from her purchased product. When another customer is to make a purchase within the assortment, she uses the expected feedback from the customers with purchases to form a reference point. Therefore, the utility of a product has three components: intrinsic deterministic utility, Gumbel distributed idiosyncratic term and reference effect. The utility of a product is determined through a fixed point, as the utility depends on the reference effect, which, in turn, depends on the utilities of the products in the assortment. We give a closed-form expression for the choice probabilities for a broad class of feedback functions that can even have discontinuities. We develop an efficient algorithm to compute the revenue-maximizing assortment when the customers choose under our choice model. We study the pricing problem under our choice model, giving structural properties of the optimal prices, which ultimately yield an efficient algorithm for computing the revenue-maximizing prices. We also provide necessary and sufficient conditions for the convexity of the negative log-likelihood function. Our computational experiments demonstrate that our choice model can help predict the choices of the customers better than a number of benchmarks.

About the Speaker
Paat Rusmevichientong is the Justin Dart Professor of Operations Management and Professor of Data Sciences and Operations in the Marshall School of Business at the University of Southern California. Prior to joining the Marshall School, he was a faculty in the School of Operations Research and Information Engineering at Cornell University. His research interests focus on revenue management, choice modeling, pricing, assortment optimization, and large-scale dynamic programming. From 2003 through 2004, he worked in the data mining and personalization group at Amazon.com. He received BA (1997) in Mathematics from University of California, Berkeley, and MS (1999) and PhD (2003) in Operations Research from Stanford University. He is a member of INFORMS.

DAO-ISEM-IORA Seminar Series: Bernhard von Stengel
2025-04-23 10:00 - 11:30
Name of Speaker
Bernhard von Stengel

Schedule
23 April 2025, 10am – 11.30am

Venue
BIZ1 03-02

Link to Register
https://nus-sg.zoom.us/meeting/register/AWc8AlsiQo67S7-kKnLwmQ

Title
Finding an Empirical Equilibrium by Machine Learning in a Pricing Game

Abstract
We apply machine learning to a classical multi-period pricing game. The game is a duopoly with demand inertia, introduced by Selten in 1965 and studied around 1990 with economic experiments. Its strategies are too complex to represent explicitly. Unlike standard multi-agent reinforcement learning of training agents against each other, our framework is that of a Policy-Space Response Oracle (PSRO) and the “double oracle” method. A few initial strategies that play against each other define a “meta-game”. A Nash equilibrium of this meta-game is computed and defines the training environment for a learning agent. Once the agent is sufficiently trained to get a higher payoff than in the current equilibrium, it is added as a new strategy to the meta-game, which expands in this way, and the process repeats. Various learning methods such as SAC (Soft Actor Critic) and PPO (Proximal Policy Optimisation) lead to different limit equilibria in this setup, namely either competitive pricing or cooperative price collusion in the duopoly.
(Joint work with Sahar Jahani and Rahul Savani)

About the Speaker
Bernhard von Stengel is Professor of Mathematics at the London School of Economics which he joined in 1998, after studies in Germany and the USA. He has been communications officer of the Game Theory Society, and was program chair of the GAMES 2016 congress (held jointly with EC’2016) in Maastricht. He is interested in mathematical questions of game theory. The geometry and computation of Nash equilibria is one of his research specialities.

DAO-ISEM-IORA Seminar Series: Erick Delage
2025-05-07 10:00 - 11:30
Name of Speaker
Erick Delage

Schedule
7 May 2025, 10am – 11.30am

Venue
BIZ1 03-02

Link to Register
https://nus-sg.zoom.us/meeting/register/GTx5hsDMTAep_DVlLOLRAA

Title
Data-driven Conditional Robust Optimization

Abstract
Conditional Robust Optimization (CRO) is a decision-making framework that blends the flexibility of robust optimization (RO) with the ability to incorporate additional information regarding the structure of uncertainty. This approach solves the RO problem where the uncertainty set structure adapts to account for the most recent information provided by a set of covariates. In this presentation, we will introduce two data-driven approaches to CRO: a sequential predict-then-optimize method and an integrated end-to-end method. We will also show how hypothesis testing can be integrated to the training in order to improve the quality of conditional coverage of the produced uncertainty sets.

About the Speaker
Erick Delage is a professor in the Department of Decision Sciences at HEC Montréal, a chairholder of the Canada Research Chair in decision making under uncertainty, and a member of the College of New Scholars, Artists and Scientists of the Royal Society of Canada. His research interests span the areas of robust and stochastic optimization, decision analysis, reinforcement learning, and risk management with applications to portfolio optimization, inventory management, energy, and transportation problems.

DAO-ISEM-IORA Seminar Series : Daniel Russo
2025-08-15 10:00 - 11:30
Name of speaker

Daniel Russo

Schedule

15 August 2025, 10am – 11.30am

Venue

BIZ2 – 0413C

Link to register

https://nus-sg.zoom.us/meeting/register/rT3lbgWGQB-LnXjhOw84MA

Title

Active Exploration via Autoregressive Generation of Missing Data

Abstract

We cast the challenges of uncertainty quantification and exploration in online decision-making as a problem of training and generation from an autoregressive sequence model, an area experiencing rapid innovation. Central to our approach is viewing uncertainty as arising from missing outcomes that would be revealed through appropriate action choices, rather than from unobservable latent parameters of the environment. This reformulation aligns naturally with modern machine learning capabilities: we can i) train generative models through next-token prediction rather than fit explicit priors, ii) assess uncertainty through autoregressive generation rather than parameter sampling, and iii) adapt to new information through in-context learning rather than explicit posterior updating. To showcase these ideas, we formulate a challenging informed bandit learning task where effective performance requires leveraging unstructured prior information (like text features) while exploring judiciously to resolve key remaining uncertainties. We validate our approach through both theory and experiments. Our theory establishes a reduction, showing success at offline next-outcome prediction translates to reliable online uncertainty quantification and decision-making, even with strategically collected data. Semi-synthetic experiments show our insights bear out in a news-article recommendation task where article text can be leveraged to minimize exploration.

About the Speaker

Daniel Russo is a Philip H. Geier Jr. Associate Professor in the Decision, Risk, and Operations division of the Columbia Business School. His research lies at the intersection of machine learning and online decision making, mostly falling under the broad umbrella of reinforcement learning. Outside academia, Dan works as an Amazon scholar applying reinforcement learning to supply chain optimization. He previously spent five years working with Spotify to apply reinforcement learning and large language models to audio recommendations. Dan completed his undergraduate studies in Math and Economics at the University of Michigan, doctoral studies at Stanford University under the supervision of Benjamin Van Roy, and worked as a postdoctoral researcher at Microsoft Research in New England. His research has been recognized by the Erlang Prize, the Frederick W. Lanchester Prize, a Junior Faculty Interest Group Best Paper Award, and first place in the George Nicholson Student Paper Competition.

DAO-ISEM-IORA Seminar Series : Alminas Žaldokas
2025-08-22 10:00 - 11:30
Name of Speaker

Alminas Žaldokas

Schedule

22 August 2025, 10am – 11.30am

Venue

HSS 4 – 7 (Hon Sui Sen Memorial Library, level 4 Seminar Room)

Link to Register

https://nus-sg.zoom.us/meeting/register/zUMnlP9MRy2HoqduBcxjmA

Title

ESG Shocks in Global Supply Chains

Abstract

We show that U.S. firms cut imports by 31.8% when their international suppliers experience environmental and social (E&S) incidents. These trade cuts are larger for publicly listed U.S. importers facing high E&S investor pressure and lead to crosscountry supplier reallocation, suggesting that E&S preferences in capital markets can be privately costly but have real effects for foreign suppliers. Larger trade cuts around the incident result in better supplier E&S performance in subsequent years, and in the eventual resumption of trade. Our results highlight the role of investors in ensuring suppliers’ E&S compliance along global supply chains.

About the Speaker

Alminas Žaldokas is currently an Associate Professor in Finance at the National University of Singapore (NUS). Prior to this appointment, Alminas Žaldokas has been teaching at the Hong Kong University of Science and Technology (HKUST) since 2012 with the primary focus on corporate finance and corporate valuation. Apart from the undergraduate and MSc courses, he was teaching in the HKUST-NYU MSc in Global Finance, HKUST bilingual EMBA, and Kellogg-HKUST EMBA programmes. He has also previously taught corporate valuation for the MBAs at the University of Texas in Austin McCombs School of Business in 2017/8 academic year.

Professor Žaldokas received his PhD in Finance at INSEAD in 2012. His previous academic degrees include MSc in Finance and Economics from London School of Economics and BSc in Business and Economics from Stockholm School of Economics in Riga.
Professor Žaldokas’s research focuses on the interaction between firm decisions in the financial and in the product markets. In particular, he studies corporate finance decisions that relate to the firm investment in innovation, the formation of collusive arrangements between firms, and the facilitation of ESG practices. This research has been published in top academic journals such as Journal of Financial Economics, Review of Financial Studies, Journal of Accounting Research, Management Science, RAND Journal of Economics, Journal of International Economics, and Journal of Financial Intermediation.

DAO-ISEM-IORA Seminar Series: Sean Zhou
2025-08-29 10:00 - 11:30
Name of Speaker
Sean Zhou

Schedule
29 August 2025, 10am – 11.30am

Venue
HSS 4 – 7 (Hon Sui Sen Memorial Library, level 4 Seminar Room)

Link to Register

https://nus-sg.zoom.us/meeting/register/HB2NQ5ZjRpuRwFiBsxDOxg

Title
Learning and Pricing for Consumer Electronics Trade-in Program

Abstract
We consider a dynamic pricing problem for a two-sided consumer electronics trade-in program, where a firm acquires and re-sells multiple types of pre-owned (used) products over a finite selling horizon. There are customers trading in their used products for new products at discounted prices and customers buying refurbished products. The firm sets trade-in prices and resale prices to maximize its total expected profit. We first discuss the scenario that the firm knows the choice models of customers. Due to the high-dimensional state space, deriving the optimal policy using dynamic programming is computationally intractable. To circumvent this, we develop simple and provably effective heuristic policies based on the solution to a deterministic upper-bound problem. We design a dynamic policy called the Batched-Adjustment Control (BAC) policy, under which the selling horizon is divided into different consecutive and disjoint batches for different products and the prices in one batch are updated based on the realized uncertainties in the previous batch. The profit loss of BAC relative to the optimal one is in the order of . When the firm does not know the choice model parameters of customers, it has to learn while making pricing decisions over time. We develop an algorithm called Parametric-Batched-Adjustment Control (PBAC), in which the firm first uses Maximum Likelihood Estimation to learn the trade-in and demand models’ parameters, and then adopt a similar pricing policy akin to BAC while using the estimated parameters. With carefully chosen algorithm parameters (e.g., length of exploration phase, batch size), we show that PBAC has a regret in the order of . This is based on joint work with Zhuoluo Zhang (Xiamen University), Murray Lei (Queen’s University), and Wenhao Li (SUFE).

About the Speaker
Sean Zhou is Professor and Chair of Department of Decisions, Operations and Technology, CUHK Business School, and Professor in Department of Systems Engineering and Engineering Management (by courtesy), at The Chinese University of Hong Kong (CUHK). He has held visiting positions at National University of Singapore and University of Toronto. He received his Ph.D. in Operations Research from North Carolina State University. His main research interests are inventory management, pricing, sustainable operations, data-driven supply chain optimization, and operations and marketing interface. He serves as Area Editor (Inventory and Supply Chain Optimization) of OR Letters and Associate Editor of various journals including Naval Research Logistics and Service Science.

DAO-ISEM-IORA Seminar Series: Nur Sunar
2025-09-04 10:00 - 11:30
Name of Speaker
Nur Sunar

Schedule
4 September 2025, 10am – 11.30am

Venue
HSS 3 – 2 (Hon Sui Sen Memorial Library, level 3 Seminar Room)

Link to Register
https://nus-sg.zoom.us/meeting/register/lZ34DiDOTh6rI4zJehKPIQ

Title
Designing Renewable Power Purchase Agreements: Impact on Green Energy Investment

Abstract
This paper studies a long-term power purchase agreement (PPA) between a firm and a new renewable energy generator. The firm must dynamically satisfy uncertain electricity demand beyond its existing energy sources, while wholesale electricity prices evolve stochastically over time. Upon signing a PPA, a new renewable facility becomes operational, and the firm owns its output for the contract duration. The new facility’s capacity is determined based on PPA terms. The firm dynamically chooses when to initiate the PPA and how much to pay to maximize its expected total discounted benefit. We show that the firm’s optimal timing follows a (time-dependent) threshold policy. Our results offer key insights for policymakers and renewable energy developers. We find that, contrary to common wisdom, reducing investment costs for renewable technologies can lead to smaller renewable capacity, output, and emissions savings when projects are developed under PPAs. This finding calls for caution in applying investment tax credits in such contexts. We also show that total renewable energy generation and emissions savings may decrease with higher site productivity. Therefore, restricting renewable facility development to most productive sites might be counterproductive under PPAs. We establish the robustness of our findings across a broad range of practical scenarios.
(joint work with Zuguang Gao and John R. Birge)

About the Speaker
Nur Sunar is an Associate Professor of Operations and Sarah Graham Kenan Scholar at the Kenan-Flagler Business School of the UNC at Chapel Hill. She received her Ph.D. from Stanford Graduate School of Business with a thesis titled “Management Problems in Energy and Sustainability.” Her current research interest is to study innovative business models, technologies, and policies, with a focus on sustainability, energy, and digital platforms. A key theme of her recent research is doing good with management science.
Dr. Sunar is particularly interested in innovative business models and novel challenges related to renewable energy technologies (e.g., rooftop solar panels, large-scale renewable energy technologies, online solar marketplaces), sustainability practices of companies/organizations (e.g., voluntary carbon offsetting) and smart city technologies (e.g., the Internet of Things, smart meters, electric vehicles, and residential batteries). She is also passionate about innovative business solutions for inclusive health.
For more information, please see https://sites.google.com/view/nur-sunar/home

DAO-ISEM-IORA Seminar Series: Victor Martínez de Albéniz
2025-09-05 10:00 - 11:30
Name of Speaker
Victor Martínez de Albéniz

Schedule
5 September 2025, 10am – 11.30am

Venue
HSS 4 – 7 (Hon Sui Sen Memorial Library, level 4 Seminar Room)

Link to Register

https://nus-sg.zoom.us/meeting/register/kZSaUc7SR_GuEKdg2LC_SA

Title
Digital Nudges at the Van Gogh Museum Increase Engagement, Pace Visitors, and Reduce Congestion

Abstract
Digital nudges have the potential to enrich experiential services, but little is known about how they affect behaviors in the field. From 2022 to 2024, we run field experiments at the Van Gogh Museum, testing the effect of interventions on the multimedia tour on visitor content consumption and movements. We find that providing that providing a highlight selection with a simple information architecture can increase consumption, coverage of the collection, without requiring more visit duration, thereby containing museum fatigue. Furthermore, faster visitor flows reduce congestion, creating a positive externality on others. Thus, well-designed digital nudges can produce more effective visits, that improve both consumer and service provider outcomes.

About the Speaker
Victor Martínez de Albéniz is a Full Professor in the Operations, Information and Technology Department at IESE Business School. He joined IESE in 2004 after earning a PhD from the Operations Research Center at the Massachusetts Institute of Technology (MIT) and an engineering degree from École Polytechnique in France.
His research spans a broad spectrum of Operations Management. He began his career working on supply chain management, optimizing inventory and purchasing systems to combine low costs with flexibility and innovation. He then moved into the retail sector, developing fashion trend forecasts, leveraging big data to respond to demand shocks, and optimizing the in-store customer experience. More recently, he has applied his expertise to improving education systems.
For more information, please see https://blog.iese.edu/martinezdealbeniz/

DAO-ISEM-IORA Seminar Series: Chen Ningyuan
2025-09-17 10:00 - 11:30
Name of Speaker

Chen Ningyuan

Schedule

17 September 2025, 10am – 11.30am

Venue

BIZ1 – 0202

Link to Register
(Via Zoom)

https://nus-sg.zoom.us/meeting/register/n1RflnWjRwyd5RSu7W6rag

Title

Post-Estimation Adjustments in Data-Driven Decision-Making with Applications in Pricing

Abstract

The predict-then-optimize (PTO) framework is a standard approach in data-driven decision-making, where a decision-maker first estimates an unknown parameter from historical data and then uses this estimate to solve an optimization problem. While widely used for its simplicity and modularity, PTO can lead to suboptimal decisions because the estimation step does not account for the structure of the downstream optimization problem. We study a class of problems where the objective function, evaluated at the PTO decision, is asymmetric with respect to estimation errors. This asymmetry causes the expected outcome to be systematically degraded by noise in the parameter estimate, as the penalty for underestimation differs from that of overestimation. To address this, we develop a data-driven post-estimation adjustment that improves decision quality while preserving the practicality and modularity of PTO. We show that when the objective function satisfies a particular curvature condition, based on the ratio of its third and second derivatives, the adjustment simplifies to a closed-form expression. This condition holds for a broad range of pricing problems, including those with linear, log-linear, and power-law demand models. Under this condition, we establish theoretical guarantees that our adjustment uniformly and asymptotically outperforms standard PTO, and we precisely characterize the resulting improvement. Additionally, we extend our framework to multi-parameter optimization settings. Numerical pricing experiments demonstrate that our method consistently improves revenue, particularly in small-sample regimes where estimation uncertainty is most pronounced. This makes our approach especially well-suited for pricing new products or in settings with limited historical price variation.

About the Speaker

Dr. Ningyuan Chen is currently an associate professor at the Department of Management at the University of Toronto, Mississauga and at the Rotman School of Management, University of Toronto. Before joining the University of Toronto, he was an assistant professor at the Hong Kong University of Science and Technology. Prior to that, he was a postdoctoral fellow at the Yale School of Management. He received his Ph.D. from the Industrial Engineering and Operations Research (IEOR) department at Columbia University in 2015. His studies have been published in Management Science, Operations Research, Annals of Statistics, NeurIPS and other journals and proceedings. His research is supported by the UGC of Hong Kong and the Discovery Grants Program of Canada. He is the recipient of the Roger Martin Award for Excellence in Research and the IMI Research Award.

DAO-ISEM-IORA Seminar Series: Vinayak Deshpande
2025-11-06 10:00 - 11:30
Name of Speaker

Vinayak Deshpande

Schedule

6 Nov 2025, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

BIZ1 0304

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/MBGAcVExSZ2vrzd3MnrERg

Title

Data driven research for better Operations decisions

Abstract

The explosion in availability of data has enabled organizations to collect wealth of information for their business operations. In this talk, I will share my experience in using a data-driven approach for improving Operations decisions from two settings: Healthcare and Aviation. I will highlight the opportunities and challenges in a data driven research approach for operational decision making.
In the first half of the talk, I will discuss the challenge of improving the efficiency of surgical procedures which account for approximately 60% of the operating cost of a hospital the United States. Hospitals spend several million dollars annually on instrument sterilization, instrument tray assembly, and instrument repurchase costs. However, in a large majority of hospitals, less than 20%–30% of reusable instruments supplied to a surgery are used on average. We obtained actual surgical instrument usage at a large multispecialty hospital in partnership with OpFlow, a healthcare software company. We formulate a data-driven mathematical optimization model for surgical tray configuration and assignment with the goal of reducing costs of unused instruments, such as sterilization, instrument purchase, and tray assembly costs. Our solution was implemented at the UNC Rex Hospital, and we report on the results of our implementation. This analysis has quantified the value of collecting point-of-usage data to be at least $1.39 million per year from using the model-recommended solution at the hospital.
In the second half of the talk, I will discuss the challenge of flight delays in the aviation sector which impacts airlines’ operating cost including increased expenses for crew, fuel, and maintenance. Propagated delays due to late arriving aircraft contribute to 40% of all flight delays as reported by the Bureau of Transportation Statistics. The aircraft assignment problem is to assign tail numbers on scheduled arriving flights at an airport to scheduled departing flights at the same airport with the objective of minimizing propagated delays. In this paper, we propose a new data-driven approach for the aircraft assignment problem by formulating it as a balanced assignment problem between incoming and outgoing flights flown by the same aircraft type at the major hub airports. We propose a data-driven clustering method to account for factors such as the originating airport, time of day, and aircraft type that affect the primary delay distribution. These empirical cluster-based aircraft assignment costs serve as an input to our stochastic assignment model. These assignment costs are then used to derive the optimal stochastic aircraft assignment for an out-of-sample data set for Delta Airlines at its three largest hub airports. We show that the stochastic assignment derived from the data-driven approach performs 2.31% better than the benchmark FIFO assignment in total propagated delay at these hub airports.

About the Speaker

Professor Deshpande is the Mann Family Distinguished Professor of Operations at the Kenan-Flagler business school at University of North Carolina. He holds a Ph.D. in Operations Management from the Wharton School, University of Pennsylvania. He also holds a M.S. in Operations Research from Columbia University, New York, and a B.Tech. in Mechanical Engineering from I.I.T., Mumbai.

Prof. Deshpande was awarded with the Dantzig Dissertation award for his Ph.D. dissertation for his work with the US Navy and DLA in optimizing the weapon systems spare parts supply chain. He has worked with the US Coast Guard on a series of projects for optimizing the supply chain used for aircraft service parts. His work with the US Coast Guard was selected as a finalist for the Edelman award and he was honored as an Edelman Award Laureate for an outstanding example of management science and operations research practice. His work on airline operations has been honored with the AGIFORS best contribution award by the Airline Operations Research Society AGIFORS. His research using data from Alibaba’s Cainiao network and JD.com on e-commerce logistics received the MSOM data driven research challenge finalist award. His recent work on surgical tray optimization was selected as a finalist for the Innovative Applications of Analytics Award by the INFORMS society.

His research interests are in the area of Supply Chain Management, E-commerce logistics, Service/Spare Parts Management, Inventory Management, Sustainable Operations, and Healthcare Operations. His research has been motivated by contexts from various industry sectors such as defense, aviation, hi-tech, retail, e-commerce, airlines, and healthcare. His research has been published in premier academic journals such as Management Science, Operations Research, POMS, and M&SOM. He recently served as the president of the supply chain college of the Production and Operations Management society.

DAO-ISEM-IORA Seminar Series: Guillaume Roels
2025-11-14 10:00 - 11:30
Name of Speaker

Guillaume Roels

Schedule

14 Nov 2025, 10am – 11.30am
(60 min talk + 30 min Q&A)

Venue

HSS 4-1

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/SWlclpbGT6eV8q9w_dkijA

Title

You, Me, or We? Co-Productive Principal-Agent Dynamics

Abstract

Problem Definition: Projects are often initiated by a single person – a principal – who then decides whether to execute it on their own (Single Execution) or to partner with someone else – an agent. If an agent is hired, the project execution may be joint (Joint Execution) or undertaken only by the agent (Delegated Execution). How do the resulting co-productive dynamics compare to what would be optimal to do?
Methodology/Results: We consider a co-productive principal-agent model with endogenous team formation. With financial transfers, as is typical in inter-organizational contexts, joint execution happens less frequently than optimally. Moreover, the optimal contract under joint execution turns out to be a 50%-50% equity split under mild conditions. When financial transfers are not allowed, as is typical in intra-organizational contexts, there might be too much joint execution and too little delegation if the agent is very efficient. Overall, the inefficiency created by moral hazard under delegated and joint execution is much less important than that created by the principal’s ability to engage in project hoarding and not form a team.
Managerial Implications: The problem of under-delegation should really be framed as a problem of project hoarding, i.e., principals do not partner enough. Moreover, it is only relevant within – and not between – organizations. When principals partner with an agent, they might delegate too much if agents are inefficient or contribute too much if agents are efficient. Although the principal-agent and team-production literature have essentially focused on setting incentives for effort exertion within a given operating mode, a more critical issue appears to induce principals to form a team (or not), leading to inefficient operating modes.

About the Speaker

Guillaume Roels is the Timken Chaired Professor of Global Technology and Innovation at INSEAD. His research lies on the interface of operational excellence, people-centric operations, and the management of services. Recent work has focused on collaborative dynamics in organizations, the design of service experiences, and customer ownership in service systems. Prior to joining INSEAD, Guillaume was an Associate Professor at the UCLA Anderson School of Management. He received an MS degree in Management Engineering and a DEA in Management from the Catholic University of Louvain, Belgium, and a PhD in Operations Research from MIT.
He is currently serving as the Editor-in-Chief of Service Science, an INFORMS journal was a Department Editor at M&SOM. He also served as the President of the M&SOM Technology, Innovation, and Entrepreneurship (TIE) Specific Interest Group (SIG) and the President of the M&SOM Service Management (SIG). Recent research awards include a finalist position on the 2023 POMS College of Service Operations Management Best Student Paper Competition, a finalist position in the 2023 INFORMS Social Media Analytics Best Student Paper Competition, and a second place in the 2023 INFORMS Service Science Cluster Best Paper Competition.

DAO-ISEM-IORA Seminar Series: Seungki Min
2026-01-30 10:00 - 11:30
Name of Speaker

Seungki Min

Schedule

30 Jan 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/psLI6qAmQPyKNZ3DOtqRvw

Title

An Information-Theoretic Analysis of Nonstationary Bandit Learning

Abstract

In many real-world bandit learning problems, the underlying environment evolves over time, requiring decision-makers to continually acquire information and adapt their action selection accordingly. In this talk, I study Bayesian formulations of nonstationary bandit problems, where environmental dynamics are modeled as stochastic processes, and develop an information-theoretic framework for analyzing attainable performance.
Our analysis yields generic regret upper bounds that extend classical results from stationary Bayesian bandits to nonstationary settings. A key insight is that the entropy rate of the optimal action process naturally quantifies the intrinsic difficulty introduced by nonstationarity. I further connect our results to existing frequentist analyses of nonstationary bandits, showing that several well-known regret bounds in the literature can be recovered as special cases within our unified framework.

About the Speaker

Seungki Min is an Assistant Professor of Operations Management at Seoul National University Business School. His research focuses on bandit optimization and reinforcement learning, with an emphasis on principled frameworks for dynamic decision problems arising in business and engineering applications, including online platforms, pricing, and finance. His research has appeared in Operations Research, Management Science, and leading AI/ML conferences such as ICML and NeurIPS. He earned his Ph.D. from Columbia Business School. Prior to academia, he worked in high-frequency trading domain.

DAO-ISEM-IORA Seminar Series: Francis de Véricourt
2026-02-06 10:00 - 11:30
Name of Speaker

Francis de Véricourt

Schedule

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

Venue

HSS 4-2

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/KcwXsVRZSI2rLe4DvkXNFQ

Title

Beyond the Black Box: Unraveling the Role of Explainability in Human-AI Collaboration

Abstract

Explainable Artificial Intelligence (AI) models have been proposed to mitigate overreliance and underreliance on AI, which reduce the effectiveness of human-AI collaborative tools. Yet, empirical evidence is mixed, and the impact of explainable AI on a decision-maker (DM)’s cognitive load and fatigue is often ignored. This paper offers a theoretical perspective on these issues. We develop an analytical model that incorporates the defining features of human and machine intelligence, capturing the limited but flexible nature of human cognition with imperfect machine recommendations. Crucially, we represent how AI-based explanations influence the DM’s belief in the algorithm’s predictive quality. Our results indicate that explainable AI has varying effects depending on the level of explainability it provides. While low explainability levels have no impact on decision accuracy and reliance levels, they lessen the cognitive burden of the DM. In contrast, higher explainability levels enhance accuracy by improving overreliance but at the expense of increased underreliance. Further, the relative impact of explainability (c.f. a black-box system) is higher when the DM is more cognitively constrained, the decision task is sufficiently complex or when the stakes are lower. Importantly, higher explainability levels can escalate the DM’s cognitive burden and hence overall processing time and fatigue, precisely when explanations are most needed, i.e. when the DM is pressed for time to complete a complex task and doubts the machine’s quality. Our study elicits comprehensive effects of explainability on decision outcomes and cognitive effort, enhancing our understanding of designing effective human-AI systems in diverse decision-making environments.

About the Speaker

Francis de Véricourt is Professor of Management Science and the founding Academic Director of the Institute for Deep Tech Innovation (DEEP) at ESMT Berlin. He also holds the Joachim Faber Chair in Business and Technology, and is the co-author of Framers, a Penguin Random House book listed on Financial Times’ Best Books. He lived and worked in France, USA, Germany and Singapore.

Francis was the first Associate Dean of Research and holder of the President’s Chair at ESMT Berlin. He held faculty positions at Duke University and INSEAD, where he was the Paul Dubrule Chaired professor in Sustainable Development, and was a post-doctoral researcher at Massachusetts Institute of Technology (MIT). His general research interest is in the area of decision science, analytics and operations, with applications in health care, sustainability and human-AI interaction. He is the author of numerous academic articles in prominent management, analytics and economics journals such as Management Science, Operations Research, American Economics Review and others. He received several outstanding research awards and is currently an Area Editor at Operations Research.

Francis has been the recipient of many teaching awards for delivering classes to MBA and Executive MBA students at ESMT and INSEAD. He has extensive experience in executive education and corporate learning solutions, and is a regular speaker in academic and industry forums.

DAO-ISEM-IORA Seminar Series: Lu Jiaqi
2026-02-13 10:00 - 11:30
Name of Speaker

Lu Jiaqi

Schedule

13 Feb 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/FGZiBt3mT9CHxWWr-w6t5Q

Title

Bandit Allocational Instability

Abstract

About the Speaker

Jiaqi Lu is an assistant professor in the School of Data Science and the School of Management and Economics (joint appointment) at the Chinese University of Hong Kong, Shenzhen. Her research aims at understanding when and how do agents’ colliding incentives and complex dynamics lead to market inefficiencies, and how to mitigate them. The types of applications usually involve matching platforms and supply chain. For example, recently, she studies bandit algorithms’ unintended side effect on downstream tasks, such as allocational instability in platform operations and sample bias in post-policy inference. Her papers typically appear in Journals including Management Science, Operations Research, Mathematics of Operations Research, and conferences such as ACM EC and WINE.
Jiaqi Lu obtained her Ph.D. in the Decision, Risk, and Operations division at Columbia Business School, and her B.E. in Industrial Engineering, B.A. in English (double major) at Tsinghua University.

DAO-ISEM-IORA Seminar Series: Georgina Hall
2026-02-27 10:00 - 11:30
Name of Speaker

Georgina Hal

Schedule

27 Feb 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/MSVeTEDGTSGxi0TGgyLmNg

Title

Sum of Squares Submodularity

Abstract

We introduce the notion of t-sum of squares (sos) submodularity, which is a hierarchy, indexed by t, of sufficient algebraic conditions for certifying submodularity of set functions. We show that, for fixed t, each level of the hierarchy can be verified via a semidefinite program of size polynomial in n, the size of the ground set of the set function. This is particularly relevant given existing hardness results around testing whether a set function is submodular (Crama, 1989). We derive several equivalent algebraic characterizations of t-sos submodularity and identify submodularity-preserving operations that also preserve t-sos submodularity. We further present a complete classification of the cases for which submodularity and t-sos submodularity coincide, as well as examples of t-sos-submodular functions. We demonstrate the usefulness of t-sos submodularity through three applications: (i) a new convex approach to submodular regression, involving minimal manual tuning; (ii) a systematic procedure to derive lower bounds on the submodularity ratio in approximate submodular maximization, and (iii) improved difference-of-submodular decompositions for difference-of-submodular optimization.
This is joint work with Anna Deza (Georgia Tech).

About the Speaker

Georgina Hall is an Assistant Professor at INSEAD in the Decision Sciences Area. Her research focuses on convex relaxations of NP-hard problems, particularly those that arise in polynomial optimization and problems on graphs. Prior to joining INSEAD in 2019, she was a postdoctoral student at INRIA. She completed her PhD in Operations Research and Financial Engineering at Princeton University in 2018. She is the recipient of the 2018 INFORMS Optimization Society Young Researcher’s Prize and the 2020 Information Theory Society Paper Award, among other awards.

DAO-ISEM-IORA Seminar Series: Peng Sun
2026-03-11 10:00 - 23:30
Name of Speaker

Peng Sun

Schedule

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

Venue

BIZ2 0511

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/8WAQ86W5TMW5MZzaiDBNiQ

Title

Optimal Push, Pull, and Failure Funding for Global Health

Abstract

Malaria and tuberculosis each cause over half a million deaths annually, yet commercial incentives to develop treatments for these and other diseases concentrated in low-income countries remain weak. Governments and nonprofits address this gap through push (e.g., grants) and pull (e.g., prizes) mechanisms. We propose a third approach: the funder pays only if the firm fails, reimbursing part of its testing costs. This failure insurance is optimal when markets are large enough to reward success but too small to justify initial investment. We model the problem as an infinite-dimensional optimization problem with adverse selection and moral hazard constraints, and use duality theory to characterize optimal funding mechanisms. Failure insurance is preferred for tuberculosis if testing costs are below $1 billion. For most tropical diseases, including malaria, the optimal policy is pull funding with supplemental push support. These results challenge current push-heavy practice and offer broader insights for global health and innovation policy.

About the Speaker

Peng Sun is a JB Fuqua Professor in the Decision Sciences area at the Fuqua School of Business, Duke University. He researches mathematical theories and models for resource allocation decisions under uncertainty, and incentive issues in dynamic environments. His work spans a range of applications areas, from operations management, economics, finance, marketing, to health care and sustainability. He has served as a Department Editor at Management Science, and an Associate Editor at Operations Research.

DAO-ISEM-IORA Seminar Series: Robert Shumsky
2026-03-18 10:00 - 11:30
Name of Speaker

Robert Shumsky

Schedule

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

Venue

BIZ2 0511

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/fjT0SX_zQPCBsBv0ciBjsw

Title

Use it or Slowly Lose it: Expertise Atrophy with Organizational AI Usage

Abstract

As organizations adopt generative AI, its use can improve productivity but reliance can lead to atrophy of worker knowledge and skills over time. The challenge is how to incentivize human oversight and maintain long-run expertise. Using a principal-agent framework, we study optimal incentive design when workers can exert costly effort to verify and correct imperfect AI output, where effort both improves current performance and preserves expertise. A central managerial challenge is that improving AI quality makes oversight harder to motivate, since acceptable outcomes increasingly occur even when workers shirk. Consequently, profit-maximizing compensation can be non-monotonic in AI quality, skill, or return on effort, and organizations may even be better off, in terms of profitability, with worse AI systems. More subtle implications arise when skills decay with AI reliance. First, due to contracting frictions, we find that firms may (rationally) allow expertise to deteriorate by substituting higher effort from non-experts for expertise, leading to significant performance losses compared to a system in which both effort and expertise can be prescribed. Second, when tasks are relatively less complex with short learning curves and high returns on effort for low-skilled workers, then the risk of skill atrophy can mitigate these frictions. For such tasks workers are self-motivated to preserve expertise, so that higher rates of potential skill loss can, counterintuitively, increase profit. These insights highlight a managerial “danger zone” in which low-to-moderate skill decay is easily overlooked yet leads to substantial long-term losses, underscoring when proactive investment in human expertise is most valuable.

About the Speaker

Robert Shumsky is a Professor of Operations Management at the Tuck School of Business at Dartmouth and is faculty co-director of Health Care Management Education at Dartmouth. His research focuses on the improvement of service operations, and he has written about capacity estimation and control, how to allocate work to improve quality, and how to coordinate service supply chains. He has conducted research on the U.S. air traffic management system and studied transportation operations for state agencies and the Federal Aviation Administration. He has also served as a consultant for both manufacturing and service operations, including call centers and health care providers. Professor Shumsky has published articles in many academic journals including Operations Research, Management Science, and the Proceedings of the National Academy of Science. He currently serves in various editorial positions for several academic journals. He received his PhD degree in Operations Research from MIT.

DAO-ISEM-IORA Seminar Series: Ignacio Rios
2026-03-20 10:00 - 23:30
Name of Speaker

Ignacio Rios

Schedule

20 Mar 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/-QjsYdlrQXyy-bdAfa6zlg

Title

Designing Effective Fundraising Campaigns: The Role of Incentives and Solicitation Mechanisms

Abstract

Charitable donations are a vital source of funding for nonprofit organizations, enabling them to carry out their mission of addressing social issues and providing support to those in need. To boost contributions, third parties often donate large sums that fundraisers use to incentivize individual donations, with one-to-one matching being the most common mechanism. However, alternative designs may lead to even higher contributions. This paper investigates the effectiveness of two design choices in the context of fundraising: (i) the incentive mechanism, focusing on the two most prevalent ones (i.e., matching and gift unlock); and (ii) the solicitation mechanism, i.e., whether donations occur simultaneously or sequentially. We introduce a stylized game-theoretical model where a fundraiser decides the design choices and corresponding design parameters to maximize overall donations. Following the fundraiser’s decision, donors make their one-time contribution. For each design choice, we characterize the equilibrium donations and find the fundraiser’s optimal policy. We find that gift unlock consistently outperforms matching. Moreover, sequential solicitation is the optimal choice with gift unlock, whereas simultaneous solicitation yields higher overall contributions with matching. Furthermore, our simulations indicate that the effectiveness of gift unlock is robust to peer effects and donor participation uncertainty. Our findings indicate that fundraisers should prioritize gift unlock over matching, align the selected incentive mechanism with the optimal solicitation format, and calibrate campaign parameters to maximize donation outcomes.

About the Speaker

Ignacio Ríos is an Assistant Professor of Operations Management at the Jindal School of Management, University of Texas at Dallas. He holds a Ph.D. in Operations, Information, and Technology and an M.A. in Economics from Stanford University, as well as degrees in Operations Management and Industrial Engineering from the University of Chile. His research expertise lies in behavioral market design, with a focus on how incentives, information, allocation rules and users’ behavior shape outcomes in markets without money. Ignacio has played a leading role in the reform of Chile’s school choice and college admissions systems, and also in designing other two-sided matching markets. His work has been recognized with numerous awards, including the Poets & Quants “40 Under 40 Best Business School Professors” distinction, the IFORS Prize for OR in Development, and the BOM Best Paper Award.

DAO-ISEM-IORA Seminar Series: Yael Grushka-Cockayne
2026-03-27 10:00 - 11:30
Name of Speaker

Yael Grushka-Cockayne

Schedule

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

Venue

HSS 4-2

Link to register
(via Zoom)

https://nus-sg.zoom.us/meeting/register/51hGI1hiRe-T473GjiQA1w

Title

Decision-making with Ordinal Ratings

Abstract

Experts often provide judgments on an ordinal scale, which are easy to generate and are intuitive. Ordinal ratings, however, are not trivial to aggregate across multiple experts, as they provide neither the strict preference ordering of a ranking, nor the intensity of preference of cardinal scores. In addition, ordinal rating judgments often map to a broad set of outcomes, which are not expressed through the ordinal, discrete set of choices elicited. In this way, ordinal ratings also neglect to express the degree of uncertainty that may exist when rankings are interpreted as forecasts. We offer a framework for mapping ordinal ratings to continuous outcome distributions, allowing for the aggregation of ratings and the expression of the uncertainty that may exist in the forecasts. Finally, our framework allows for rendering the aggregate distributional forecasts back to the original ordinal scale, providing again an intuitive set of judgements, to be used by the decision maker. We demonstrate our framework in the context of National Football League (NFL) scout assessments of players performance. These assessments, treated as forecasts, are utilized by general managers when making player selection decisions in the annual NFL draft.

About the Speaker

Yael Grushka-Cockayne
Landmark Communication Incorporated Professor of Business Administration, Vice Dean and Senior Associate Dean for Professional Degree Programs, Academic Co-Director of the LaCross Institute for AI.
Professor Yael Grushka-Cockayne’s research and teaching activities focus on data science, artificial intelligence, forecasting, project management and behavioral decision-making. Her research is published in numerous academic and professional journals, and she is a regular speaker at international conferences in the areas of decision analysis, project management and management science. Prof. Grushka-Cockayne is an award-winning teacher, winning the Darden Morton Leadership Faculty Award in 2011, the University of Virginia’s Mead-Colley Award in 2012, the Darden Outstanding Faculty Award in 2013 and 2022, University of Virginia All University Teaching Award in 2015, the Faculty Diversity Award in 2013 and 2018, and the Transformational Faculty Award in 2024. Prof. Grushka-Cockayne teaches the core “Decision Analysis” course, an elective she designed on project management, an elective on data science and a new course on coding with ChatGPT.
Before starting her academic career, she worked in San Francisco as a marketing director of an Israeli ERP company. As an expert in the areas of project management, Prof. Grushka-Cockayne has served as a consultant to international firms in the aerospace and pharma industries. She is a UVA Excellence in Diversity fellow and a member of INFORMS, the President of the Decision Analysis Society, and a member of the Operational Research Society and the Project Management Institute (PMI). She served an associate editor at Management Science and is currently as associate editor at Operation Research.
Grushka-Cockayne was named one of “21 Thought-Leader Professors” in Data Science. Her course “Fundamentals of Project Planning and Management” Coursera MOOC has over 300,000 enrolled, across 200 countries worldwide. Her “Data Science for Business” Harvard Online course, launched in 2021, has taught hundreds of learners around the world.

DAO-ISEM-IORA Seminar Series: Park Sinchaisri
2026-04-10 10:00 - 11:30
Name of Speaker

Park Sinchaisri

Schedule

10 Apr 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/oo0ElW4xSIu9BcdsAyKQ2A

Title

Algorithmic Advice, Human Compliance, and Learning

Abstract

Problem definition:Organizations increasingly deploy algorithmic tools to support complex operational decisions,raising a practical design question: how should these tools be built when designers care not only about immediate performance, butalso about preserving and building human skill that remains valuable when advice is unavailable, imperfect, or requires genuineoversight? We study how theprecisionof algorithmic advice shapes this trade-off.Methodology/results:We develop a stylized modelof advice-taking and learning. The model characterizes a reward-learning frontier: precise, action-level advice is easier to implementand improves payoffs while available through higher compliance, whereas broad, strategic advice requires interpretation, inducesgreater exploration, and generates knowledge that is portable, even when decision environments differ. We test the model’s predictionsin two online experiments in an electric-vehicle routing and charging task, representing typical characteristics of sequential decisiontasks. Consistent with the theory, precise numerical advice delivers the strongest gains during the advice phase, whereas broaderadvice can yield more robust performance after advice is removed, specifically if the new environment differs substantially, butnot completely. We use inverse reinforcement learning to recover interpretable latent objective components from action traces,distinguishing transient compliance from persistent internalization.Managerial implications:Our results provide design guidancefor advice systems that balance short-run operational efficiency with the development of long-run human capability. They also helpvalidate inverse reinforcement learning as an effective tool for estimating human behaviors in complex sequential tasks

About the Speaker

Park Sinchaisri is an Assistant Professor of Operations and IT Management at the Haas School of Business, University of California, Berkeley. His research draws on operations management, economics, machine learning, and behavioral science to study human decision-making in complex environments, design human-AI systems that improve decision-making, and develop strategies for managing the future of work. His work has been published in Management Science and Manufacturing & Service Operations Management, and has also appeared in leading human-computer interaction venues including CSCW. He received his PhD in Operations, Information and Decisions and an AM in Statistics from the Wharton School of the University of Pennsylvania, an SM in Computational Science and Engineering from MIT, and an ScB in Computer Engineering and Applied Mathematics-Economics from Brown University. Originally from Bangkok, Thailand, he hopes his research can help address urban challenges and improve outcomes for marginalized workers.

DAO-ISEM-IORA Seminar Series: Kwok-Hao Lee
2026-04-17 10:00 - 11:30
Name of Speaker

Kwok-Hao Lee

Schedule

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

Venue

HSS 3-1

Link to register
(via Zoom)

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

Title

Two-Sided Markets Shaped By Platform-Guided Search

Abstract

We investigate concerns that vertically integrated platforms like Amazon steer demand towards their own offers via algorithmic prominence, potentially harming consumers. On Amazon, for each product, the Buybox prominence algorithm selects one seller to feature, influencing which offers consumers consider. Using novel Amazon sales and Buybox (prominence) data, we estimate a structural model capturing the effects of such algorithmic prominence on consumer choices, seller pricing, and entry. We find that the platform can indeed steer demand as 95% of consumers consider only the Buybox offer. The Buybox is highly price-elastic (−21), but skews towards Amazon’s own offers, which are featured as frequently as observably similar offers priced 5% cheaper. Still, as consumers prefer these offers, this skew does not amount to self-preferencing in the sense of harming consumers: consumer surplus is roughly maximized at the estimated Amazon Buybox advantage, which balances higher prices against showing consumers their preferred offers.

About the Speaker

Lee Kwok Hao is an industrial organisation economist working at the intersection of digital markets and the smart city. He uses administrative and platform data to study how algorithms and policy rules govern search, matching, pricing, and allocation, with a focus on transportation systems and public housing. As an Assistant Professor (Presidential Young Professor) at the Department of Strategy & Policy at the National University of Singapore (NUS) Business School, Kwok Hao has been a recipient of the Social Science and Humanities Research Fellowship under the Social Science Research Council of Singapore. Previously, Kwok Hao was a Presidential Fellow at the NUS Business School, during which he spent a postdoctoral stint at the Cowles Foundation at Yale University. He obtained my PhD from Princeton University after formative years at the University of Chicago and Washington University in St. Louis.

DAO-ISEM-IORA Seminar Series: Yanwei Jia
2026-04-24 10:00 - 11:30
Name of Speaker

Yanwei Jia

Schedule

24 Apr 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/QpOphmoURVCrbzjftZht4g

Title

When to Quit a Venture: Normative Theory and Structural Identification of Decoupled Belief and Decision

Abstract

Understanding how agents learn and make decisions under uncertainty is a fundamental question in many fields, with applications including real options, R&D, and entrepreneurial ventures. The conventional approach formulates this learning process as an optimal stopping problem within a Bayes framework, assuming agents possess the cognitive sophistication to continuously update their beliefs based on statistical principles, thereby rigidly locking their decisions to these updated beliefs and forcing a strict, deterministic threshold rule. This paper develops a continuous-time reinforcement learning framework for sequential experimentation that formally separates beliefs from actions. By decoupling the evaluation and policy processes, we provide a unifying framework that yields both normative benchmarks and flexible positive dynamics. Normatively, using the workhorse Gaussian bandit model, we prove that by properly tuning learning rates, the framework achieves a logarithmic regret bound, matching the efficiency of Bayesian rationality. Positively, the decoupled policy generates distinct and testable predictions, such as experience-driven, path-dependent quitting dynamics, even when the belief is consistent with its Bayesian counterpart. Crucially, we prove the structural identifiability of these hidden learning dynamics. By utilizing the method of simulated moments, we demonstrate how this framework can be structurally estimated directly from censored observational field data and extended to general jump-diffusion bandits.

About the Speaker

Yanwei Jia is an assistant professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong. He obtained his Ph.D. degree from the National University of Singapore in 2020, and B.Sc. from Tsinghua University in 2016. Prior to joining CUHK in 2023, he was an associate research scientist and adjunct assistant professor in the Department of Industrial Engineering and Operations Research at Columbia University. His research interest falls broadly into financial decision-making problems and uses the structural approach to study the decision making and information aggregation mechanism.

DAO-ISEM-IORA Seminar Series: Diwakar Gupta
2026-05-20 10:00 - 11:30
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.
