IORA - Institute of Operations Research and Analytics

DAO-ISEM-IORA Seminar Series: Victor Martínez de Albéniz
2025-09-05 10:00 - 11:30 (Asia/Singapore)
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: 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.
