Contact Shandra Bremer at [email protected] with any questions.
February 27
Bora Keskin, Duke Fuqua School of Business
Title: Feature-based Scheduling and Dynamic Learning with a Large Backlog
Time: 11 a.m. - noon
Location: R2240
Abstract: Motivated by mass emergencies such as pandemics and earthquakes that result in a large number of patients requiring critical services, we study a feature-based scheduling problem with N patients waiting to be served by a decision maker. The decision maker knows each patient's features and waiting cost, but does not know how the expected service time depends on the patient features. After a patient is served, the decision maker observes the realized service time and determines which patient to serve next. We prove that the decision maker's expected regret, i.e., the difference between the expected total waiting cost of the decision maker and that of a clairvoyant who knows the patients' expected service times, is at least of order N^{3/2}. We then design a learn-then-commit policy and an uncertainty ellipsoid policy to dynamically learn the expected service times, and prove that the expected regrets of these two policies are of order N^{5/3} log^{1/2} N and N^{3/2} log^{3/2} N, respectively. Finally, we conduct simulation experiments and a case study based on real-world data from Duke University Hospital to demonstrate the practical value of our policies relative to commonly used approaches. (Paper available at SSRN: https://ssrn.com/abstract=4852356.)
Biography: Bora Keskin is an Associate Professor of Operations Management at Duke University’s Fuqua School of Business. His research focuses on data-driven decision making under uncertainty, with applications in dynamic pricing, revenue management, platform operations, and supply chain innovation. His recent work explores how emerging technologies—such as blockchain, IoT, and AI—are reshaping the future of operations. Bora received his Ph.D. from Stanford University. His work has been recognized with several awards, including the Lanchester Prize (2019) and the MSOM Young Scholar Prize (2024). Prior to joining Duke in 2015, he worked as a consultant at McKinsey & Company and served on the faculty of the University of Chicago Booth School of Business. At Fuqua, Bora teaches the MBA elective Value Chain Innovation in Business Processes, which emphasizes technological change and data-driven practices. He also teaches a Ph.D. course on revenue management and pricing. Outside Duke, he serves as an Associate Editor for Management Science and Operations Research, and as a Senior Editor for Production and Operations Management.
March 13
Safak Yucel, McDonough School of Business, Georgetown University
Title: Additionality of Carbon Offsets: Project-specific vs. Standardized Baselines
Time: 11 a.m. - noon
Location: R2240
Biography: 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.
Abstract: Developers generate carbon offsets by investing in emissions-reduction projects to receive two sources of revenue: project revenue, e.g., from the electricity sold in a renewable energy project, and offset revenue based on offsets issued by a non-profit carbon registry. The registry ensures additionality, i.e., the offset should represent one unit of reduction from the developer's business-as-usual emissions---what the developer's emissions would have been without the offset revenue. Although environmental groups raise greenwashing concerns against non-additional offsets, ensuring additionality is challenging because it requires assessing project revenue, which is the developer's private information. In practice, the registry assigns a baseline to represent business-as-usual emissions through one of the two methods: Under the project-specific method, a developer self-reports its business-as-usual emissions to the registry, which then inspects the report and assigns reported emissions as the baseline if it accepts the project. Under the standardized method, the registry assigns a common baseline to a group of similar projects. It is unclear which method leads to fewer non- additional offsets, greater reduction in emissions, and should be chosen by a registry. We analyze these economic and environmental implications by developing a sequential game between a registry and project developers. We find that project-specific baselines may lead to fewer non-additional offsets but lower emissions reduction, cautioning environmental groups against simply advocating for the method that leads to fewer non-additional offsets. We also find that a registry may prefer project-specific baselines even when they result in more non-additional offsets. Finally, we find that a registry's preference between the two methods is typically consistent with a corporate buyer's.
March 20
Shreyas Sekar, University of Toronto Scarborough and Rotman School of Management
Title: Designing Pandora's Box: Product Rankings for Two-Sided Marketplaces
Time: 11 a.m. - noon
Location: R2240
Biography: Shreyas Sekar is an Assistant Professor of Operations Management at the Department of Management, University of Toronto Scarborough, and cross-appointed to the Operations Management and Statistics area at the Rotman School of Management.
His research is centered around data-driven decision making in dynamic and strategic environments, with applications to digital marketplaces. His work draws upon techniques from diverse domains such as optimization, machine learning, revenue management and pricing, and mechanism design to tackle problems at the interface of operations management, online platforms, and data analytics.
Prior to his appointment at the University of Toronto, Shreyas was a Postdoctoral Fellow at the Harvard Business School and the Laboratory for Innovation Science at Harvard. He received his PhD in Computer Science from Rensselaer Polytechnic Institute where he was awarded the Robert McNaughton Prize for the best graduate dissertation in CS.
Abstract: With the rapid growth of e-commerce, product rankings have emerged as a key lever used by two-sided platforms to match supply with demand. Conventionally, the design of product rankings has focused almost exclusively on the demand side of the market, and aimed at helping consumers discover
products and guiding them towards higher-value options. However, such an approach is fundamentally incomplete: it overlooks how ranking policies shape seller profitability and, consequently, their willingness to participate in the marketplace. We propose a holistic framework for product rankings that accounts for how both buyers and sellers respond to the platform's policy. Surprisingly, we find that widely adopted personalized greedy rankings can be suboptimal, as they fail to generate sufficient revenue for niche sellers to justify joining the marketplace. To address this inefficiency, we develop a randomized, incentive-compatible ranking policy that balances the mix of mainstream and niche sellers in top positions, without compromising revenue or consumer surplus. With many e-commerce platforms contending with seller attrition and rising consumer dissatisfaction, our work offers a new perspective on product rankings; in particular, we show how rankings can be a tool for growing the marketplace as a whole.
This is joint work with Yiangos Papanastasiou
March 27
Lesley Meng, Yale School of Management
Title: Crisis at the Core: Examining the Ripple Effects of Critical Incidents on Emergency Department Physician Work Performance and Work Style
Time: 11 a.m. - noon
Location: R2240
Biography: Lesley Meng is an Assistant Professor of Operations Management at the Yale School of Management. Professor Meng's research utilizes novel large-scale datasets to investigate the (often hidden) impact of management decisions at the organization level on healthcare worker behavior, and subsequently, the effectiveness and efficiency of patient care. More generally, Professor Meng's research is in the area of empirical healthcare operations management, which combines her past education in medical science and public health with her doctoral training in operations management, econometrics, and causal inference.
Professor Meng holds an Honors Specialization in Medical Science from the University of Western Ontario, an Honors in Business Administration degree from the Richard Ivey School of Business, a Masters of Public Health in Health Policy and Management from Columbia University, and a Ph.D. in Operations, Information, &
Decisions from The Wharton School of the University of Pennsylvania. Prior to her Ph.D., Professor Meng worked as a Research Associate at the Massachusetts General Hospital Institute for Technology Assessment. During her time at Columbia, she worked as a Project Analyst at the Memorial Sloan-Kettering Cancer Center and as a Research Assistant at the Harvard School of Public Health.
April 10
Zhengyuan Zhou, New York University Stern School of Business
Title: Optimal No-Regret Learning in Repeated First-Price Auctions
Time: 11 a.m. - noon
Location: R2240
Biography: Zhengyuan Zhou is currently an associate professor in New York University Stern School of Business, Department of Technology, Operations and Statistics. Before joining NYU Stern, Professor Zhou spent the year 2019-2020 as a Goldstine research fellow at IBM research. He received his BA in Mathematics and BS in Electrical
Engineering and Computer Sciences, both from UC Berkeley, and subsequently a PhD in Electrical Engineering from Stanford University in 2019. His research interests lie at
the intersection of machine learning, stochastic optimization and game theory and focus on leveraging tools from those fields to develop methodological frameworks to
solve data-driven decision-making problems.
Abstract: First-price auctions have very recently swept the online advertising industry, replacing second-price auctions as the predominant auction mechanism on many platforms for display ads bidding. This shift has brought forth important challenges for a bidder: how should one bid in a first-price auction, where unlike in second-price auctions, it is no longer optimal to bid one’s private value truthfully and hard to know the others’ bidding behaviors? In this paper, we take an online learning angle and address the fundamental problem of learning to bid in repeated first-price auctions. We discuss our recent work in leveraging the special structures of the first-price auctions to design minimax optimal no-regret bidding algorithms.
April 17
Vahideh Manshadi, Yale School of Management
Title: Why the Rooney Rule Fumbles: Limitations of Interview-stage Diversity Interventions in Labor Markets
Time: 11 a.m. - noon
Location: R2240
Biography: Vahideh Manshadi is the Michael H. Jordan Professor of Operations at Yale School of Management and the Research Director for Operations Research at the Center for Algorithms, Data, & Market Design at Yale (CADMY).
Her current research focuses on the operations of online and matching platforms, especially those with societal impact, including volunteer crowdsourcing, refugee resettlement, organ allocation, and information (news) platforms. She has collaborated with nationwide platform-based nonprofits, including Feeding America’s MealConnect, Food Rescue US , VolunteerMatch, and national kidney exchange programs, and often impacted the practice of these organizations.
Her research has been recognized with numerous awards across various INFORMS communities, including the Auctions and Market Design Rothkopf Prize (2020, 2021, 2024), the MSOM Best Student Paper Prize (2021, 2024), the Best OM Paper in Operations Research Award (2025), the Doing Good with Good OR Award (2023), the Public Sector OR Best Paper Award (finalist, 2020, 2024), and the Revenue Management and Pricing Practice Award (finalist, 2024), among many others.
Professor Manshadi serves as an associate editor for Management Science and Operations Research, and as the vice president of the Auctions & Market Design section at INFORMS. She also contributes to the ACM SIGecom, having served as the Program Co-Chair of EAAMO ’23 and as the Track/Area/Tutorial Chair for EC and EAAMO. She received her Ph.D. in electrical engineering at Stanford University, where she also received MS degrees in statistics and electrical engineering. Before joining Yale, she was a postdoctoral scholar at the MIT Operations Research Center.
Abstract: Many industries, including the NFL with the Rooney Rule and law firms with the Mansfield Rule, have adopted interview-stage diversity interventions requiring a minimum representation of disadvantaged groups in the interview set. However, the effectiveness of such policies remains inconclusive. In light of this, we develop a framework of a two-stage hiring process, where rational firms, with limited interview and hiring capacities, aim to maximize the match value of their hires. The labor market consists of two equally sized social groups, m and w, with identical ex-post match value distributions. Match values are revealed only post-interview, while interview decisions rely on partially informative pre-interview scores. Pre-interview scores are more informative for group m, while interviews reveal more for group w; as a result, if firms could interview all candidates, both groups would be equally hired. However, due to limited interview capacity and information asymmetry, we show that requiring equal representation in the interview stage does not translate into equal representation in the
hiring outcome, even though interviews are more informative for group w. In certain regimes, with or without intervention, a firm may interview more group w candidates but still hire fewer. At an individual level, we show that strong candidates from both groups benefit from the intervention as the candidate-level competition weakens. For borderline candidates, only group w candidates gain at the expense of group m. To understand the impact of non-universal interview-stage interventions on the market, we study a model with two vertically differentiated firms, where only the top firm adopts the intervention. We characterize the unique equilibrium and demonstrate potentially negative effects: we show that in certain regimes the lower firm hires fewer group w candidates due to increased firm-level competition for them, and further find examples where overall fewer group w candidates are hired across the market. At an individual level, while superstar
candidates in both groups benefit, surprisingly the impact on borderline candidates may reverse: the lower firm may replace borderline group w candidates with borderline group m candidates in its interview set, effectively reducing the chance of those borderline group w candidates being hired. Overall, our findings highlight challenges in diversifying the labor market at early hiring stages due to information asymmetry, filtering, and competition. Beyond our context, our natural framework of a market with two-stage hiring may be of independent interest.
April 22
Ruomeng Cui, Emory Goizueta Business School
Title: The Value of Last-Mile Delivery in Online Retail
Time: 11 a.m. - noon
Location: R2230
Biography: Ruomeng Cui is a Goizueta Foundation Term Chair Associate Professor in the Department of Information System and Operations Management at Emory University’s Goizueta Business School. Professor Cui’s research focuses on causal-driven decision making in platforms, retail, and supply chains. Her expertise lies in causal-driven decision making, identify-then-optimize models, causal inference, causal machine learning, and economics. In her research, Professor Cui investigates how operations strategies create and deliver value in companies' digital and AI transformation. Specifically, she studies how digitization and AI reshapes how companies compete and operate. Professor Cui has published papers in leading academic journals, including Management Science, Manufacturing & Service Operations Management (M&SOM), Operations Research, Production & Operations Management (POM), Harvard Business Review and others.
Her research has been recognized by 19 prestigious and highly competitive prizes including 2022 POMS Early Career Research Accomplishments Award, 2023 & 2024 M&SOM Best Paper in Management Science Award, 2019 INFORMS Junior Faculty Interest Group (JFIG) Paper Competition award, and 2019 M&SOM Practice-Based Paper Competition award. Professor Cui’s research has been widely covered by the media, including NPR, Financial Times, Fox News, Fortune Magazine, and HBR.
Professor Cui has consulted in various capacities for Amazon, Alibaba, JD.com, PepsiCo, Tencent, Cainiao, Meituan, Collage.com, and many other leading firms. She has been a visiting scholar at Amazon since 2022, where she designed, developed, and implemented cutting-edge causal inference, machine learning, optimization, and economic models to drive supply chain and robotics decisions in various spaces.
April 24:
Joint-Sponsored with the Zell Lurie Institute Entrepreneurship Research Seminar Series
Michael Luca, Johns Hopkins Carey Business School
Title: Leaving Money on the Dashboard: Price Dispersion and Search Frictions on Uber and Lyft
Time: 11 a.m. - noon
Location: R2220
Biography: Michael Luca is a professor and the director of the Technology and Society Initiative at the Johns Hopkins University, Carey Business School, and a faculty research fellow at the NBER. Professor Luca's research, teaching, and advisory work focuses on the design of online platforms, and on the ways in which data can inform managerial and policy decisions.
Abstract: We document price differences for identical trips on Uber and Lyft, based on an audit of the two platforms. While price dispersion exists in the market, device-level data show that only 16.1 percent of consumers opening one app also open the other. Our estimates suggest that the modest frictions
involved in comparison shopping increase platforms’ gross booking volume by over $300 million annually in New York City alone. While price-comparison engines could in principle reduce frictions, Uber’s API terms of use limit such services, reducing riders’ ability to price compare.