BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//IORA - Institute of Operations Research and Analytics - ECPv6.15.11//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:IORA - Institute of Operations Research and Analytics
X-ORIGINAL-URL:https://iora.nus.edu.sg
X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Singapore
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:+08
DTSTART:20210101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220902T100000
DTEND;TZID=Asia/Singapore:20220902T113000
DTSTAMP:20260417T103405
CREATED:20220812T033552Z
LAST-MODIFIED:20220829T070504Z
UID:15964-1662112800-1662118200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Andrew Lim
DESCRIPTION:Andrew Lim is a Professor in the Department of Analytics and Operations and the Department of Finance at NUS Business School. He is also affiliated with the Institute for Operations Research and Analytics. His research is in the area of stochastic control\, optimization under uncertainty\, financial engineering\, and robust and data driven decision making. From 2002 — 2014\, he was on the faculty of the Department of Industrial Engineering and Operations Research at the University of California (Berkeley). He is a past recipient of the National Science Foundation CAREER Award. He serves as an Associate Editor for Operations Research and Management Science\, and was previously on the editorial board of the IEEE Transactions on Automatic Control. \n  \n\n\n\nName of Speaker\nProfessor Andrew Lim\n\n\nSchedule\n2 September 2022\, Friday at 10:00am \n(60 minutes talk + 30 minutes Q&A)\n\n\nVenue \nInnovation 4.0 Building\, level 1\, Seminar Room (next to the level 1 café)\n\n\nLink to Register \n(Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZYld-mhpj4tGNWdd6GD8BGY-whthwWbduyq\n\n\nTitle\nMechanisms for Coordinating Systems of Decentralized Agents \n \n\n\nAbstract\nA typical service/operations system is populated by multiple interacting decentralized agents who make decisions that collectively determine system performance. Decentralized agents are hired because they are domain experts\, but the aggregate system is usually not efficient if agents optimize in isolation. Coordination is difficult\, however\, as it requires a decision maker/mechanism designer/principal who can optimize the aggregate system\, which is unrealistic when the system is complex and domain experts are needed to control its many parts. We consider these issues in the setting of a service system modeled by a single server queue where the arrival rate is controlled by multiple agents who dynamically sets prices and earn revenue on each arrival\, and the service rate by a different agent who is concerned about minimizing service costs. We show that transfer payments between agents can induce decisions that optimize system efficiency even if every agent misspecifies the impact of the other agents in their models. The optimal transfers\, however\, depend on the “private” models of each agent and can only be directly computed by a “smart” mechanism designer/principal who can optimize the aggregate system. We propose a mechanism for computing the optimal transfers where decentralized agents iteratively share their valuations of shared resources. We show that this algorithm converges to the optimal transfer function at a geometric rate\, and provide natural conditions under which it is optimal for agents to report resource valuations truthfully. This algorithm can be implemented without a “smart” mechanism designer/principal\, and decentralized agents are not required to share information about their domain of expertise\, or even correctly specify the models\, actions or number of other agents when optimizing their decisions. \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-andrew-lim-2/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220909T100000
DTEND;TZID=Asia/Singapore:20220909T113000
DTSTAMP:20260417T103405
CREATED:20220812T033640Z
LAST-MODIFIED:20220902T070837Z
UID:15966-1662717600-1662723000@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Roland Yap
DESCRIPTION:Roland Yap is an associate professor in the School of Computing\, National University of Singapore. He obtained his PhD from Monash University\, Australia. His research interests are in Artificial Intelligence\, Big Data\, Constraints\, Programming Languages\, Security. He is well known for his work on Constraint Logic Programming and the CLP(R) programming language which is one of the first programming languages where constraints are first class. CLP(R) has led to many developments in Constraint Programming (CP) and Logic Programming. \n  \n\n\n\nVenue \nInnovation 4.0 Building\, level 1\, Seminar Room (next to the level 1 café) \n \n\n\nLink to Register \n(Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZYqd-mhrTopHtSTBPnJnbgrD4SqeGsCmMJz\n\n\nTitle\nA Bus Routing Problem with Multiple Stop Preferences and Tradeoffs \n \n\n\nAbstract\nWe present a variant of the School Bus Routing Problem. In this variant\, there multiple stop locations per passenger with preferences on the stops. There is a multimodal transportation aspect. There is also a tradeoff between route cost and passenger preferences. For the realistic scenario\, a real-time requirement that the route be obtained quickly. This necessitates efficient solution which can give good solutions. We discuss one instance of this problem with real datasets in the Singapore context.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-roland-yap/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220916T100000
DTEND;TZID=Asia/Singapore:20220916T113000
DTSTAMP:20260417T103405
CREATED:20220812T033747Z
LAST-MODIFIED:20220912T013308Z
UID:15968-1663322400-1663327800@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Phebe Vayanos
DESCRIPTION:Phebe Vayanos is an Assistant Professor of Industrial & Systems Engineering and Computer Science at the University of Southern California. She is also an Associate Director of CAIS\, the Center for Artificial Intelligence in Society at USC. Her research is focused on Operations Research and Artificial Intelligence and in particular on optimization and machine learning. Her work is motivated by problems that are important for social good\, such as those arising in public housing allocation\, public health\, and biodiversity conservation. Prior to joining USC\, she was lecturer in the Operations Research and Statistics Group at the MIT Sloan School of Management\, and a postdoctoral research associate in the Operations Research Center at MIT. She holds a PhD degree in Operations Research and an MEng degree in Electrical & Electronic Engineering\, both from Imperial College London. She serves as a member of the ad hoc INFORMS AI Strategy Advisory Committee\, she is an elected member of the Committee on Stochastic Programming (COSP)\, and the VP of Communications for the INFORMS Section on Public Sector Operations Research. She is an Associate Editor for Operations Research Letters and Computational Management Science. She is a recipient of the NSF CAREER award and the INFORMS Diversity\, Equity\, and Inclusion Ambassador Program Award. \n\n\n\nVenue \nTalk will be held via Zoom \n \n\n\nLink to Register \n(Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZIlduGgqz0sEtFr4NIgpQnX2vhMelYAA5b6\n\n\nTitle\nInterpretability\, Robustness\, and Fairness in Predictive and Prescriptive Analytics for Social Impact \n \n\n\nAbstract\nMotivated by problems in homeless services delivery\, suicide prevention\, and substance use prevention\, we consider the problem of learning optimal interpretable\, robust\, and fair models in the form of decision-trees to assist with decision-making in socially sensitive\, high-stakes settings. We propose new models and algorithms\, showcase their flexibility\, and theoretical and practical benefits\, and demonstrate substantial improvements over the state of the art. This presentation is based on the following papers: \nStrong optimal classification trees\, S. Aghaei\, A. Gómez\, P. Vayanos. Under second round of review at Operations Research\, January 2021. \nLearning optimal fair classification trees\, N. Jo\, S. Aghaei\, J. Benson\, A. Gómez\, P. Vayanos. Under review for ACM Conference on Fairness\, Accountability\, and Transparency (FAccT)\, 2022. \nLearning optimal prescriptive trees from observational data\, N. Jo\, S. Aghaei\, A. Gómez\, P. Vayanos\, Under Review at Management Science\, August 2021. \nOptimal robust classification trees\, N. Justin\, S. Aghaei\, A. Gómez\, P. Vayanos\, AAAI  Workshop on Adversarial Machine Learning and Beyond\, 2022.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-phebe-vayanos/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220930T100000
DTEND;TZID=Asia/Singapore:20220930T113000
DTSTAMP:20260417T103405
CREATED:20220812T033845Z
LAST-MODIFIED:20221004T092447Z
UID:15970-1664532000-1664537400@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Ozlem Ergun
DESCRIPTION:Dr. Özlem Ergun is a COE Distinguished Professor and Associate Chair for Graduate Studies in Mechanical and Industrial Engineering at Northeastern University. Dr. Ergun’s research focuses on design and management of large-scale and decentralized networks. She has applied her work on network design\, management\, and resilience to problems arising in many critical systems including transportation\, pharmaceuticals\, and healthcare.  She has worked with organizations that respond to emergencies and humanitarian crises around the world\, including USAID\, UNWFP\, UNHCR\, IFRC\, OXFAM America\, CARE USA\, FEMA\, USACE\, CDC\, AFCEMA\, and MedShare International.  Recently\, Dr. Ergun partnered with the Massachusetts’ Executive Office of Elder Affairs (EOEA) to help match qualified medical professionals to Long Term Care facilities with open positions around the state as part of the state’s response efforts to COVID19. Dr. Ergun also served as a member of the National Academies Committee on Building Adaptable and Resilient Supply Chains after Hurricanes Harvey\, Irma\, and Maria and the National Academies Committee on Security of America’s Medical Supply Chain. She was the President of INFORMS Section on Public Programs\, Service and Needs in 2013. She currently serves as the Area Editor at the Operations Research journal for Policy Modeling and the Public Sector Area and a Department co-Editor at MSOM journal for Environment\, Health and Society Department. \n  \nPrior to joining Northeastern Dr. Ergun was the Coca-Cola Associate Professor in the School of Industrial and Systems Engineering at Georgia Institute of Technology\, where she also co-founded and co-directed the Health and Humanitarian Systems Research Center at the Supply Chain and Logistics Institute.  She received a B.S. in Operations Research and Industrial Engineering from Cornell University in 1996 and a Ph.D. in Operations Research from the Massachusetts Institute of Technology in 2001. \n  \n\n\n\nVenue \nTalk will be held via Zoom \n \n\n\nLink to register\nhttps://nus-sg.zoom.us/meeting/register/tZwtceyurzkoHd2Hh7IItvgFzCRgXgwVQzxD \n \n\n\nTitle\nOptimizing Post-Disruption Response Operations to Improve Resilience of Critical Infrastructure Systems \n \n\n\nAbstract\nCritical infrastructure systems (CIS) underpin almost every aspect of the modern society by enabling the essential functions through overlaying service networks. After a disruption impacting the CIS\, the functionality of the overlaying service networks degrades. Thus\, after an extreme event\, in order to minimize the negative impact to society\, it is crucial to restore the disrupted CIS as soon as possible. In this talk\, we focus on disruptions created by natural hazards on transportation CIS and develop methods to efficiently plan the post-disaster response operations. \n  \nIn the aftermath of a natural disaster\, the transportation network is disrupted due to the debris blocking the roads and obstructing the flow of relief aid and search-and-rescue teams between critical facilities and disaster sites. In the first few days following a disaster\, in order to deliver aid to those in need\, blocked roads must be cleared by pushing the debris to the sides. In this context\, we define the road network recovery problem (RNRP) as finding a schedule to clear the roads with limited resources such that all the service demanding locations are served in the shortest possible time. First\, we address the deterministic RNRP and propose a novel network science inspired measure to quantify the criticality of the components within a disrupted service network and develop a restoration heuristic. Next\, we consider RNRP with stochastic demand and propose an approximate dynamic programming approach for identifying an effective policy under uncertainty. \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-ozlem-ergun/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221007T100000
DTEND;TZID=Asia/Singapore:20221007T113000
DTSTAMP:20260417T103405
CREATED:20220812T033922Z
LAST-MODIFIED:20221004T092529Z
UID:15972-1665136800-1665142200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Kaidi Yang
DESCRIPTION:Kaidi Yang is an Assistant Professor in the Department of Civil and Environmental Engineering at the National University of Singapore. He aims to develop efficient and trustworthy algorithms for the design and operation of future mobility systems\, with a particular focus on advances in vehicular technology (e.g.\, connected and automated vehicles\, electric vehicles\, etc.) and shared mobility. Before joining NUS\, he was a postdoctoral scholar with the Autonomous Systems Lab at Stanford University. He received his Ph.D. from ETH Zurich in 2019\, M.Sc. in Control Science and Engineering from Tsinghua University in 2014\, and dual bachelor’s degrees in Automation and Mathematics from Tsinghua University in 2011. \n  \n\n\n\nVenue \nInnovation 4.0 Building\, level 1\, Seminar Room (next to the level 1 café) \n \n\n\nLink to Register \n(Hybrid Session)\nhttps://nus-sg.zoom.us/meeting/register/tZYtfuisrjovHNE8qXjzeBwFwSDutMZlaLbu\n\n\nTitle\nOperation of Traditional and Autonomous Mobility-on-Demand\n\n\nAbstract\nThe past decade has witnessed the widespread deployment of Mobility-on-Demand (MoD) services\, such as the ride-hailing services provided by Uber and Grab. One key operational challenge associated with MoD services is the vehicle imbalances due to asymmetric transportation demand: vehicles tend to accumulate in some regions while becoming depleted in others\, giving rise to inefficient operations of the MoD system. We aim to employ emerging automated vehicles (AVs) to improve the operation of MoD systems\, leveraging their capability of being globally coordinated. In the first part of the talk\, we consider the transition period of AV deployment\, whereby an MoD system operates a mixed fleet of AVs and human-driven vehicles (HVs). In such systems\, AVs are centrally coordinated by the operator\, and the HVs might strategically respond to the coordination of AVs. We model such a system using a Stackelberg framework where the MoD operator serves as the leader and human-driven vehicles serve as the followers. We further develop a real-time coordination algorithm for AVs. In the second part of the talk\, we propose a set of reinforcement learning (RL)-based algorithms to improve the efficiency of MoD systems operating a fleet of AVs. We demonstrate that graph neural networks enable RL agents to recover behaviour policies significantly more transferable\, generalisable\, and scalable than policies learned through other approaches. We further improve the generalisability by integrating meta-learning to transfer to unseen scenarios (e.g.\, different cities). \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-kaidi-yang/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221028T100000
DTEND;TZID=Asia/Singapore:20221028T113000
DTSTAMP:20260417T103405
CREATED:20220812T034645Z
LAST-MODIFIED:20221025T014948Z
UID:15974-1666951200-1666956600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Asa Palley
DESCRIPTION:Asa Palley is an Assistant Professor of Operations and Decision Technologies at the Kelley School of Business at Indiana University. He develops and studies methods to gather and aggregate expert opinions for use in managerial making. Secondary interests include learning in sequential decision problems\, carbon pricing and investment in renewable generation and storage capacity\, and the application of decision analysis to public policy questions. His work has been published in the journals Management Science\, Experimental Economics\, and Risk Analysis. \n\n\n\nVenue \nInnovation 4.0 Building\, level 1\, Seminar Room (next to the level 1 café) \n \n\n\nLink to Register \n(Hybrid Session)\nhttps://nus-sg.zoom.us/meeting/register/tZwpde-tqTkjHtS27eyS1k611RkaNyQDO4DT\n\n\nTitle\nBoosting the Wisdom of Crowds Within a Single Judgment Problem: Weighted Averaging Based on Peer Predictions\n\n\nAbstract\nA combination of point estimates from multiple judges often provides a more accurate aggregate estimate than a point estimate from a single judge\, a phenomenon called “the wisdom of crowds”. However\, if the judges use shared information when forming their estimates\, the simple average will end up over-emphasizing this common component at the expense of the judges’ private information. A decision maker could in theory obtain a more accurate estimate by appropriately combining all information behind the judges’ opinions. Although this information underlies the judges’ individual estimates\, it is typically unobservable and thus cannot be directly aggregated by a decision maker. In this article\, we propose a weighting of judges’ individual estimates that appropriately combines their collective information within a single estimation problem. Judges are asked to provide both a point estimate of the quantity of interest and a prediction of the average estimate that will be given by all other judges. Predictions of others are then used as part of a criterion to determine weights that are applied to each judge’s estimate to form an aggregate estimate. Our weighting procedure is robust to noise in the judges’ responses and can be expressed in closed form. We use both simulation and data from a collection of experimental studies to illustrate that the weighting procedure outperforms existing methods. An R package called metaggR implements our method and is available on CRAN. \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-asa-palley/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221104T100000
DTEND;TZID=Asia/Singapore:20221104T113000
DTSTAMP:20260417T103405
CREATED:20220812T034742Z
LAST-MODIFIED:20221102T064417Z
UID:15976-1667556000-1667561400@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Ying-Ju Chen
DESCRIPTION:Ying-Ju Chen is a Chair Professor at HKUST. Prior to the current position\, he was a faculty in the Department of IEOR at UC Berkeley. He obtained a PhD degree in Operations Management from Stern School of Business at New York University in 2007\, and he also holds master’s and bachelor’s degrees of Electrical Engineering from National Taiwan University. \nHe is a recipient of Franklin Prize for Teaching Excellence (MBA non-required/MSc\, highest honor at HKUST Business School\, 2 winners per year)\, NYU teaching excellence award\, Most Influential Service Operations Paper Award in Production and Operations Management\, Harold W. Kuhn Award of Naval Research Logistics\, Second place of INFORMS Junior Faculty Interest Group (JFIG) paper competition\, Higher Education Outstanding Scientific Research Output Award (Social Science\, third prize)\, Best paper award of CSAMSE (third prize)\, the Harold MacDowell Award from Stern School\, Meritorious Service Awards from Management Science and Manufacturing & Service Operations Management\, and other awards and fellowships during his academic journey. He is ranked No. 2 among researchers world-wide by weighted corrected publication rate in Operations Management according to an article in Decision Sciences (2021). \nHe serves as a department editor for NRL and a senior/associate editor for POM and M&SOM journals. His current research interests lie in network economics\, socially responsible operations\, operations-marketing interface\, and supply chain management. His work has appeared in several leading journals in the fields of economics\, electrical engineering\, information systems\, marketing\, and operations research. \n  \n\n\n\nVenue \nTalk will be held via Zoom\n\n\nLink to Register \n(Zoom Session)\nhttps://nus-sg.zoom.us/meeting/register/tZArceuhqj4jG9K-Xgwq9YDyfLWIDuRkt2_i\n\n\nTitle\nPostgraduate program applications: simultaneous search\, sequential outcomes\, and reservation fees\n\n\nAbstract\nThis paper studies a simultaneous-search problem in which a player observes the outcomes sequentially\, and must pay reservation fees to maintain eligibility for recalling the earlier offers. We use postgraduate program applications to illustrate the key ingredients of this family of problems. We develop a parsimonious model with two categories of schools: reach schools\, which the player feels very happy upon joining\, but the chance of getting into one is low; and safety schools\, which are a safer choice but not as exciting. The player first decides on the application portfolio\, and then the outcomes from the schools applied to arrive randomly over time. We start with the extreme case wherein the safety schools always admit the player. We show that it suffices to focus on the last safety school\, which allows us to conveniently represent the player’s value function by a product form of the probability of entering the last safety period and the expected payoff from then on. \nWe show that the player’s payoff after applications is increasing and discrete concave in the number of safety schools. We also develop a recursive dynamic programming algorithm when  admissions to safety schools are no longer guaranteed. We demonstrate instances in which the player applies to more safety schools when either the reservation fee gets higher or the admission probability drops lower\, and articulate how these arise from the portfolio optimization consideration. This has strong managerial implications for service providers in devising their reservation fees and admission rates\, especially for institutions that are not universally favored by prospective applicants.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-ying-ju-chen/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221110T100000
DTEND;TZID=Asia/Singapore:20221110T113000
DTSTAMP:20260417T103405
CREATED:20221107T010749Z
LAST-MODIFIED:20221107T011038Z
UID:16455-1668074400-1668079800@iora.nus.edu.sg
SUMMARY:DAO-IORA joint seminar: Zhang Jiheng
DESCRIPTION:Jiheng Zhang is the head and a professor in the Department of Industrial Engineering and Decision Analytics of HKUST. He also hold a joint appointment at the Department of Mathematics of HKUST. His research interests are in the areas of 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 Operations Research\, Stochastic Systems\, Probability in the Engineering and Informational Sciences. He has been the co-director of Elliptic lab since 2018\, focusing on various practical projects with industry partners including Huawei and Webank. He has invented several patents on large-scale production planning and blockchain consensus mechanism design with industry partners. He received his Ph.D. degree in operations research from the H. Milton Stewart School of Industrial and Systems Engineering at 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. \n  \n\n\n\nVenue \nSeminar room at i4.0 building (Level 1)\n\n\nLink to Register \n(Hybrid session)\nhttps://nus-sg.zoom.us/meeting/register/tZAlc-2uqz4vGddG2dIGCNr8NaOpCl8Wp_Xw\n\n\nTitle\nOn-Demand Ride-Matching in a Spatial Model with Abandonment and Cancellation\n\n\nAbstract\nRide-hailing platforms such as Uber\, Lyft\, and DiDi coordinate supply and demand by matching passen- gers and drivers. The platform has to promptly dispatch drivers when receiving requests\, since otherwise passengers may lose patience and abandon the service by switching to alternative transportation methods. However\, having less idle drivers results in a possible lengthy pick-up time\, which is a waste of system capacity and may cause passengers to cancel the service after they are matched. Due to complex spatial and queueing dynamics\, the analysis of the matching decision is challenging. In this paper\, we propose a spatial model to approximate the pick-up time based on the number of waiting passengers and idle drivers. We analyze the dynamics of passengers and drivers in a queueing model where the platform can control the matching process by setting a threshold on the expected pick-up time. Applying fluid approximations\, we obtain accurate performance evaluations and an elegant optimality condition\, based on which we propose a policy that adapts to time-varying demand.
URL:https://iora.nus.edu.sg/events/dao-iora-joint-seminar-zhang-jiheng/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221111T100000
DTEND;TZID=Asia/Singapore:20221111T113000
DTSTAMP:20260417T103405
CREATED:20221101T081611Z
LAST-MODIFIED:20221107T010932Z
UID:16407-1668160800-1668166200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Long Zhao
DESCRIPTION:Long Zhao is an assistant professor in the Department of Analytics & Operations (DAO) at NUS Business School\, National University of Singapore. He received his Ph.D. in Decision Sciences from the McCombs School of Business at the University of Texas at Austin. Dr. Zhao’s research interests lie in data-driven decision-making. His research has been the finalist of INFORMS Data mining best paper competition and the finance section best student paper competition. \n  \n\n\n\nVenue \nHon Sui Sen Memorial Library – HSS 4-5\n\n\nLink to Register \n(Hybrid session)\nhttps://nus-sg.zoom.us/meeting/register/tZcsd–vqT4pGtaBggmJWaMKIuN6uTstODTJ\n\n\nTitle\nConstructing Quantiles via Forecast Errors: Theory and Empirical Evidence\n\n\nAbstract\nProbabilistic forecasts (such as quantiles) are essential inputs to decision-making in the face of uncertainty. However\, the most common type often comes in the form of point forecasts. As such\, it is necessary for the decision maker to construct uncertainty measures around the obtained point forecasts. One simple approach proposed in the literature suggests leveraging historical forecast errors to create quantile estimators around the given point forecast (referred to as the E2Q method). The sample quantile and normal approximation are two popular E2Q estimators. The former relies on the empirical distribution of the forecast errors while the latter treats the underlying distribution as if it were normal. Despite their prevalence\, the relative performances of the two estimators remain unknown. In this paper\, we find that the performance of a quantile estimator depends on its bias and variance. In particular\, higher variance always leads to worse performance. Furthermore\, unbiasedness is never optimal for a fixed variance and becomes less and less appealing as variance increases. Thus\, as an asymptotically unbiased estimator\, the sample quantile is appealing only when its variance is small. We confirm our theoretical findings using the M5 forecast competition data. Since this competition consists of both the “accuracy” (point) and “uncertainty” (quantile) tracks\, we also compare the E2Q method with other methods that directly forecast quantiles. We found that the E2Q method using the top point forecasts can outperform the top direct quantile forecasts. This empirical finding suggests that the E2Q method can be a promising alternative to forecasting quantiles directly.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-long-zhao/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221125T100000
DTEND;TZID=Asia/Singapore:20221125T113000
DTSTAMP:20260417T103405
CREATED:20221101T081722Z
LAST-MODIFIED:20221123T011119Z
UID:16409-1669370400-1669375800@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - He Wang
DESCRIPTION:He Wang is an Assistant Professor and Colonel John B. Day Early Career Professor in the School of Industrial and Systems Engineering at Georgia Tech. His research interests include pricing and revenue management\, supply chain\, transportation\, and machine learning. His works have received 1st place in INFORMS Junior Faculty Interest Group paper competition\, Best Paper in Operation Research Award by the MSOM Society\, NSF CAREER Award\, and faculty research awards from Amazon and Didi. \n  \n\n\n\nVenue \nSeminar Room at Innovation 4.0 building (Level 1)\n\n\nLink to Register \n(Hybrid session)\nhttps://nus-sg.zoom.us/meeting/register/tZwudemurj4sHdy-N4Bl9b1CoBEnCO0vZ7mw\n\n\nTitle\nConstant Regret Re-solving Heuristics for Revenue Management Problems\n\n\nAbstract\nWe will discuss a classic network revenue management model of Gallego and van Ryzin (1997)\, which considers a retailer who sells a product (or multiple products) subject to initial inventory constraints over T consecutive periods. Because the optimal policy via dynamic programming is computationally intractable\, researchers have proposed various approximate policies for this problem. We are interested in the so-called “re-solving heuristic\,” which periodically solves the fluid approximation model. In the quantity-based revenue management setting with discrete types (joint work with P. Bumpensanti)\, we find that the re-solving heuristic has a worst-case regret of O(T^{1/2}) compared to the optimal policy\, whereas a modified re-solving heuristic can achieve uniformly bounded O(1) regret. In the price-based revenue management setting with continuous price sets (joint work with Yining Wang)\, we show that the re-solving heuristic attains O(1) regret compared to the value of the optimal policy. This improves the O(lnT) regret upper bound established by Jasin (2014). In addition\, we prove that there is an Ω(lnT) gap between the value of the optimal policy and that of the fluid model\, implying that the fluid model is not an adequate benchmark for constant regret.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-he-wang/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221206T100000
DTEND;TZID=Asia/Singapore:20221206T113000
DTSTAMP:20260417T103405
CREATED:20220812T034841Z
LAST-MODIFIED:20221205T061819Z
UID:15978-1670320800-1670326200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Mika Sumida
DESCRIPTION:Mika Sumida is an Assistant Professor of Data Sciences and Operations in the Marshall School of Business at the University of Southern California. Her research focuses on developing efficient\, provably good algorithms for revenue management and resource allocation problems\, with applications in the sharing economy\, online marketplaces\, and delivery systems. She holds a Ph.D. in Operations Research and Information Engineering from Cornell University\, and a B.A. from Yale University. Prior to her Ph.D.\, she spent two years working in operations consulting at Analytics Operations Eng.\, Inc. \n\n\n\nLink to Register \n(Hybrid session)\nhttps://nus-sg.zoom.us/meeting/register/tZctcuGvqDksE9HugB0-sYRJ-g5nksO-uEaG\n\n\nTitle\nRevenue Management with Heterogeneous Resources\n\n\nVenue\nBIZ 1 – 0204\n\n\nAbstract\nWe study revenue management problems with heterogeneous resources\, each with unit capacity. An arriving customer makes a booking request for a particular interval of days in the future. We offer an assortment of resources in response to each booking request. The customer makes a choice within the assortment to use the chosen resource for her desired interval of days. The goal is to find a policy that determines an assortment of resources to offer to each customer to maximize the total expected revenue over a finite selling horizon. The problem has two useful features. First\, each resource is unique with unit capacity. Second\, each customer uses the chosen resource for a number of consecutive days. We consider static policies that offer each assortment of resources with a fixed probability. We show that we can efficiently perform rollout on any static policy\, allowing us to build on any static policy and construct an even better policy. Next\, we develop two static policies\, each of which is derived from linear and polynomial approximations of the value functions. We give performance guarantees for both policies\, so the rollout policies based on these static policies inherit the same guarantee. Lastly\, we develop an approach for computing an upper bound on the optimal total expected revenue. Our results for efficient rollout\, static policies\, and upper bounds all exploit the aforementioned two useful features of our problem. We use our model to manage hotel bookings based on a dataset from a real-world boutique hotel\, demonstrating that our rollout approach can provide remarkably good policies and our upper bounds can significantly improve those provided by existing techniques.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-mika-sumida/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221209T140000
DTEND;TZID=Asia/Singapore:20221209T150000
DTSTAMP:20260417T103405
CREATED:20221201T075852Z
LAST-MODIFIED:20221201T080015Z
UID:16468-1670594400-1670598000@iora.nus.edu.sg
SUMMARY:DAO-IORA joint seminar: Ethan X. Fang
DESCRIPTION:Ethan X. Fang is an Assistant Professor of Biostatistics & Bioinformatics at Duke Medical School and affiliated with Decision Sciences of Fuqua Business School and Rhodes Information Initiative at Duke University. He works on different data science problems from computational and statistical perspectives. Before joining Duke\, he was an assistant professor of Statistics at Penn State. He got his PhD from Princeton University under the direction of Han Liu and Robert Vanderbei\, and got his Bachelor’s degree from National University of Singapore under the direction of Kim-Chuan Toh. His works have appeared at top venues in different areas such as statistics\, optimization\, machine learning\, and operations research. He received 2016 Best Paper Prize in Continuous Optimization for Young Researchers (1 paper selected in 3 years). \n  \n\n\n\nVenue \nBIZ1 – 0204\n\n\nLink to Register\nhttps://forms.office.com/r/WgcFdUQQYW\n\n\nTitle\nInference for Ranking Problems\n\n\nAbstract\nWe propose a novel combinatorial inference framework to conduct general uncertainty quantification in ranking problems. We consider the widely adopted Bradley-Terry-Luce (BTL) model\, where each item is assigned a positive preference score that determines the Bernoulli distributions of pairwise comparisons’ outcomes. Our proposed method aims to infer the general ranking properties of the BTL model. The general ranking properties include the “local” properties such as if an item is preferred over another and the “global” properties such as if an item is among the top K-ranked items. We further generalize our inferential framework to multiple testing problems where we control the false discovery rate (FDR) and apply the method to infer the top-K ranked items. We also derive the information-theoretic lower bound to justify the minimax optimality of the proposed method. We conduct extensive numerical studies using both synthetic and real data sets to back up our theory.\n\n\n\nYou may contact WONG Cecilia/TAN Dorothy at 6516 6225/6516 3067 for enquiries.
URL:https://iora.nus.edu.sg/events/dao-iora-joint-seminar-ethanfang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221213T100000
DTEND;TZID=Asia/Singapore:20221213T113000
DTSTAMP:20260417T103405
CREATED:20220812T035011Z
LAST-MODIFIED:20221211T084251Z
UID:15982-1670925600-1670931000@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Saif Benjaafar
DESCRIPTION:Saif Benjaafar is McKnight Presidential Endowed Professor and Distinguished McKnight University Professor at the University of Minnesota. He is Head of the Department of Industrial & Systems Engineering at the University of Minnesota\, where he also directs the Initiative on the Sharing Economy. He is a founding member of the Singapore University of Technology and Design where he served as Head of Engineering Systems and Design. He is the Editor in Chief of the INFORMS journal Service Science. He serves on the board of directors of Hourcar\, a social car sharing organization. His research is in the area of operations broadly defined\, with a current focus on sustainable operations and innovation in business models\, including sharing economy\, on-demand services\, and digital marketplaces. He is a Fellow of INFORMS and IISE. \n\n\n\nVenue \nBIZ 1 – 0304\n\n\nLink to Register \n(Hybrid session)\nhttps://nus-sg.zoom.us/meeting/register/tZEqdumsqTIqHdynlcuEWYlO-g6cmUD5yg1t\n\n\nTitle\nDimensioning and Pricing of Shared Vehicle Networks\n\n\nAbstract\nIn the first part of the talk\, we consider the problem of optimal fleet sizing (dimensioning) in an on-demand and one way vehicle sharing system. We leverage a property of closed queueing networks that relates the dynamics of a network with K items to one with K-1 items to obtain explicit and closed form lower and upper bounds on the optimal number of vehicles that are asymptotically exact. We use the bounds to show that buffer capacity (capacity in excess of the nominal load) can be expressed in terms of three explicit terms that can be interpreted as follows: (1) standard buffer capacity that is protection against randomness in demand and rental times\, (2) buffer capacity that is protection against vehicle roaming\, and (3) a correction term. We show that the capacity needed to buffer against vehicle roaming can be substantial even in systems with vanishingly small demand. In the second part of the talk\, give a fixed fleet size\, we consider the dynamic pricing in such a network and show that a static pricing policy that arises from solving a maximum flow relaxation of the problem guarantees a performance ratio of order 1- O(1/) where K is the number of vehicles. The approach used\, which leverages the same property of closed queueing networks used in the dimensioning problem\, is startingly simple and yields performance guarantees that are tighter than those previously obtained in the literature. Time permitting\, we will also discuss ongoing work that considers dimensioning and pricing in other settings with spatial queueing network features. \n  \n(The talk will draw on material from the following two papers: \nhttps://pubsonline.informs.org/doi/epdf/10.1287/mnsc.2021.3957\, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3998297 and \nhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=4130757. \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-saif-benjaafar/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230127T100000
DTEND;TZID=Asia/Singapore:20230127T113000
DTSTAMP:20260417T103405
CREATED:20221101T081834Z
LAST-MODIFIED:20230110T031345Z
UID:16411-1674813600-1674819000@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Ville Satopaa
DESCRIPTION:TBA
URL:https://iora.nus.edu.sg/events/iora-seminar-series-ville-satopaa/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230207T100000
DTEND;TZID=Asia/Singapore:20230207T113000
DTSTAMP:20260417T103405
CREATED:20230110T031633Z
LAST-MODIFIED:20230130T034859Z
UID:16647-1675764000-1675769400@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Timothy Chan
DESCRIPTION:Timothy Chan is the Associate Vice-President and Vice-Provost\, Strategic Initiatives at the University of Toronto\, the Canada Research Chair in Novel Optimization and Analytics in Health\, a Professor in the department of Mechanical and Industrial Engineering\, and a Senior Fellow of Massey College. His primary research interests are in operations research\, optimization\, and applied machine learning\, with applications in healthcare\, medicine\, sustainability\, and sports. He received his B.Sc. in Applied Mathematics from the University of British Columbia (2002)\, and his Ph.D. in Operations Research from the Massachusetts Institute of Technology (2007). Before coming to Toronto\, he was an Associate in the Chicago office of McKinsey and Company (2007-2009)\, a global management consulting firm. During that time\, he advised leading companies in the fields of medical device technology\, travel and hospitality\, telecommunications\, and energy on issues of strategy\, organization\, technology and operations. \n  \n\n\n\nName of Speaker\nTimothy Chan\n\n\nSchedule\nTuesday 7 February 2023\, 10am – 1130am\n\n\nVenue \nBIZ2 4-13A (BIZ 2\, level 4 Seminar Room)\n\n\nLink to Register \n(Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZcpdO2tpz8jEt3qhGZYuJ2GhZEuzV8r3skw\n\n\nTitle\nGot (optimal) milk?\n\n\nAbstract\nHuman donor milk is considered the ideal nutrition for millions of infants that are born preterm each year. Donor milk is collected\, processed\, and distributed by milk banks. The macronutrient content of donor milk is directly linked to infant brain development and can vary substantially across donations\, which is why multiple donations are typically pooled together to create a final product. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content of donor milk\, which means pooling is done heuristically. We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of deposits and then optimally combine them in pools\, respectively. In collaboration with our partner milk bank\, we collect a data set of milk to train our predictive models. We rigorously simulate milk bank practices to fine-tune our optimization models and evaluate operational scenarios such as changes in donation habits during the COVID-19 pandemic. Finally\, we conduct a year-long trial implementation\, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets between 31% to 76% more often than the baseline\, while taking 67% less recipe creation time. This is the first paper in the broader blending literature that combines machine learning and optimization. We demonstrate that such pipelines are feasible to implement and can yield significant improvements over current practices. Our insights can guide practitioners in any application area seeking to implement machine learning and optimization-based decision-support.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-timothy-chan/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230210T100000
DTEND;TZID=Asia/Singapore:20230210T113000
DTSTAMP:20260417T103405
CREATED:20230110T031729Z
LAST-MODIFIED:20230213T021652Z
UID:16650-1676023200-1676028600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Antoine Désir
DESCRIPTION:Antoine is an Assistant professor of Technology and Operations Management at INSEAD. His research applies mathematical modeling and analytics to operations management problems with an aim to: (1) quantify fundamental tradeoffs\, and (2) design efficient data-driven algorithms to support operational decisions. More precisely\, he focuses on revenue management and choice modeling with applications such as online advertising. He was an MSOM student paper finalist in 2014 and 2017 and a Nicholson student paper finalist in 2014 and 2015. He spent a year as a post-doctoral researcher at Google NYC. \n\n\n\nName of Speaker\nAntoine Desir\n\n\nSchedule\nFriday 10 February 2023\, 10.00am – 11.30am\n\n\nVenue \nI4-01-03 (Innovation 4.0\, level 1 Seminar Room)\n\n\nLink to Register \n(Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZwtdOyoqjIuGtIP444shDdv-KQrbeOQcFl3\n\n\nTitle\nRepresenting Random Utility Choice Models with Neural Networks\n\n\nAbstract\nMotivated by the successes of deep learning\, we propose a class of neural network-based discrete choice models\, called RUMnets\, which is inspired by the random utility maximization (RUM) framework. This model formulates the agents’ random utility function using the sample average approximation (SAA) method. We show that RUMnets sharply approximate the class of RUM discrete choice models: any model derived from random utility maximization has choice probabilities that can be approximated arbitrarily closely by a RUMnet. Reciprocally\, any RUMnet is consistent with the RUM principle. We derive an upper bound on the generalization error of RUMnets fitted on choice data\, and gain theoretical insights on their ability to predict choices on new\, unseen data depending on critical parameters of the dataset and architecture.  By leveraging open-source libraries for neural networks\, we find that RUMnets outperform other state-of-the-art choice modeling and machine learning methods by a significant margin on two real-world datasets. This is joint work with Ali Aouad (LBS). \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-antoine-desir/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230303T100000
DTEND;TZID=Asia/Singapore:20230303T113000
DTSTAMP:20260417T103405
CREATED:20230209T084917Z
LAST-MODIFIED:20230227T024024Z
UID:16703-1677837600-1677843000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series – Jing Wu
DESCRIPTION:Prof. Jing Wu is an Associate Professor in the Department of Decision Sciences and Managerial Economics at the Chinese University of Hong Kong (CUHK) Business School. He receives his Ph.D. (major in operations management\, minor in economics & finance) and MBA from the University of Chicago Booth School of Business and his bachelor’s degree in Electronic Engineering from Tsinghua University. Prof. Wu’s primary research fields are the operations-finance interface\, global supply chains\, FinTech\, and business intelligence. His papers are published in leading journals such as Management Science\, M&SOM\, and POMS. His articles appear in business magazines such as MIT Sloan Management Review\, the Economist\, and Forbes. In particular\, his quantitative research findings on the supply chain impact of COVID-19 and the Trade War have been reported by over 400 media outlets in over 20 countries worldwide. He is a Senior Editor for Production and Operations Management and serves on the Editorial Board for Journal of Operations Management. He has been a committee/track chair for leading international academic conferences such as INFORMS and POMS meetings. Before academia\, he worked as a quantitative strategist at Deutsche Bank New York. \n\n\n\nName of Speaker\nJing Wu\n\n\nSchedule\nFriday 3 March 2023\, 10.00am – 11.30am\n\n\nVenue \nI4-01-03 (Innovation 4.0\, level 1 Seminar Room)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZYkc-yurjsuGtSdKCtZD62yxhugAKzE-6Nh\n\n\nTitle\nThe Golden Revolving Door: Hedging through Hiring Government Officials\n\n\nAbstract\nUsing both the onset of the US-China trade war in 2018 and the most recent Russia-Ukraine conflict and associated trade tensions\, we show that government-linked firms increase their importing activity by roughly 33% (t=4.01) following the shock\, while non-government linked firms trading to the same countries do the opposite\, decreasing activity. These increases appear targeted\, in that we see no increase for government-linked supplier firms generally to other countries (even countries in the same regions) at the same time\, nor of these same firms in these regions at other times of no tension. In terms of mechanism\, government supplier-linked firms are nearly twice as likely to receive tariff exemptions as equivalent firms doing trade in the region who are not also government suppliers. More broadly\, these effects are increasing in level of government connection. For example\, firms that are geographically closer to the agencies to which they supply increase their imports more acutely. Using micro-level data\, we find that government supplying firms that recruit more employees with past government work experience also increase their importing activity more – particularly when the past employee worked in a contracting role. Lastly\, we find evidence that this results in sizable accrued benefits in terms of firm-level profitability\, market share gains\, and outsized stock returns.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-jing-wu/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230317T100000
DTEND;TZID=Asia/Singapore:20230317T113000
DTSTAMP:20260417T103405
CREATED:20230220T060513Z
LAST-MODIFIED:20230309T050139Z
UID:16733-1679047200-1679052600@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series – Karthyek Murthy
DESCRIPTION:Karthyek Murthy serves as an Assistant Professor in Singapore University of Technology & Design. His research interests lie in data-driven operations research. Prior to joining SUTD\, he was a postdoctoral researcher in Columbia University IEOR department. His research has been recognised with 2021 INFORMS Junior Faculty Forum (JFIG) Paper competition (Third place)\, 2019 WSC Best Paper Award\, and IBM PhD fellowship. Karthyek serves as an Associate Editor for the INFORMS journal Stochastic Systems and as a judge for the INFORMS Nicholson student paper competition. \n\n\n\nName of Speaker\nKarthyek Murthy\n\n\nSchedule\nFriday 17 March 2023\, 10.00am – 11.30am\n\n\nVenue \nI4-01-03 (Innovation 4.0\, level 1 Seminar Room)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0pd-mrqT8sHdHKwZT0EH5wYD4D-WJxsVNx\n\n\nTitle\nLocally robust models for optimization under tail-based data imbalance\n\n\nAbstract\nMany problems in operations and risk management require the familiar “estimate\, then optimize” workflow involving a model estimation from data in the first step before plugging in the trained model to solve various optimization tasks. In this talk\, we first give the ingredients for constructing locally robust optimization formulations in which the first step model estimation has no effect\, locally\, on the optimal solutions. Then delving specifically into optimization problems affected by tail-based data imbalance\, we show that this local sensitivity translates to improved out-of-sample performance freed from the first-order impact of model errors caused by model selection and misspecification biases that are especially difficult to avoid when performing estimation with imbalanced data. The key ingredient in achieving this local robustness is a novel debiasing procedure that adds a non-parametric bias correction term to the objective. The debiased objective retains convexity\, and the imputation of the correction term relies only on a non-restrictive large deviations behavior conducive for transferring knowledge from representative data-rich regions to the datascarce tail regions suffering from imbalance. The bias correction gets determined by the extent of model error in the estimation step and the specifics of the stochastic program in the optimization step\, thereby serving as a scalable “smart-correction” step bridging the disparate goals in estimation and optimization. Besides showing the empirical effectiveness of the proposed formulation in real datasets\, the numerical experiments bring out the utility of locally robust solutions in tackling model errors and shifts in distribution between training and deployment.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-karthyek-murthy/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230323T100000
DTEND;TZID=Asia/Singapore:20230323T113000
DTSTAMP:20260417T103405
CREATED:20230110T032726Z
LAST-MODIFIED:20230321T023308Z
UID:16655-1679565600-1679571000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series – Saed Alizamir
DESCRIPTION:Saed Alizamir is an Associate Professor of Operations Management at Yale School of Management. He joined Yale in 2013 after receiving a PhD in Decision Sciences from Duke University’s Fuqua School of Business. Professor Alizamir’s research interests lies in the area of social responsibility and public sector operations. In his research\, he focuses  on problems in public policy that involve private-public interactions and dynamic decision-making. The goal of his research is to provide normative recommendations that inform better policy decisions\, especially in areas where not enough data exists to run full-fledged empirical studies. He has worked on government subsidy instruments in renewable energy industry and electric vehicle markets\, agricultural supply chains\, demand management in residential electricity sector\, and optimal control of diagnostic systems such as nurse triage. In 2021\, Professor Alizamir was named as one of the World’s Best 40 Under 40 Business School Professors by Poets & Quants. He serves as an Associate Editor for the Operations Research journal\, and co-chaired the M&SOM cluster for the INFORMS conference in 2019. At Yale\, he teaches MBA level courses in core Operations\, Managing Sustainable Operations\, and Quantitative Decision Models. \n\n\n\nName of Speaker\nSaed Alizamir\n\n\nSchedule\nThursday 23 March 2023\, 10am – 11.30am\n\n\n Venue \nI4-01-03 Seminar Room\n\n\n Registration Link\nhttps://nus-sg.zoom.us/meeting/register/tZMkfuuurTwjGdfG2nYqcDCFI8vVGYD-qMR0\n\n\n Title\nSearch Delegation Policies for Compliance Enforcement\n\n\nAbstract\n  \nEnforcement of regulatory compliance over time often involves intermittent search in the form of inspection in order to reveal the compliance state of the regulated entity. To enable cost-effective enforcement of environmental compliance standards\, regulatory agencies encourage production firms to voluntarily discover and correct compliance violations. Although such self-regulation activities often bring desired benefits\, they create nontrivial challenges. To study this tradeoff\, we develop a model that captures the interactions between a regulator and a firm that unfold over time. Because constant monitoring is prohibitive\, the regulator and the firm perform costly inspections to discover the compliance state of production. If the regulator detects noncompliance\, the firm is required to pay penalty and restore compliance. To avoid penalty\, the firm performs self-inspections to preemptively detect noncompliance and restore compliance without reporting the action to the regulator. We show that inefficiency caused by the firm’s private action is amplified if the regulator adopts a policy of requiring permanent restoration. Under such a policy\, the firm’s self-inspections may leave the regulator and the environment worse off. By contrast\, self-inspections always bring a net benefit to the regulator if repeated temporary restorations are allowed. We also find that\, due to self-inspections\, a paradoxical situation arises where the regulator prefers mandating permanent restoration despite having a small chance of enforcing it.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-saed-alizamir/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230331T100000
DTEND;TZID=Asia/Singapore:20230331T113000
DTSTAMP:20260417T103405
CREATED:20230209T085028Z
LAST-MODIFIED:20230322T084550Z
UID:16706-1680256800-1680262200@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series – Philip Zhang
DESCRIPTION:Renyu (Philip) Zhang has been an Associate Professor (with tenure) at the Department of Decision Sciences and Managerial Economics\, The Chinese University of Hong Kong Business School since September 2022. He is also an economist and Tech Lead at Kwai\, one of the world’s largest online video-sharing and live-streaming platforms. Philip’s recent research focuses on developing data science methodologies (e.g.\, data-driven optimization\, causal inference\, and machine learning) to evaluate and optimize the operations strategies in the contexts of online platforms and marketplaces\, sharing economy\, and social networks\, especially their recommendation\, advertising\, pricing\, and matching policies. His research works have appeared in top business journals such as Management Science\, Operations Research\, and Manufacturing & Service Operations Management\, and have been recognized by various research awards of the INFORMS and POMS communities. His research projects have been funded by various funding agencies including HK RGC\, NSFC\, SMEC\, and STCSM.  Philip serves as a Senior Editor for Production and Operations Management\, and an Associate Editor for Naval Research Logistics. He has also developed data science and economics frameworks to evaluate and optimize the user growth strategy and the platform ecosystem of Kwai. Prior to joining CUHK\, Philip was an Assistant Professor of Operations Management at New York University Shanghai between 2016 and 2022. Please visit Philip’s personal website for more about him: https://rphilipzhang.github.io/rphilipzhang/ \n\n\n\nName of Speaker\nZhang Renyu\, Philip\n\n\nSchedule\nFriday 31 March 2023\, 10.00am – 11.30am\n\n\nVenue \nI4-01-03 (Innovation 4.0\, level 1 Seminar Room)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZEtduirqT4iG9L-B7cNDO-sjXX8YFw9Csoz\n\n\nTitle\nDeep Learning Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence\n\n\nAbstract\nLarge-scale online platforms launch hundreds of randomized experiments (a.k.a. A/B tests) every day to iterate their operations and marketing strategies\, while the combinations of these treatments are typically not exhaustively tested. It triggers an important question of both academic and practical interests: Without observing the outcomes of all treatment combinations\, how to estimate the causal effect of any treatment combination and identify the optimal treatment combination? We develop a novel framework combining deep learning and doubly robust estimation to estimate the causal effect of any treatment combination for each user on the platform when observing only a small subset of treatment combinations. Our proposed framework (called debiased deep learning\, DeDL) exploits Neyman orthogonality and combines interpretable and flexible structural layers in deep learning. We prove theoretically that this framework yields efficient\, consistent\, and asymptotically normal estimators under mild assumptions\, thus allowing for identifying the best treatment combination when only observing a few combinations. To empirically validate our method\, we then collaborate with a large-scale video-sharing platform and implement our framework for three experiments involving three treatments where each combination of treatments is tested. When only observing a subset of treatment combinations\, our DeDL approach significantly outperforms other benchmarks to accurately estimate and infer the average treatment effect (ATE) of any treatment combination\, and to identify the optimal treatment combination.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-philip-zhang/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230428T100000
DTEND;TZID=Asia/Singapore:20230428T113000
DTSTAMP:20260417T103405
CREATED:20230110T032935Z
LAST-MODIFIED:20230405T020705Z
UID:16659-1682676000-1682681400@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series – Javad Nasiry
DESCRIPTION:Javad Nasiry is a professor of Operations Management at McGill’s Desautels Faculty of Management where he joined in 2019.  He is the director of Sustainable Growth Initiative (SGI) which is a cross-faculty initiative to mobilize the talent and expertise within McGill University to help businesses move towards more socially and environmentally sustainable business models. \nHis main research interests are in behavioural operations\, supply chain management\, sustainability\, retail operations\, and operations-marketing interface.  His research in sustainable operations focuses on the environmental consequences of new business models in apparel\, renewable energy\, and agriculture industries. \nPrior to joining McGill\, he was an associate professor of operations management in the School of Business and Management at the Hong Kong University of Science and Technology (HKUST) where he joined in 2010. \n\n\n\nName of Speaker\nJavad Nasiry\n\n\nSchedule\nFriday 28 April 2023\, 10.00am – 11.30am\n\n\nVenue \nI4-01-03 (Innovation 4.0\, level 1 Seminar Room)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZEsfuihqDgqHNL63FOwX-SW-XU0E_mfDTdE\n\n\nTitle\nSustainability in the fast fashion industry\n\n\nAbstract\nWe establish a much-needed link between the fast fashion business model and its environmental consequences. A fast fashion system allows firms to react quickly to changing consumer demand by replenishing inventory (via quick response) and introducing more fashion styles. We study the environmental impact of the fast fashion business model by analyzing its implications for product quality\, variety\, and inventory decisions. We find that a key driver of low product quality in the fast fashion industry is the firm’s incentive to offer variety to hedge against uncertain fashion trends. When variety is endogenous\, quality decreases as consumers become more sensitive to fashion or as the cost of introducing new styles decreases. \nWe assess the effectiveness of three environmental initiatives (waste disposal regulations\, consumer education\, and production tax schemes) in countering the environmental impact of fast fashion.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-zhengyuan-zhou/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230512T100000
DTEND;TZID=Asia/Singapore:20230512T113000
DTSTAMP:20260417T103405
CREATED:20230502T054823Z
LAST-MODIFIED:20230503T125438Z
UID:16850-1683885600-1683891000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Chun So Yeon
DESCRIPTION:So Yeon Chun is an Associate Professor of Technology & Operations Management at INSEAD. So Yeon’s data-driven research focuses on the interface between operations and marketing in consumer loyalty reward programs (consumer choices\, point currencies and monetization\, customer lifetime values\, and consumer behavior experiments)\, revenue management (pricing and forecasting)\, and risk management (risk measures\, portfolio optimization) with applications in industries such as retail\, transportation\, finance\, and hospitality. So Yeon holds a PhD in Operations Research and an MS in Applied Statistics from the School of Industrial and Systems Engineering at the Georgia Institute of Technology. \n\n\n\nName of Speaker\nChun So Yeon\n\n\nSchedule\nFriday 12 May 2023\, 10.00am – 11.30am\n\n\nVenue \nBIZ1-0202\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0vdO-sqDwvHtaug9Pj_1n9e035FOi3xDX-\n\n\nTitle\nPoint vs. Money: Monetization of Loyalty Currency and Consumer Payment Choice Behavior\n\n\nAbstract\nIn recent years\, companies have made various changes to the design and operational management of their loyalty programs to further monetize their virtual currency\, or points\, by making them more like money. However\, it remains unclear whether consumers treat points as they treat money when deciding whether to use them to pay for a purchase. In this talk\, we aim to address this question and explore how and why consumers spend loyalty points differently than money. In the first part of the talk\, we investigate the important factors that influence consumers’ decisions to pay with either points or money\, and we examine how a co-branded credit card partnership affects consumers’ attitudes toward point currency. We develop a model of consumers’ payment decisions and estimate it using proprietary transaction data from a major US airline company through a hierarchical Bayesian framework. In the second part of the talk\, we present a series of behavioral experiments that focus on the design and operation of the exchange rate between points and money.  We examine the behavioral bias that consumers exhibit toward point currency and explore whether the effects of the exchange rate design are unique to loyalty currencies or more generally apply to other foreign currencies.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-chun-so-yeon/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230804T100000
DTEND;TZID=Asia/Singapore:20230804T113000
DTSTAMP:20260417T103405
CREATED:20230802T041735Z
LAST-MODIFIED:20230802T041815Z
UID:17167-1691143200-1691148600@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Bar Light
DESCRIPTION:Bar Light is an assistant professor in the Department of Statistics and Operations Research in Tel Aviv University’s School of Mathematics. Bar was previously a Postdoctoral Researcher at Microsoft Research focusing on market design and designing ad-auctions. Bar obtained a PhD in Operations Research from Stanford university. His research mainly focuses on market design for platforms\, the analysis of large markets and systems\, and dynamic optimization. \n\n\n\nName of Speaker \nBar Light\n\n\nSchedule \n4 August 2023\, 10.00am – 11.30am\n\n\nVenue  \nBIZ1-0202\n\n\nLink to Register \nhttps://nus-sg.zoom.us/meeting/register/tZYkcOCgqTwrGNaBBd0TAVKzv8j4hP53YiPw\n\n\nTitle \nBudget Pacing in Repeated Auctions: Regret and Efficiency without Convergence\n\n\nAbstract \nWe study the aggregate welfare and individual regret guarantees of dynamic pacing algorithms in the context of repeated auctions with budgets. Such algorithms are commonly used as bidding agents in Internet advertising platforms. We show that when agents simultaneously apply a natural form of gradient-based pacing\, the liquid welfare obtained over the course of the learning dynamics is at least half the optimal expected liquid welfare obtainable by any allocation rule. Crucially\, this result holds without requiring convergence of the dynamics\, allowing us to circumvent known complexity-theoretic obstacles of finding equilibria. This result is also robust to the correlation structure between agent valuations and holds for any core auction\, a broad class of auctions that includes first-price\, second-price\, and generalized second-price auctions. For individual guarantees\, we further show such pacing algorithms enjoy dynamic regret bounds for individual value maximization\, with respect to the sequence of budget-pacing bids\, for any auction satisfying a monotone bang-for-buck property.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-bar-light/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230810T100000
DTEND;TZID=Asia/Singapore:20230810T113000
DTSTAMP:20260417T103405
CREATED:20230807T074110Z
LAST-MODIFIED:20230807T074316Z
UID:17206-1691661600-1691667000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Michelle Wu
DESCRIPTION:Michelle Xiao Wu is a Co-Director in the Data Science Lab at MIT Institute for Data\, Systems\, and Society (IDSS). Before joining IDSS\, she was an Assistant Professor at Carson College of Business\, Washington State University. She received her Ph.D. (major in operations management\, minor in economics) and MBA from the University of Chicago Booth School of Business and an M.Sc degree in Physics from Northwestern University. \nHer research interests focus on operations management in the digital economy\, including pricing for e-commerce platforms\, digital content release\, and the sharing economy. Her other research interests include machine learning\, the operations-finance interface\, and supply chain management. Her papers are published in leading journals such as Management Science\, M&SOM\, JMIS\, and IJPR. \nIn her consulting experience with various companies\, she provides implementable methods and strategies to optimize operational decisions\, in manufacturing\, e-commerce\, and brick & mortar retail. \n\n\n\nName of Speaker\nMichelle Wu\n\n\nDate\n10 August 2023\, 10am – 11.30am\n\n\nVenue \nBIZ1-0206\n\n\nRegistration Link \nhttps://nus-sg.zoom.us/meeting/register/tZAlcuqgpz4iEtYiqIsqyAVij6uSyYM-4GWH\n\n\nTitle \nEmpowering Businesses with Data\, Analytics\, and Automation\n\n\nAbstract\nWe present our work with a global online fashion retailer\, Zalando\, as an example of how a global retailer can utilize massive amount of data to optimize price discount decisions over a large number of products in multiple countries on a weekly basis.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-michelle-wu/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230825T100000
DTEND;TZID=Asia/Singapore:20230825T113000
DTSTAMP:20260417T103405
CREATED:20230817T020701Z
LAST-MODIFIED:20230817T020701Z
UID:17387-1692957600-1692963000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Yan Zhenzhen
DESCRIPTION:Dr. Zhenzhen Yan is an assistant professor at School of Physical and Mathematical Sciences\, Nanyang Technological University. She joined SPMS since 2018. Before that\, she received her PhD in Management Science from the National University of Singapore\, and her BSc and MSc in Management Science\, Operations Research from the National University of Defense and Technology in China. Her research interests mainly focus on the interplay between optimization and data analytics. Her first line of research is to solve various operations management problems and engineering problems from the distributionally robust perspective\, including supply chain design and operations\, and healthcare operations. The second line is to develop data-driven optimization approaches with applications to e-commerce operations and resource allocation. Her work has been published in leading operations management journals including Management Science\, Operations Research\, MSOM and POMS\, and top AI conferences including Neurips\, UAI and AAAI. Her work has received media coverage in various outlets including the Straits Times and ScienceDaily etc. She currently serves as an Associate Editor of Decision Sciences. \n\n\n\nName of Speaker\nYan Zhenzhen\n\n\nSchedule\n25 August 2023\, 10am\n\n\nVenue  \nBIZ1 03-07\n\n\nRegistration Link (Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZUvcOCqrD4uG9eJgleNTFTjjnTADqikckwd\n\n\nTitle \nSample-Based Online Generalized Assignment Problem with Unknown Poisson Arrivals\n\n\nAbstract\nWe study an edge-weighted online stochastic Generalized Assignment Problem with unknown Poisson arrivals. We provide a sample-based multi-phase algorithm by utilizing both pre-existing offline data (named historical data) and sequentially revealed online data. The developed algorithm employs the concept of exploration-exploitation to dynamically learn the arrival rate and optimize the allocation decision. We establish its parametric performance guarantee measured by a competitive ratio. We further provide a guideline on fine tuning the parameters under different sizes of historical data based on the established parametric form. By analyzing a special case which is a classical online weighted matching problem\, we also provide a novel insight on how the historical data’s quantity and quality (measured by the number of underrepresented agents in the data) affect the trade-off between exploration and exploitation in online algorithms and their performance. Finally\, we demonstrate the effectiveness of our algorithms numerically.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-yan-zhenzhen/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230908T100000
DTEND;TZID=Asia/Singapore:20230908T113000
DTSTAMP:20260417T103405
CREATED:20230831T074219Z
LAST-MODIFIED:20230831T074455Z
UID:17443-1694167200-1694172600@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Yaron Shaposhnik
DESCRIPTION:Yaron Shaposhnik is an Assistant Professor of Information Systems and Operations Management at the Simon School of Business in the University of Rochester. Most broadly\, he is interested in the optimization and analysis of mathematical models that capture real world problems\, and in developing decision support tools that leverage analytics to improve operations. \n\n\n\nName of Speaker \nYaron Shaposhnik\n\n\nSchedule  \n8 September 2023\, 10am – 11.30am\n\n\nRegistration Link \nhttps://nus-sg.zoom.us/meeting/register/tZcsdu6vrzwsGdC8u6_NyrnGumir0pnyZY21\n\n\nTitle of Talk \nGlobally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation\n\n\nAbstract  \nWe develop a method for understanding specific predictions made by (global) predictive models by constructing (local) models tailored to each specific observation (these are also called “explanations” in the literature). Unlike existing work that “explains” specific observations by approximating global models in the vicinity of these observations\, we fit models that are globally-consistent with predictions made by the global model on past data. We focus on rule-based models (also known as association rules or conjunctions of predicates)\, which are interpretable and widely used in practice. We design multiple algorithms to extract such rules from discrete and continuous datasets\, and study their theoretical properties. Finally\, we apply these algorithms to multiple credit-risk models trained on the Explainable Machine Learning Challenge data from FICO and demonstrate that our approach effectively produces sparse summary-explanations of these models in seconds. Our approach is model-agnostic (that is\, can be used to explain any predictive model)\, and solves a minimum set cover problem to construct its summaries.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-yaron-shaposhnik/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230915T100000
DTEND;TZID=Asia/Singapore:20230915T113000
DTSTAMP:20260417T103405
CREATED:20230907T034153Z
LAST-MODIFIED:20230907T034223Z
UID:17474-1694772000-1694777400@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Teo Chung Piaw & Wang Quanmeng
DESCRIPTION:Name of speakers \nTeo Chung Piaw & Wang Quanmeng\n\n\nSchedule \n15 September 2023\, 10am – 11.30am\n\n\nVenue\nBIZ1 – 0205\n\n\nRegistration \nhttps://nus-sg.zoom.us/meeting/register/tZIofumopzgoGtUtQiqUDX1hBKnar2DU7wzJ\n\n\nTitle of talk\nLast mile innovations: The case of the Locker Alliance Network\n\n\nAbstract \nIn this talk\, we’ll explore a collection of academic research we’ve conducted\, funded by IMDA\, focusing on Singapore’s “Locker Alliance Network” (LAN). This government-led initiative aims to establish a network of public lockers in residential areas and community hubs to improve the efficiency of last-mile parcel deliveries. Our research tackles key operational questions\, such as the ideal density\, coverage\, and impact of the LAN. \nTo address these questions\, we’ve employed locker usage data from a commercial courier service to calibrate a model that gauges how walking distance and other variables influence customer preferences for locker pickups versus traditional home or office deliveries. Additionally\, we’ve created a facility location model that leverages existing parcel delivery data to optimize the LAN’s design. Contrary to traditional thinking\, our results indicate that peak parcel volume areas are not necessarily the best locations for lockers. Instead\, our model recommends an optimal coverage radius of 250 meters for the LAN in Singapore. One unique challenge we faced was the absence of home-office pair information for residents\, leading us to develop a new type of facility location model where the choice set is unknown. Our findings suggest that under realistic assumptions—namely\, that home delivery will always be more popular than locker pickup—the lack of this specific information has minimal impact on the performance of our locker facility location model. \nIn related research\, we’ve also examined the LAN’s effects on routing efficiency and conducted empirical tests to understand how exposure and popularity influence adoption choices. We also discuss how the challenges in this public facility (that it is interoperable and used by many different LSPs) are partially addressed due to a “nested” pattern in the optimal solution to the facility location model.\n\n\nAbout the speakers \nChung Piaw Teo is Provost’s Chair Professor in NUS Business School and Executive Director of the Institute of Operations Research and Analytics (IORA) in the National University of Singapore\, and concurrently a co-director in the SIA-NUS Digital Aviation Corp Lab. With a focus on optimization and supply chain management\, Professor Teo is trying to bridge the gap between theoretical research and practical applications of OR and Analytics in business and engineering. \nHe was a fellow in the Singapore-MIT Alliance Program\, an Eschbach Scholar in Northwestern University (US)\, Professor in Sungkyunkwan Graduate School of Business (Korea)\, and a Distinguished Visiting Professor in YuanZe University (Taiwan). He is department editor for MS (Optimization)\, and a former area editor for OR (Operations and Supply Chains). He was elected Fellow of INFORMS and Chang Jiang Scholar (China) in 2019. He has also served on several international committees such as the Chair of the Nicholson Paper Competition (INFORMS\, US)\, member of the LANCHESTER and IMPACT Prize Committee (INFORMS\, US)\, Fudan Prize Committee on Outstanding Contribution to Management (China)\, and recently chaired the EIC search committee for Operations Research\, an INFORMS journal. \nQuanmeng Wang is a research fellow at Institute of Operations Research and Analytics\, where he also earned his PhD. His research mainly focus on model development for operation problems in logistics. He participated in several research projects collaborated with industry partner of IORA\, including a leading express company of China and a government public service sector of Singapore.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-teo-chung-piaw-wang-quanmeng/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230922T100000
DTEND;TZID=Asia/Singapore:20230922T113000
DTSTAMP:20260417T103405
CREATED:20230918T081639Z
LAST-MODIFIED:20230918T081639Z
UID:17770-1695376800-1695382200@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Alex Yang
DESCRIPTION:Name of speaker\nS. Alex Yang\n\n\nSchedule  \n22 September 2023\, 10am – 11.30am\n\n\nVenue \nI4-01-03 Seminar Room\n\n\nRegistration  \nhttps://nus-sg.zoom.us/meeting/register/tZ0sdOCuqzkoGNS7Zb8Byg3Kg8GrkOoxwvH8\n\n\nTitle of talk \nCrowd-judging on Two-sided Platforms: An Analysis of In-group Bias\n\n\nAbstract  \nDisputes over transactions on two-sided platforms are common and usually arbitrated through platforms’ customer service departments or third-party service providers. This paper studies crowd-judging\, a novel crowd-sourcing mechanism whereby users (buyers and sellers) volunteer as jurors to decide disputes arising from the platform. Using a rich dataset from the dispute resolution center at Taobao\, a leading Chinese e-commerce platform\, we aim to understand this innovation and propose and analyze potential operational improvements\, with a focus on in-group bias (buyer jurors favor the buyer\, likewise for sellers). Platform users\, especially sellers\, share the perception that in-group bias is prevalent and systematically sways case outcomes as the majority of users on such platforms are buyers\, undermining the legitimacy of crowd-judging. Our empirical findings suggest that such concern is not completely unfounded: on average\, a seller juror is approximately 10% likelier (than a buyer juror) to vote for a seller. Such bias is aggravated among cases that are decided by a thin margin\, and when jurors perceive that their in-group’s interests are threatened. However\, the bias diminishes as jurors gain experience: a user’s bias reduces by nearly 95% as their experience grows from zero to the sample-median level. Incorporating these findings and juror participation dynamics in a simulation study\, the paper delivers three managerial insights. First\, under the existing voting policy\, in-group bias influences the outcomes of no more than 2% of cases. Second\, simply increasing crowd size\, either through a larger case panel or aggressively recruiting new jurors\, may not be efficient in reducing the adverse effect of in-group bias. Finally\, policies that allocate cases dynamically could simultaneously mitigate the impact of in-group bias and nurture a more sustainable juror pool. \nLink to paper: https://pubsonline.informs.org/doi/10.1287/mnsc.2023.4818\n\n\nAbout the speaker\nS. Alex Yang is an Associate Professor of Management Science and Operations at London Business School. Alex holds a PhD and an MBA from the University of Chicago Booth School of Business\, an MS from Northwestern University\, and a BS from Tsinghua University. Alex’s primary research focus is on the interface of operations management and finance\, especially in trade credit\, supply chain finance\, and FinTech. His recent research focuses on platform governance and operations and value chain management and innovation. Alex’s research has appeared in academic journals in operations and finance\, such as Management Science\, M&SOM\, and Journal of Financial Economics\, and has received several best paper awards. He is the associate editor of several academic journals. An award-winning teacher\, Alex has taught on the MBA\, EMBA\, and executive education programmes in universities and business schools around the world. Beyond research and teaching\, Alex has working and consulting experience in banks\, Fintech and technology companies\, hedge funds\, airlines\, and international organizations. \nhttps://www.london.edu/faculty-and-research/faculty-profiles/y/yang-s \nhttps://salexyang.com
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-alex-yang/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20231006T100000
DTEND;TZID=Asia/Singapore:20231006T113000
DTSTAMP:20260417T103405
CREATED:20231003T143351Z
LAST-MODIFIED:20231003T143351Z
UID:18075-1696586400-1696591800@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Mabel Chou\, Sun Qinghe\, Li Wei
DESCRIPTION:Name of speakers \nMabel C. Chou\, Sun Qinghe\, Li Wei  \n\n\nSchedule  \n6 October 2023\, 10am – 11.30am  \n\n\nVenue  \nHon Sui Sen Memorial Library\, Seminar Room 4-7 \n\n\nZoom link  \nhttps://nus-sg.zoom.us/meeting/register/tZAlcu2pqzkvHNA_ldzyBArQnujX1H_xk8Tr  \n\n\nTitle of talk \nData driven bunker procurement planning: working with the maritime industry   \n\n\nAbstract \nIn this presentation\, we will recount our journey collaborating with the maritime industry\, discussing the challenges we encountered and elucidating how we transformed these challenges into gratifying experiences and impactful contributions. We will use our work on bunker procurement decisions with a global container shipping company as an example to illustrate the impact we made and the lessons we learned.    \nBunker refueling decisions in international shipping are crucial operational choices. Each ship acts like a movable storage unit navigating through diverse markets\, procuring bunker fuels from different ports to sustain its voyage. This involves grappling with challenges posed by varying bunker fuel prices over time and locations. To tackle this challenge\, we propose data-driven structure-prescriptive (SP) approaches that combine the strengths of modern machine learning with the insights from traditional OR modeling and optimization. Instead of predicting future marine fuel prices\, our approach directly learns the optimal refueling policy from data and adapts refueling decisions to the current market conditions\, including fuel prices\, crude oil price\, NYSE index\, etc.   \nOur focus lies in leveraging the well-established understanding that the optimal refueling decision adheres to a state-dependent base-stock refueling policy. This decision depends on factors such as the port of call\, fuel tank capacity\, market conditions\, and is finite-valued\, depending on the vessel’s schedule and voyage. We provide a practical framework to incorporate these structural properties into data-driven decision-making for bunker refueling operations. The proposed SP approaches successfully recovered the “true” optimal refueling policy in synthetic simulations. Moreover\, our experiments unveiled that incorporating more structural properties into the learning process significantly improved the out-of-sample (OOS) performance. In the case study\, we compared our proposed SP approach with the firm’s existing operation\, resulting in a noteworthy reduction of fuel expenses\, which amounts to approximately 2.52 million USD per year in savings for a fleet of six ships.   \nIn addition\, to facilitate our collaboration with industry\, we propose an eXplainable multi-stage bunker procurement planning (X-BPP) framework for the maritime industry. In this presentation\, we will showcase this framework\, discuss its performance\, and share the lessons we learn in implementing the system.    \n\n\nAbout the speakers \nMabel C. Chou is an associate professor in the Analytics and Operations department at National University of Singapore (NUS). She received the B.Sc. degree in mathematics from National Taiwan University\, the M.Sc. degree in mathematics and Ph.D. degree in industrial engineering and management sciences from Northwestern University. Her research focuses on production scheduling and supply chain analysis. Her current research interest is in the application of optimization tools and business analytics for engineering\, service\, and supply chain management problems. She is an associate editor for Operations Research\, a senior editor for Production and Operations Management and an associate editor for Pacific Journal of Optimization. She has also consulted for companies such as GSK\, Caterpillar\, P&G\, SIA Engineering Company\, National University Hospital\, Tan Tock Seng Hospital\, Lenovo\, Supreme Components International\, etc.    \nSun Qinghe is an Assistant Professor at the Department of Logistics and Maritime Studies (LMS)\, PolyU Business School.  Her research combines data with optimization to provide insights into risk management within supply chain systems\, particularly within the maritime logistics sector. Qinghe received her Ph.D. in Operations Research from the National University of Singapore (NUS) in 2022\, jointly advised by Mabel Chou and Qiang Meng\, and her B.Sc. in Maritime Studies from Nanyang Technological University (NTU)\, Singapore.   \nLi Wei is a Research Fellow at the National University of Singapore’s Institute of Operations Research and Analytics\, jointly advised by Mabel C. Chou and Chen Ying. He has a broad interest in model development for Financial Forecasting-related problems and his research is often motivated by industry initiatives. He obtained his Ph.D. in Computational Finance from the Norwegian University of Science and Technology before joining NUS.  
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-mabel-chou-sun-qinghe-li-wei/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20231120T100000
DTEND;TZID=Asia/Singapore:20231120T113000
DTSTAMP:20260417T103405
CREATED:20231114T041749Z
LAST-MODIFIED:20231114T041900Z
UID:18505-1700474400-1700479800@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Zhou Zhengyuan
DESCRIPTION:  \n\n\n\nName of Speaker\nZhengyuan Zhou\n\n\nSchedule\n20 November 2023\, 10am – 11.30am\n\n\nVenue  \nBIZ1 – 0302\n\n\nLink to Register \nhttps://nus-sg.zoom.us/meeting/register/tZIudu6qrTorE90DBkeYzCo1WC_rQEUdCldn\n\n\nTitle \nOptimal No-Regret Learning in Repeated First-Price Auctions\n\n\nAbstract\nFirst-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.\n\n\nAbout the Speaker\nZhengyuan Zhou is currently an assistant 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.\n\n\n\n  \n 
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-zhou-zhengyuan/
CATEGORIES:IORA Seminar Series
END:VEVENT
END:VCALENDAR