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X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
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DTSTART:20210101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220902T100000
DTEND;TZID=Asia/Singapore:20220902T113000
DTSTAMP:20260418T111719
CREATED:20220812T033552Z
LAST-MODIFIED:20220829T070504Z
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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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220909T100000
DTEND;TZID=Asia/Singapore:20220909T113000
DTSTAMP:20260418T111719
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220916T100000
DTEND;TZID=Asia/Singapore:20220916T113000
DTSTAMP:20260418T111719
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220930T100000
DTEND;TZID=Asia/Singapore:20220930T113000
DTSTAMP:20260418T111719
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
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