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DTSTART;TZID=Asia/Singapore:20220311T100000
DTEND;TZID=Asia/Singapore:20220311T113000
DTSTAMP:20260407T125137
CREATED:20211216T015014Z
LAST-MODIFIED:20220307T054220Z
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SUMMARY:IORA Seminar Series – Assaf Zeevi
DESCRIPTION:Assaf Zeevi is Professor and holder of the Kravis chair at the Graduate School of Business\, Columbia University. His research and teaching interests lie at the intersection of Operations Research\, Statistics\, and Machine Learning. In particular\, he has been developing theory and algorithms for reinforcement learning\, Bandit problems\, stochastic optimization\, statistical learning and stochastic networks. Assaf’s work has found applications in online retail\, healthcare analytics\, dynamic pricing\, recommender systems\, and social learning in online marketplaces. \nAssaf received his B.Sc. and M.Sc. (Cum Laude) from the Technion\, in Israel\, and subsequently his Ph.D. from Stanford University. He spent time as a visitor at Stanford University\, the Technion and Tel Aviv University. He is the recipient of several teaching and research awards including a CAREER Award from the National Science Foundation\, an IBM Faculty Award\, Google Research Award\, as well as several best paper awards including the 2019 Lanchester Prize.   Assaf has recently served a term as Vice Dean at Columbia Business School and Editor-in-Chief of Stochastic Systems (the flagship journal of INFORMS’ Applied Probability Society). He also serves on various other editorial boards and program committees in the Operations Research and Machine Learning communities\, as well as scientific advisory boards for startup companies in the high technology sector. \n\n\n\nName of Speaker \nAssaf Zeevi\n\n\nSchedule\n11 March 2022\, 10am – 11.30am \n(60 min talk + 30 min Q&A)\n\n\nLink to register  \n(Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZYtdOusrj4qGtYQnSrSqKvhRN7RjGgj8vM3\n\n\nTitle\nOnline Learning in Sequential Selection Problems \n(A Two Part Talk…)\n\n\nAbstract\nIn this sequence of two (self-contained) talks\, I will describe some recent work on learning theoretic formulations in sequential selection problems\, focusing on two vignettes. \nThe first (to be covered in part 1) will focus on an optimal stopping problem:  given a random sequence of independent observations revealed one at a time over some finite horizon of play\, the objective is to design an algorithm that “stops’’ this sequence to maximize the expected value of the “stopped” observation.  (Once the sequence is stopped there is no recourse and the game terminates.) When the (common) distribution governing the random sequence is known\, the optimal rule is a (distribution-dependent) threshold policy that is obtained by backward induction; work on this problem has a long and storied history.  Surprisingly\, if one does *not* assume the distribution to be known a priori\, there is fairly little work in extant literature\, and the talk will develop this formulation\, expound some of the challenges involved in its learning theoretic formulations\, and an indication of what can (and cannot) be achieved in this setting. \nThe second vignette (to be covered in part 2) will focus on a sequential stochastic assignment problem\, which dates back roughly 50 years.  In this problem a known number of sequentially arriving items\, say\, “jobs\,” need to be assigned to a pool of\, say\, “workers\,” and once each job is assigned to a worker\, both job and worker are no longer admissible for further assignment. Each job is characterized by a quality / complexity indicator drawn independently from an underlying distribution\, and each worker is characterized by a known “productivity coefficient”  (for example\, the effectiveness by which that person can process said job).  The objective is to assign jobs to workers so that the expected overall work time required for performing all the jobs will be minimal.  This formulation has been used extensively in the OR literature in a variety of application domains\, and is increasingly relevant in the study of online marketplaces and matching markets.  As in the case of the optimal stopping problem\, when the ambient distribution is known a priori the optimal assignment policy is obtained using backward induction arguments. Naturally\, in most realistic applications knowledge of this key problem primitive is not available\, giving rise\, again\, to learning theoretic formulations which will be the main focus of this part of the talk.
URL:https://iora.nus.edu.sg/events/assafzeevi2022p1/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/assaf-pic_w.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220316T100000
DTEND;TZID=Asia/Singapore:20220316T113000
DTSTAMP:20260407T125137
CREATED:20211216T015214Z
LAST-MODIFIED:20220311T082205Z
UID:14709-1647424800-1647430200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Assaf Zeevi (Talk Part 2)
DESCRIPTION:Assaf Zeevi is Professor and holder of the Kravis chair at the Graduate School of Business\, Columbia University. His research and teaching interests lie at the intersection of Operations Research\, Statistics\, and Machine Learning. In particular\, he has been developing theory and algorithms for reinforcement learning\, Bandit problems\, stochastic optimization\, statistical learning and stochastic networks. Assaf’s work has found applications in online retail\, healthcare analytics\, dynamic pricing\, recommender systems\, and social learning in online marketplaces. \nAssaf received his B.Sc. and M.Sc. (Cum Laude) from the Technion\, in Israel\, and subsequently his Ph.D. from Stanford University. He spent time as a visitor at Stanford University\, the Technion and Tel Aviv University. He is the recipient of several teaching and research awards including a CAREER Award from the National Science Foundation\, an IBM Faculty Award\, Google Research Award\, as well as several best paper awards including the 2019 Lanchester Prize.   Assaf has recently served a term as Vice Dean at Columbia Business School and Editor-in-Chief of Stochastic Systems (the flagship journal of INFORMS’ Applied Probability Society). He also serves on various other editorial boards and program committees in the Operations Research and Machine Learning communities\, as well as scientific advisory boards for startup companies in the high technology sector. \n\n\n\nName of Speaker  \nAssaf Zeevi\n\n\nSchedule \n16 March 2022\, 10am – 11.30am\n\n\nVenue: \nI4-01-03 Seminar Room  (For NUS Staff & Students)\n\n\nLink to register  (Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZYpceigpzIiEtMuQAiHk7PrMuiZcWUqgvfx\n\n\nTitle \nOnline Learning in Sequential Selection Problems (Part 2)\n\n\nAbstract \nIn this sequence of two (self-contained) talks\, I will describe some recent work on learning theoretic formulations in sequential selection problems\, focusing on two vignettes. \nThe first (to be covered in part 1) will focus on an optimal stopping problem:  given a random sequence of independent observations revealed one at a time over some finite horizon of play\, the objective is to design an algorithm that “stops’’ this sequence to maximize the expected value of the “stopped” observation.  (Once the sequence is stopped there is no recourse and the game terminates.) When the (common) distribution governing the random sequence is known\, the optimal rule is a (distribution-dependent) threshold policy that is obtained by backward induction; work on this problem has a long and storied history.  Surprisingly\, if one does *not* assume the distribution to be known a priori\, there is fairly little work in extant literature\, and the talk will develop this formulation\, expound some of the challenges involved in its learning theoretic formulations\, and an indication of what can (and cannot) be achieved in this setting. \nThe second vignette (to be covered in part 2) will focus on a sequential stochastic assignment problem\, which dates back roughly 50 years.  In this problem a known number of sequentially arriving items\, say\, “jobs\,” need to be assigned to a pool of\, say\, “workers\,” and once each job is assigned to a worker\, both job and worker are no longer admissible for further assignment. Each job is characterized by a quality / complexity indicator drawn independently from an underlying distribution\, and each worker is characterized by a known “productivity coefficient”  (for example\, the effectiveness by which that person can process said job).  The objective is to assign jobs to workers so that the expected overall work time required for performing all the jobs will be minimal.  This formulation has been used extensively in the OR literature in a variety of application domains\, and is increasingly relevant in the study of online marketplaces and matching markets.  As in the case of the optimal stopping problem\, when the ambient distribution is known a priori the optimal assignment policy is obtained using backward induction arguments. Naturally\, in most realistic applications knowledge of this key problem primitive is not available\, giving rise\, again\, to learning theoretic formulations which will be the main focus of this part of the talk.\n\n\nAbout the speaker \nAssaf Zeevi is Professor and holder of the Kravis chair at the Graduate School of Business\, Columbia University. His research and teaching interests lie at the intersection of Operations Research\, Statistics\, and Machine Learning. In particular\, he has been developing theory and algorithms for reinforcement learning\, Bandit problems\, stochastic optimization\, statistical learning and stochastic networks. Assaf’s work has found applications in online retail\, healthcare analytics\, dynamic pricing\, recommender systems\, and social learning in online marketplaces. \nAssaf received his B.Sc. and M.Sc. (Cum Laude) from the Technion\, in Israel\, and subsequently his Ph.D. from Stanford University. He spent time as a visitor at Stanford University\, the Technion and Tel Aviv University. He is the recipient of several teaching and research awards including a CAREER Award from the National Science Foundation\, an IBM Faculty Award\, Google Research Award\, as well as several best paper awards including the 2019 Lanchester Prize.   Assaf has recently served a term as Vice Dean at Columbia Business School and Editor-in-Chief of Stochastic Systems (the flagship journal of INFORMS’ Applied Probability Society). He also serves on various other editorial boards and program committees in the Operations Research and Machine Learning communities\, as well as scientific advisory boards for startup companies in the high technology sector.
URL:https://iora.nus.edu.sg/events/assafzeevi2022p2/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/assaf-pic_w.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220318T100000
DTEND;TZID=Asia/Singapore:20220318T113000
DTSTAMP:20260407T125137
CREATED:20211216T015847Z
LAST-MODIFIED:20220311T081817Z
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SUMMARY:IORA Seminar Series – Jiding Zhang
DESCRIPTION:Jiding Zhang is an Assistant Professor of Operations Management at NYU Shanghai. Jiding’s primary research interests lie in the field of marketplace analytics. In her recent work\, she analyzes the operations and economics of various digital platforms using both data analytic and mathematical modeling tools. She is also interested in developing data-driven methods for analysis of online markets. Jiding obtained her PhD from the Operations\, Information and Decisions Department of The Wharton School\, under supervision of Professors Senthil Veeraraghavan\, Ken Moon and Sergei Savin. \n\n\n\nName of Speaker\nZhang Jiding\n\n\nSchedule \n18 March\, 10am – 11.30am\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZApfuqppj4oHNZ-X8Btkjv-RqJAHz1zdBva\n\n\nTitle of Talk\nDoes Fake News Content Create Echo Chambers?\n\n\nAbstract\nPlatforms have recently come under criticism from regulatory agencies\, policymakers\, and media scholars for the burgeoning influence of unfettered fake news online. There has been debate regarding whether such online false news content creates echo chambers—segments of the market in which false news is exclusively or predominantly consumed.  We use a large-scale dataset reporting individual households’ online activity to understand the trends in online news consumption and examine the claim that online news creates echo chambers. We find that the consumption of false news online is widespread\, yet despite that\, such echo chambers are minimal. Through a structural model we analyze the joint consumption of online news from mainstream sources and from sources producing false content. Using a natural experiment created by a policy change on the largest social media platform\, we find that not only are echo chamber effects not pronounced on the aggregate level\, the causal effect of consuming more from false news sources is greater countervailing consumption of mainstream news. Naive\, operational interventions such as reducing the supply of false news sources may unnecessarily reduce the overall consumption of news from mainstream sources without adequately protecting the small minority most vulnerable to consuming only false news.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-jiding-zhang/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/jiding_zhang1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220325T100000
DTEND;TZID=Asia/Singapore:20220325T113000
DTSTAMP:20260407T125137
CREATED:20211216T020507Z
LAST-MODIFIED:20220316T062542Z
UID:14713-1648202400-1648207800@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Luyi Yang
DESCRIPTION:Luyi YANG is an assistant professor in the Operations and Information Technology Management Group at the University of California\, Berkeley’s Haas School of Business. His research interests include service operations\, business model innovation\, digital marketplaces\, smart mobility\, sustainability\, and operations-marketing interface. His research is published or forthcoming in leading journals such as Management Science\, Operations Research\, and Manufacturing & Service Operations Management and recognized by various research awards such as M&SOM Service Management SIG Best Paper Award\, INFORMS Service Science Best Cluster Paper Award\, INFORMS Minority Issues Forum Paper Competition\, and INFORMS Junior Faculty Interest Group (JFIG) Forum Paper Competition (Honorable Mention). He has taught courses in business analytics\, data mining\, and operations management. Prior to joining Berkeley Haas\, he was an assistant professor of operations management and business analytics at Johns Hopkins University’s Carey Business School. He received his Ph.D. and MBA from the University of Chicago\, Booth School of Business\, and his BS in Industrial Engineering and BA in English\, both from Tsinghua University. \n\n\n\nName of speaker\nLuyi Yang\n\n\nSchedule \n25 March 2022\, 10am – 11.30am\n\n\nLink to register (via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZMuf-yppzwoGtRzBFsorYVikmPRtPTr8Ac6\n\n\nTitle of talk\nRight to Repair: Pricing\, Welfare\, and Environmental Implications\n\n\nAbstract\nThe “right to repair” (RTR) movement calls for government legislation that requires manufacturers to provide repair information\, tools\, and parts so that consumers can independently repair their own products with more ease. The initiative has gained global traction in recent years. Repair advocates argue that such legislation would break manufacturers’ monopoly on the repair market and benefit consumers. They further contend that it would reduce the environmental impact by reducing e-waste and new production. Yet\, the RTR legislation may also trigger a price response in the product market as manufacturers try to mitigate the profit loss. This paper employs an analytical model to study the pricing\, welfare\, and environmental implications of RTR. We find that as the RTR legislation continually lowers the independent repair cost\, manufacturers may initially cut the new product price and then raise it. This non-monotone price adjustment may further induce a non-monotone change in consumer surplus\, social welfare\, and environmental impact. Strikingly\, the RTR legislation can potentially lead to a “lose-lose-lose” outcome that compromises manufacturer profit\, reduces consumer surplus\, and increases the environmental impact\, despite repair being made easier and more affordable.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-luyi-yang/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/Yang-Luyi-photo-1.jpg
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