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X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
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DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240102T100000
DTEND;TZID=Asia/Singapore:20240102T113000
DTSTAMP:20260419T055544
CREATED:20231224T131337Z
LAST-MODIFIED:20231224T131337Z
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SUMMARY:DAO-IORA Seminar Series - Chen Xi
DESCRIPTION:  \n\n\n\n\nName of Speaker\nChen Xi\n\n\nSchedule\n2 January 2024\, 10am – 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/tZ0tcuygqTstGtXSwYItVg9uaq46sPHW8Akl\n\n\nTitle \nDigital Privacy in Personalized Pricing and New Directions in DeFI\n\n\nAbstract\nAbstract: This talk has two parts. The first part is on digital privacy in personalized pricing. When involving personalized information\, how to protect the privacy of such information becomes a critical issue in practice. In this talk\, we consider a dynamic pricing problem with an unknown demand function of posted prices and personalized information. By leveraging the fundamental framework of differential privacy\, we develop a privacy-preserving dynamic pricing policy\, which tries to maximize the retailer revenue while avoiding information leakage of individual customers’ information and purchasing decisions. This is joint work with Prof. Yining Wang and Prof. David Simchi-Levi. \nThe second part is on my very recent research in decentralized finance. I will first discuss my work on delta hedging liquidity positions on the automated market maker (Uniswap V3) and then highlights some open problems in decentralized finance.\n\n\nAbout the Speaker\nXi Chen is the Andre Meyer Full professor at Stern School of Business\, New York University\, who is also an affiliated professor at Computer Science and Center for Data Science. Before that\, he was a Postdoc in the group of Prof. Michael Jordan at UC Berkeley and obtained his Ph.D. from the Machine Learning Department at Carnegie Mellon University. \nHe studies high-dimensional machine learning\, online learning\, large-scale stochastic optimization\,  and applications to operations management and FinTech. Recently\, he started a new research line on blockchain technology and decentralized finance. He is a recipient of COPSS Leadership Academy\, Elected Member of International Statistical Insititute (ISI)\, The World’s Best 40 under 40 MBA Professor by Poets & Quants\, and Forbes 30 under 30 in Science.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-chen-xi/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240112T100000
DTEND;TZID=Asia/Singapore:20240112T113000
DTSTAMP:20260419T055544
CREATED:20231228T080840Z
LAST-MODIFIED:20231228T080840Z
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SUMMARY:DAO-IORA Seminar Series - Zhao Jinglong
DESCRIPTION:Name of Speaker\nZhao Jinglong\n\n\nSchedule\n12 January 2024\, 10am – 11.30am\n\n\nVenue \nBIZ1-0203\n\n\nLink to Register \n \nhttps://nus-sg.zoom.us/meeting/register/tZEpcOGopz4jE91kG6vFGQ78zjTCRdz9iGFZ\n\n\nTitle\nAdaptive Neyman Allocation\n\n\nAbstract\nIn experimental design\, Neyman allocation refers to the practice of allocating subjects into treated and control groups\, potentially in unequal numbers proportional to their respective standard deviations\, with the objective of minimizing the variance of the treatment effect estimator. This widely recognized approach increases statistical power in scenarios where the treated and control groups have different standard deviations\, as is often the case in social experiments\, clinical trials\, marketing research\, and online A/B testing. However\, Neyman allocation cannot be implemented unless the standard deviations are known in advance. Fortunately\, the multi-stage nature of the aforementioned applications allows the use of earlier stage observations to estimate the standard deviations\, which further guide allocation decisions in later stages. In this paper\, we introduce a competitive analysis framework to study this multi-stage experimental design problem. We propose a simple adaptive Neyman allocation algorithm\, which almost matches the information-theoretic limit of conducting experiments. Using online A/B testing data from a social media site\, we demonstrate the effectiveness of our adaptive Neyman allocation algorithm\, highlighting its practicality especially when applied with only a limited number of stages.\n\n\nAbout the Speaker\nJinglong Zhao is an Assistant Professor of Operations and Supply Chain Management at Questrom School of Business at Boston University. He works at the interface between optimization and econometrics. His research leverages discrete optimization techniques to design field experiments with applications in online platforms. Jinglong completed his PhD in Social and Engineering Systems and Statistics at Massachusetts Institute of Technology.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-zhao-jinglong/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240119T100000
DTEND;TZID=Asia/Singapore:20240119T113000
DTSTAMP:20260419T055544
CREATED:20240115T021633Z
LAST-MODIFIED:20240115T021633Z
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SUMMARY:DAO-IORA Seminar Series - Qin Hanzhang
DESCRIPTION:  \n\n\n\n\nName of Speaker\nQin Hanzhang\n\n\nSchedule\n19 January 2024\, 10am – 11.30am\n\n\nVenue  \nI4-01-03 Seminar Room\n\n\nLink to Register \n https://nus-sg.zoom.us/meeting/register/tZEpce-orz8rEtZcddjRXnFf3EACPJSUMiAt\n\n\nTitle \nLower Sample Complexity of Reinforcement Learning for Structured MDPs: Evidence from Inventory Control\n\n\nAbstract\nI will discuss the important open problems of 1) What is the sample complexity (i.e.\, how may number of data samples is needed) of learning nearly optimal policy for multi-stage stochastic inventory control when the underlying demand distribution is initially unknown; and 2) How to compute such a policy when the required number of data samples are given. For the first half of the talk\, without considering fixed ordering cost\, I will start answering the questions from the backlog setting via SAIL\, a novel SAmple based Inventory Learning algorithm. Then\, results for the more practical lost-sales setting will be discussed\, including the first sample complexity result for this more challenging setting with only mild assumptions (that ensures quality data)\, by leveraging both recent developments of variance reduction techniques for reinforcement learning and the structural properties of the dynamic programming formulation for inventory control settings. Numerical simulations show that SAIL significantly outperforms competing methods in terms of inventory cost minimization. Then in the second half\, I will discuss several recent developments on sample complexity related to all three types of the MDP formulations (finite-horizon MDPs\, infinite-horizon discounted/average-cost MDPs) for inventory control with fixed ordering cost. Somewhat surprisingly\, in all three cases\, it is found that sample complexity of the most naïve plug-in estimators is strictly lower than the “best possible” bounds derived for general MDPs. The first half will be based on joint work with David Simchi-Levi (MIT) and Ruihao Zhu (Cornell)\, and the second half will be based on joint work with Boxiao Chen (UIC)\, Xiaoyu Fan (NYU)\, Michael Pinedo (NYU) and Zhengyuan Zhou (NYU).\n\n\nAbout the Speaker \nHanzhang Qin is an Assistant Professor at the Department of Industrial Systems Engineering and Management at NUS. He is also an affiliated faculty member at the NUS Institute for Operations Research and Analytics. His research was recognized by several awards\, including INFORMS TSL Intelligent Transportation Systems Best Paper Award and MIT MathWorks Prize for Outstanding CSE Doctoral Research. Before joining NUS\, Hanzhang spent one year as a postdoctoral scientist in the Supply Chain Optimization Technologies Group of Amazon NYC. He earned his PhD in Computational Science and Engineering under supervision of Professor David Simchi-Levi\, and his research interests span stochastic control\, applied probability and statistical learning\, with applications in supply chain analytics and transportation systems. He holds two master’s\, one in EECS and one in Transportation both from MIT. Prior to attending MIT\, Hanzhang received two bachelor degrees in Industrial Engineering and Mathematics from Tsinghua University.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-qin-hanzhang/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240126T100000
DTEND;TZID=Asia/Singapore:20240126T113000
DTSTAMP:20260419T055544
CREATED:20240119T120215Z
LAST-MODIFIED:20240119T120215Z
UID:19350-1706263200-1706268600@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Zhan Ruohan
DESCRIPTION:  \n\n\n\nName of Speaker\nZhan Ruohan\n\n\nSchedule\n26 January 2024\, 10am – 11.30am\n\n\nVenue  \nI4-01-03\n\n\nLink to Register \nhttps://nus-sg.zoom.us/meeting/register/tZApdeirrD8uGdK8Z3tiW6Can4XywK0kVD4C\n\n\nTitle \nEstimation and Inference under Recommender Interference\n\n\nAbstract\nIn digital platforms\, recommender systems (RecSys) are essential for aligning content with viewer preferences. This work considers the evaluation of RecSys updates\, referred to as “treatments”\, by analyzing their “global treatment effect” (GTE) – the expected overall benefit of universally applying these treatments. Our focus is on treatments targeting content creators. We utilize A/B experiments on the creator side and identify that the conventional difference-in-mean estimator is biased for GTE estimation\, due to interference among creators competing for visibility. To address this challenge\, we introduce a semi-parametric model that combines a parametric choice model\, designed to streamline the recommendation process\, with a nonparametric component that employs machine learning to account for the heterogeneity among viewers and content. Using this model\, we approximate GTE with a doubly robust estimator that satisfies Neyman orthogonality\, ensuring consistency and asymptotic normality\, and supporting hypothesis testing. We show the robustness and semiparametric efficiency of our estimator even under model mis-specification. We demonstrate the efficacy of our method through simulations and practical applications on a leading short video platform. This is joint work with Shichao Han\, Yuchen Hu\, and Zhenling Jiang.\n\n\nAbout the Speaker \nRuohan Zhan is an assistant professor in the Department of Industrial Engineering and Decision Analytics at the Hong Kong University of Science and Technology. She earned her PhD from Stanford University and her BS from Peking University. Specializing in causal inference\, statistics\, and machine learning\, Ruohan develops new methods to solve problems from online marketplaces\, particularly on challenges related to causal effect identification\, economic analysis\, experimentation and operations. Her research has been published in top-tier journals including Management Science and Proceedings of National Academy of Sciences\, as well as leading machine learning conferences including NeurIPS\, ICLR\, WWW\, and KDD.\n\n\n\n  \n  \n 
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-zhan-ruohan/
CATEGORIES:IORA Seminar Series
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