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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230303T100000
DTEND;TZID=Asia/Singapore:20230303T113000
DTSTAMP:20260417T184929
CREATED:20230209T084917Z
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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
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DTSTART;TZID=Asia/Singapore:20230317T100000
DTEND;TZID=Asia/Singapore:20230317T113000
DTSTAMP:20260417T184929
CREATED:20230220T060513Z
LAST-MODIFIED:20230309T050139Z
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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
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DTSTART;TZID=Asia/Singapore:20230323T100000
DTEND;TZID=Asia/Singapore:20230323T113000
DTSTAMP:20260417T184929
CREATED:20230110T032726Z
LAST-MODIFIED:20230321T023308Z
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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
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DTSTART;TZID=Asia/Singapore:20230331T100000
DTEND;TZID=Asia/Singapore:20230331T113000
DTSTAMP:20260417T184929
CREATED:20230209T085028Z
LAST-MODIFIED:20230322T084550Z
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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
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