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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210115T100000
DTEND;TZID=Asia/Singapore:20210116T110000
DTSTAMP:20260417T124136
CREATED:20210112T090755Z
LAST-MODIFIED:20210303T030021Z
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SUMMARY:IORA Seminar Series | 15 Jan | 10am
DESCRIPTION:Shape-constrained convex regression problem deals with fitting a convex function to the observed data\, where additional constraints are imposed\, such as component-wise monotonicity and uniform Lipschitz continuity. This talk presents a unified framework for computing the least squares estimator of a multivariate shape-constrained convex regression function in $mathbb{R}^d$. We prove that the least squares estimator is computable via solving a constrained convex quadratic programming (QP) problem with $(n+1)d$ variables\, $n(n-1)$ linear inequality constraints and $n$ possibly non-polyhedral inequality constraints\, where $n$ is the number of data points. To efficiently solve the generally very large-scale convex QP\, we design a proximal augmented Lagrangian method ({tt pALM}) whose subproblems are solved by the semismooth Newton method ({tt SSN}). To further accelerate the computation when $n$ is huge\, we design a practical implementation of the constraint generation method such that each reduced problem is efficiently solved by our proposed {tt pALM}. Comprehensive numerical experiments\, including those in the pricing of basket options and estimation of production functions in economics\, demonstrate that our proposed {tt pALM} outperforms the state-of-the-art algorithms\, and the proposed acceleration technique further shortens the computation time by a large margin.   [This talk is based on joint work with Meixia Lin and Defeng Sun]   \n  \n\n\n\nName of Speaker\n  Prof Toh Kim Chuan  \n\n\nSchedule \n  Friday 15 January 2021 \, 10am  \n\n\nLink \nhttps://nus-sg.zoom.us/j/83515146165?pwd=eUpLZm5NWSs0RUpxTU5jV3JTeFQ5UT09\n\n\nID\n835 1514 6165\n\n\nPassword\n700968\n\n\nTitle \n  An augmented Lagrangian method with constraint generations for shape-constrained convex regression problems  \n\n\nAbstract \n Shape-constrained convex regression problem deals with fitting a convex function to the observed data\, where additional constraints are imposed\, such as component-wise monotonicity and uniform Lipschitz continuity. This talk presents a unified framework for computing the least squares estimator of a multivariate shape-constrained convex regression function in $mathbb{R}^d$. We prove that the least squares estimator is computable via solving a constrained convex quadratic programming (QP) problem with $(n+1)d$ variables\, $n(n-1)$ linear inequality constraints and $n$ possibly non-polyhedral inequality constraints\, where $n$ is the number of data points. To efficiently solve the generally very large-scale convex QP\, we design a proximal augmented Lagrangian method ({tt pALM}) whose subproblems are solved by the semismooth Newton method ({tt SSN}). To further accelerate the computation when $n$ is huge\, we design a practical implementation of the constraint generation method such that each reduced problem is efficiently solved by our proposed {tt pALM}. Comprehensive numerical experiments\, including those in the pricing of basket options and estimation of production functions in economics\, demonstrate that our proposed {tt pALM} outperforms the state-of-the-art algorithms\, and the proposed acceleration technique further shortens the computation time by a large margin.   [This talk is based on joint work with Meixia Lin and Defeng Sun]  \n\n\nAbout the Speaker\nKim–Chuan Toh is a Professor at the Department of Mathematics\, National University of Singapore (NUS). He obtained his BSc degree in Mathematics from NUS and PhD degree in Applied Mathematics from Cornell University.   His current research focuses on designing efficient algorithms and software for convex programming and its applications\, particularly large–scale optimization problems arising from data science/machine learning\, and large–scale matrix optimization problems such as linear semidefinite programming (SDP) and convex quadratic semidefinite programming (QSDP).   He is currently an Area Editor for Mathematical Programming Computation\, an Associate Editor for the SIAM Journal on Optimization\, Mathematical Programming Series B\, and ACM Transactions on Mathematical Software. He received the Farkas Prize awarded by the INFORMS Optimization Society in 2017 and the triennial Beale–Orchard Hays Prize awarded by the Mathematical Optimization Society in 2018. He was elected as a Fellow of the Society for Industrial and Applied Mathematics in 2018.  \n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-15-jan-10am/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/01/Toh-Kim-Chuan-320x320-1.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210226T100000
DTEND;TZID=Asia/Singapore:20210226T110000
DTSTAMP:20260417T124136
CREATED:20210224T074018Z
LAST-MODIFIED:20210301T083122Z
UID:10098-1614333600-1614337200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series | 26 Feb | 10am
DESCRIPTION:Extensive research has shown that human decision makers in operations and supply chains are often influenced by cognitive biases and social preferences\, and as a consequence fail to achieve the optimal performance prescribed by normative operations theories.  In this talk\, we will discuss how behavioral factors affect decisions and economic outcomes in two operations management settings.  In the first setting\, we analyze how cognitive biases affect inventory decisions when biases are endogenously given in competition.  In the second setting\, we study how fairness and competition interact in a supply chain of one supplier and two retailers.  In both settings\, we find that the presence of competition can reverse the impact that the behavioral factors have on decision and performance in individual settings. \n  \n\n\n\nName of Speaker\n  \nA/P Wu Yaozhong \n \n\n\nSchedule \n  \nFriday 26 Feb 2021 \, 10am \n \n\n\nLink \nhttps://nus-sg.zoom.us/j/85139484987?pwd=TE1OMDNnSkwwNEwxQ2xjbTJ4MVQvUT09 \n\n\n\nJoin our Cloud HD Video Meeting \nZoom is the leader in modern enterprise video communications\, with an easy\, reliable cloud platform for video and audio conferencing\, chat\, and webinars across mobile\, desktop\, and room systems. Zoom Rooms is the original software-based conference room solution used around the world in board\, conference\, huddle\, and training rooms\, as well as executive offices and classrooms. Founded in 2011\, Zoom helps businesses and organizations bring their teams together in a frictionless environment to get more done. Zoom is a publicly traded company headquartered in San Jose\, CA. \nnus-sg.zoom.us\n\n\n\n\n\n\nID\n851 3948 4987\n\n\nPassword\n000323\n\n\nTitle \n  \nUnderstanding the role of behavioral factors in competitive operations and supply chains \n \n\n\nAbstract \n  \nExtensive research has shown that human decision makers in operations and supply chains are often influenced by cognitive biases and social preferences\, and as a consequence fail to achieve the optimal performance prescribed by normative operations theories.  In this talk\, we will discuss how behavioral factors affect decisions and economic outcomes in two operations management settings.  In the first setting\, we analyze how cognitive biases affect inventory decisions when biases are endogenously given in competition.  In the second setting\, we study how fairness and competition interact in a supply chain of one supplier and two retailers.  In both settings\, we find that the presence of competition can reverse the impact that the behavioral factors have on decision and performance in individual settings. \n \n\n\nAbout the Speaker\n  \nYaozhong Wu is an Associate Professor of Analytics and Operations at the NUS Business School. He received his Ph.D. in Technology and Operations Management from INSEAD. His main research interests are in the field of behavioral operations management. His papers have appeared in academic journals\, such as Management Science\, Operations Research\, Manufacturing & Service Operations Management\, Production and Operations Management\, and Journal of Operations Management.  A co-authored paper won the second place in the Wickham Skinner Awards for Best Paper Published in Production and Operations Management in 2014.  Another co-authored paper won the Best Working Paper Award of the INFORMS Behavioral Operations Management Section in 2016. Currently\, he serves as a senior editor for Production and Operations Management.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-26-feb-10am/
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/01/Wu-Yaozhong.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210305T100000
DTEND;TZID=Asia/Singapore:20210305T120000
DTSTAMP:20260417T124136
CREATED:20210304T080714Z
LAST-MODIFIED:20210304T084313Z
UID:10784-1614938400-1614945600@iora.nus.edu.sg
SUMMARY:The Dao of Robustness
DESCRIPTION:We present a general framework for data-driven optimization called robustness optimization that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution would deviate from the empirical distribution.  Unlike data-driven robust optimization approaches\, the decision maker does not have to size the  ambiguity set\, but specifies an acceptable target\, or loss of optimality compared to the baseline optimization model\, as a trade off for the model’s ability to withstand greater uncertainty.  We axiomatize the decision criterion associated with robustness optimization\, termed as the  fragility measure  and present its representation theorem. We present practicable robustness optimization models including models with safegurarding constraints\, adaptive and dynamic optimization models. Similar to robust optimization\, we show that robustness optimization can also be done in a tractable way. We also provide numerical studies on adaptive problems and show that the solutions to the robustness optimization models are effective in alleviating the Optimizer’s Curse (Smith and Winkler 2006) and yielding superior out-of-sample performance compared the empirical optimization model and current data-driven robust optimization models. This is a joint work with Mingling Zhou and Zhuoyu Long. \n  \n\n\n\nName of Speaker\nProf Melvyn Sim\n\n\nSchedule \nFriday 5 March 2021 \, 10am\n\n\nLink \nhttps://nus-sg.zoom.us/j/84738208342?pwd=UTlkN3FqRTBRbFZ6dEJvcVpVYUlkdz09\n\n\nID\n847 3820 8342\n\n\nPassword\n008617\n\n\nTitle \nThe Dao of Robustness\n\n\nAbstract \nWe present a general framework for data-driven optimization called robustness optimization that favors solutions for which a risk-aware objective function would best attain an acceptable target even when the actual probability distribution would deviate from the empirical distribution.  Unlike data-driven robust optimization approaches\, the decision maker does not have to size the  ambiguity set\, but specifies an acceptable target\, or loss of optimality compared to the baseline optimization model\, as a trade off for the model’s ability to withstand greater uncertainty.  We axiomatize the decision criterion associated with robustness optimization\, termed as the  fragility measure  and present its representation theorem. We present practicable robustness optimization models including models with safegurarding constraints\, adaptive and dynamic optimization models. Similar to robust optimization\, we show that robustness optimization can also be done in a tractable way. We also provide numerical studies on adaptive problems and show that the solutions to the robustness optimization models are effective in alleviating the Optimizer’s Curse (Smith and Winkler 2006) and yielding superior out-of-sample performance compared the empirical optimization model and current data-driven robust optimization models. This is a joint work with Mingling Zhou and Zhuoyu Long.\n\n\nAbout the Speaker\nDr Melvyn Sim is a professor at the Department of Analytics and Operations\, National University of Singapore. For the past twenty years\, he has been working the the area of optimization and decision making under uncertainty. \nFind out more about Prof Sim and Robust Optimization: https://youtu.be/eGXBd7KxjEY
URL:https://iora.nus.edu.sg/events/the-dao-of-robustness/
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/01/melvynsim.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210312T100000
DTEND;TZID=Asia/Singapore:20210312T113000
DTSTAMP:20260417T124136
CREATED:20210305T090935Z
LAST-MODIFIED:20210305T091145Z
UID:10813-1615543200-1615548600@iora.nus.edu.sg
SUMMARY:Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression
DESCRIPTION:Dr. Ying Chen is a financial statistician and data scientist. She develops statistical modelling and machine learning methods customized for nonstationary\, high frequency and large dimensional complex data such as cryptocurrency\, limit order book\, and renewable energy. She also works on business intelligence\, forecasting\, text mining and sentiment analysis\, and network analysis. Dr. Chen is Associate Professor in Department of Mathematics and Joint Appointee in Risk Management Institute (1 July 2019 to 30 June 2021)\, National University of Singapore. Dr. Chen is Associate Editor of 5 journals including Statistica Sinica (August 1\, 2017 to July 31\, 2023)\, Statistics and Its Interface\, Computational Statistics\, Digital Finance\, and Journal of Operations Research and Decisions. She is ISI Elected Member since March 2016. She is regular member of the Advisory Board of Institute of Statistical Mathematics\, Japan from 1 April 2018 to 31 March 2020. \n\n\n\nName of Speaker\nA/P Chen Ying\n\n\nSchedule \nFriday 12 March 2021 \, 10am\n\n\nLink \nhttps://nus-sg.zoom.us/j/87277632379?pwd=MElkRGxnd2x2QUg4VnFwMUp6WE9iZz09\n\n\nID\n872 7763 2379\n\n\nPassword\n247866\n\n\nTitle \nProbabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression\n\n\nAbstract \nProbabilistic forecasting of electricity load curves is of fundamental importance for effective scheduling and decision making in the increasingly volatile and competitive energy markets. We propose a novel approach to construct probabilistic predictors for curves (PPC)\, which leads to a natural and new definition of quantiles in the context of curve-to-curve linear regression. There are three types of PPC: a predict set\, a predictive band and a predictive quantile\, and all of them are defined at a pre-specified nominal probability level. In the simulation study\, the PPC achieve promising coverage probabilities under a variety of data generating mechanisms. When applying to one day ahead forecasting for the French daily electricity load curves\, PPC outperform several state-of-the-art predictive methods in terms of forecasting accuracy\, coverage rate and average length of the predictive bands. For example\, PPC achieve up to 2.8-fold of the coverage rate with much smaller average length of the predictive bands. The predictive quantile curves provide insightful information which is highly relevant to hedging risks in electricity supply management. (Joint work with Xiuqin Xu\, Yannig Goude and Qiwei Yao. Available at https://arxiv.org/abs/2009.01595)
URL:https://iora.nus.edu.sg/events/probabilistic-forecasting-for-daily-electricity-loads-and-quantiles-for-curve-to-curve-regression/
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/03/chenying.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210326T100000
DTEND;TZID=Asia/Singapore:20210326T110000
DTSTAMP:20260417T124136
CREATED:20210329T060811Z
LAST-MODIFIED:20210329T061339Z
UID:12506-1616752800-1616756400@iora.nus.edu.sg
SUMMARY:
DESCRIPTION:Dr. Swati Gupta is an Assistant Professor and Fouts Family Early Career Professor in the H. Milton Stewart School of Industrial & Systems Engineering\, and School of Computer Science (by courtesy) at Georgia Institute of Technology. She received a Ph.D. in Operations Research from MIT in 2017 and a joint Bachelors and Masters in CS from IIT\, Delhi in 2011. Dr. Gupta’s research interests are in optimization\, machine learning and algorithmic fairness. Her work spans various application domains such as revenue management\, energy and quantum computation. She received the NSF CISE Research Initiation Initiative (CRII) Award in 2019. She was also awarded the prestigious Simons-Berkeley Research Fellowship in 2017-2018\, where she was selected as the Microsoft Research Fellow in 2018. Dr. Gupta received the Google Women in Engineering Award in India in 2011. Dr. Gupta’s research is partially funded by the NSF and DARPA. \n\n\n\nName of Speaker\nDr Swati Gupta\n\n\nSchedule \nFriday 26 March 2021 \, 10am\n\n\nLink \nhttps://nus-sg.zoom.us/j/84784213927?pwd=eGs5U0Mwcm9XY1htcGlNQ3J5aEhCdz09\n\n\nID\n847 8421 3927\n\n\nPassword\n329485\n\n\nTitle \nMitigating the Impact of Bias in Selection Algorithms\n\n\nAbstract \nThe introduction of automation into the hiring process has put a spotlight on a persistent problem: discrimination in hiring on the basis of protected-class status. Left unchecked\, algorithmic applicant-screening can exacerbate pre-existing societal inequalities and even introduce new sources of bias; if designed with bias-mitigation in mind\, however\, automated methods have the potential to produce fairer decisions than non-automated methods. In this work\, we focus on selection algorithms used in the hiring process (e.g.\, resume-filtering algorithms) given access to a “biased evaluation metric”. That is\, we assume that the method for numerically scoring applications is inaccurate in a way that adversely impacts certain demographic groups. \nWe analyze the classical online secretary algorithms under two models of bias or inaccuracy in evaluations: (i) first\, we assume that the candidates belong to disjoint groups (e.g.\, race\, gender\, nationality\, age)\, with unknown true utility Z\, and “observed” utility Z/\beta for some unknown \beta that is group-dependent\, (ii) second\, we propose a “poset” model of bias\, wherein certain pairs of candidates can be declared incomparable.  We show that in the biased setting\, group-agnostic algorithms for online secretary problem are suboptimal\, often causing starvation of jobs for groups with \beta>1. We bring in techniques from matroid secretary literature and order theory to develop group-aware algorithms that are able to achieve certain “fair” properties\, while obtaining near-optimal competitive ratios for maximizing true utility of hired candidates in a variety of adversarial and stochastic settings. Keeping in mind the requirements of U.S. anti-discrimination law\, however\, certain group-aware interventions can be construed as illegal\, and we will conclude the talk by partially addressing tensions with the law and ways to argue legal feasibility of our proposed interventions. This talk is based on work with Jad Salem and Deven R. Desai.
URL:https://iora.nus.edu.sg/events/12506/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210409T100000
DTEND;TZID=Asia/Singapore:20210409T110000
DTSTAMP:20260417T124136
CREATED:20210409T061603Z
LAST-MODIFIED:20210409T061603Z
UID:13064-1617962400-1617966000@iora.nus.edu.sg
SUMMARY:What is your data worth? Quantifying the value of data in AI.
DESCRIPTION:James Zou is an assistant professor of biomedical data science\, CS and EE at Stanford University. He is also a Chan-Zuckerberg investigator. James work on making ML more reliable\, accountable and human compatible. Several of his methods are used by tech\, biotech and pharma companies. He has received several best paper awards at top CS venues\, the 2019 RECOMB best paper award\, NSF CAREER Award\, Google Faculty Award\, Tencent AI award\, Amazon Research Award and the Sloan Fellowship. \n\n\n\nSchedule \n  \nFriday 9 April 2021 \, 10am \n \n\n\nLink \n  \nhttps://nus-sg.zoom.us/j/86231051396?pwd=MHA5K3REWmx4enNWK1FjN0VycHpsUT09\n\n\nID\n862 3105 1396\n\n\nPassword\n492241\n\n\nTitle \n  \nWhat is your data worth? Quantifying the value of data in AI. \n \n\n\nAbstract \nAs data becomes the fuel driving technological and economic growth\, a fundamental challenge is how to quantify the value of data in algorithmic predictions and decisions. For example\, CA Gov. Newsom recently proposed “data dividend” whereby consumers are compensated by companies for the data that they generate. In this talk we will present a principled framework to quantify the value of different data in AI. Beyond regulatory implications\, we will discuss applications to denoising\, active learning\, and domain adaption on large biomedical datasets. I will conclude by discussing how data valuation contributes to a broader framework of accountable AI.
URL:https://iora.nus.edu.sg/events/what-is-your-data-worth-quantifying-the-value-of-data-in-ai/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/04/James.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210827T100000
DTEND;TZID=Asia/Singapore:20210827T113000
DTSTAMP:20260417T124136
CREATED:20210811T054649Z
LAST-MODIFIED:20210811T095518Z
UID:14171-1630058400-1630063800@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Designing Video Games: A Research Overview
DESCRIPTION:A/P Christopher Thomas Ryan teaches at the University of British Columbia in the Sauder School of Business. His research interests include optimization (broadly defined)\, theoretical economics\, operations management\, organizational learning\, pedagogy\, and business history. \n  \n\n\n\nName of Speaker\nA/P Christopher Thomas Ryan\n\n\nSchedule\nFriday 27 August 2021\, 10am\n\n\n Link to Register\nhttps://nus-sg.zoom.us/meeting/register/tZUtduutqT0uH92GC20RTeo9_WTQvoDW4DCg\n\n\n Title\nDesigning video games: A research overview\n\n\nAbstract\nVideo games are the fastest-growing and largest sector of the entertainment industry. Video games have also been at the heart of technological innovations for decades. However\, there is\, I believe\, a largely untapped opportunity for operations research methods to study questions of game design. In the words of Paul Tozour\, a well-known game designer\, in 2013: \n“If used properly\, decision modeling can significantly enhance many aspects of the design process … we should be able to search through a much larger number of possible solutions than we could do by hand or with our imaginations … we can often get better results\, get results faster\, and in some cases\, we can even solve problems that simply can’t be solved any other way.” \nIn this talk I will review some of the literature on using operations research tools to study video game design. I will also talk about four of my recent papers looking at four important problems in video game design: level design\, world design\, designing for diversity of play\, and designing “boosters” in puzzle games. I will also present a framework for future research in video games and an invitation for more researchers to join us. \nI will present work based on projects with the following co-authors: Oussama Hanguir (Columbia)\, Yifu Li (Xian Jiao Tong Liverpool)\, Will Ma (Columbia)\, Lifei Sheng (University of Houston Clear Lake)\, Benny Wong (UBC)\, and Xuying Zhao (Notre Dame).
URL:https://iora.nus.edu.sg/events/iora-seminar-series-designing-video-games-a-research-overview/
CATEGORIES:IORA Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210903T100000
DTEND;TZID=Asia/Singapore:20210903T110000
DTSTAMP:20260417T124136
CREATED:20210812T022859Z
LAST-MODIFIED:20210827T060534Z
UID:14181-1630663200-1630666800@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Ilya O. Ryzhov
DESCRIPTION:Ilya O. Ryzhov is an Associate Professor of Operations Management and Management Science at the Robert H. Smith School of Business\, University of Maryland. His research focuses on decision-making under uncertainty with applications in business analytics. He is an Associate Editor at Operations Research\, and received the 2017 INFORMS Simulation Society Outstanding Publication Award\, as well as the 2020 INFORMS Urban Transportation SIG Outstanding Paper Award. \n  \n\n\n\nName of Speaker\nA/P Ilya O. Ryzhov\n\n\nSchedule\nFriday 3 September 2021\, 10am\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0lce-grTMpHNKr4ldsTPL-uZEjwfAgtNAx\n\n\nTitle\nData-driven robust resource allocation with monotonic cost functions\n\n\nAbstract\nThis work illustrates the potential of statistical methods in operations research problems. We consider a two-stage planning problem (arising\, e.g.\, in city logistics) where a resource is first divided among a set of independent regions\, and then costs are incurred based on the allocation to each region. Costs are decreasing in the quantity of the resource\, but their precise values are unknown. We develop a new data-driven uncertainty model for monotonic cost functions\, which can be used in conjunction with robust optimization to obtain tractable allocation decisions that significantly improve worst-case performance outcomes.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-ilya-o-ryzhov/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/png:https://iora.nus.edu.sg/wp-content/uploads/2021/08/Ilya_Rhyzov.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210910T100000
DTEND;TZID=Asia/Singapore:20210910T110000
DTSTAMP:20260417T124136
CREATED:20210812T023152Z
LAST-MODIFIED:20210902T050131Z
UID:14183-1631268000-1631271600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Dennis Zhang
DESCRIPTION:Dennis Zhang is a tenured associate professor of Operations and Manufacturing Management at the Olin Business School. A/P Zhang’s research focuses on data-driven operations in digital economy and platforms. He implements field experiments and use observational data to improve operations. \n  \n\n\n\nName of Speaker\nA/P Dennis Zhang\n\n\nSchedule\nFriday 10 September\, 10am\n\n\nRegistration Link\nhttps://nus-sg.zoom.us/meeting/register/tZUkdOCgrDkoHtKyWVqFtDPx_-nGbnESs2tL\n\n\nTitle\nChoice Overload with Search Cost and Anticipated Regret: Theoretical Framework and Field Evidence\n\n\nAbstract\nAs consumers are offered an ever-increasing number of options for almost every purchase decision in online retail\, understanding the impact of assortment size on consumer choice decisions––especially on both search and purchase behavior––is critical. Our research speaks to this question by combining empirical analyses with theoretical modeling. First\, via a large-scale field experiment involving $1.6$ million consumers on Alibaba’s online retail platforms\, we causally examine how consumers’ click and purchase behavior changes as the number of products in a choice set increases. We document that consumers’ likelihood of clicking or purchasing at least one product increases at first but then decreases as the number of offered products rises. To explain this inverted-U-shaped relationship\, we develop a “consider-then-choose-with-regret” (CTCR) choice model that incorporates consumers’ search cost and anticipated regret. Numerical experiments suggest that our CTCR model leads to smaller optimal assortments containing products of higher expected utilities and lower prices on average than the classical multinomial logit choice model. Altogether\, this work presents real-world experimental evidence for choice overload on both search and purchase behaviour\, advances the field’s understanding of how assortment sizes alter consumer choices\, and provides a theoretical foundation for incorporating the choice overload effect in operational decisions. \nhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=3890056\n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-dennis-zhang/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/08/Dennis-Zhang.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210917T100000
DTEND;TZID=Asia/Singapore:20210917T110000
DTSTAMP:20260417T124136
CREATED:20210812T024436Z
LAST-MODIFIED:20210910T064137Z
UID:14186-1631872800-1631876400@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Chen Ningyuan
DESCRIPTION:Dr. Ningyuan Chen is currently an assistant professor at the Department of Management at the University of Toronto Mississauga and cross-appointed at the Rotman School of Management\, University of Toronto. Before joining the University of Toronto\, he was an assistant professor at the Hong Kong University of Science and Technology. He received his Ph.D. from the Industrial Engineering and Operations Research (IEOR) department at Columbia University in 2015. He is interested in various approaches to making data-driven decisions in applications including revenue management. His research is supported by the UGC of Hong Kong and the Discovery Grants Program of Canada. \n  \n\n\n\nName of Speaker\nDr Chen Ningyuan\n\n\nSchedule\nFriday 17 September\, 10am\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0td-ysrz8uGtLavCw5BEWFm37wQmWdiVsx\n\n\nTitle\nModel-Free Assortment Pricing with Transaction Data\n\n\nAbstract\nWe study the problem when a firm sets prices for products based on the transaction data\, i.e.\, which product past customers chose from an assortment and what were the historical prices that they observed. Our approach does not impose a model on the distribution of the customers’ valuations and only assumes\, instead\, that purchase choices satisfy incentive-compatible constraints. The individual valuation of each past customer can then be encoded as a polyhedral set\, and our approach maximizes the worst-case revenue assuming that new customers’ valuations are drawn from the empirical distribution implied by the collection of such polyhedra. We show that the optimal prices in this setting can be approximated at any arbitrary precision by solving a compact mixed-integer linear program. Moreover\, we study the single-product case and relate it to the traditional model-based approach. We also design three approximation strategies that are of low computational complexity and interpretable. Comprehensive numerical studies based on synthetic and real data suggest that our pricing approach is uniquely beneficial when the historical data has a limited size or is susceptible to model misspecification.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-chen-ningyuan/
CATEGORIES:IORA Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20210924T100000
DTEND;TZID=Asia/Singapore:20210924T110000
DTSTAMP:20260417T124136
CREATED:20210910T064606Z
LAST-MODIFIED:20210919T133956Z
UID:14399-1632477600-1632481200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Michael Choi
DESCRIPTION:Michael Choi is currently an Assistant Professor at the Yale-NUS College\, and by courtesy\, at the Department of Statistics and Data Science at National University of Singapore (NUS). He is also affiliated with the Institute of Operations Research and Analytics (iORA). Prior to joining Yale-NUS\, he was an Assistant Professor for three years in the School of Data Science at The Chinese University of Hong Kong\, Shenzhen. He received his PhD in Operations Research from Cornell University in 2017\, and his undergraduate degree in Actuarial Science (First Class Honours) from The University of Hong Kong in 2013. \nHis research interests centre around stochastic processes and their broad applications and intersections with other fields such as data science\, with a particular focus on Markov chains theory and stochastic algorithms driven by Markov chains. He has published extensively in leading journals of his area\, including Transactions of the American Mathematical Society\, Stochastic Processes and their Applications\, Combinatorics\, Probability and Computing\, and Electronic Communications in Probability. \n\n\n\nName of Speaker\nDr Michael Choi\n\n\nSchedule\nFriday 24 September\, 10am\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0ofu6hqzkiGtFQQExJuuRXSZnKC2bA9CO_\n\n\nTitle\nOn the convergence of an improved and adaptive kinetic simulated annealing\n\n\nAbstract\nInspired by the work of [Fang et al.. An improved annealing method and its large-time behaviour. Stochastic Process. Appl. (1997)\, Volume 71 Issue 1 Page 55-74.]\, who propose an improved simulated annealing algorithm based on a variant of overdamped Langevin diffusion with state-dependent diffusion coefficient\, we cast this idea in the kinetic setting and develop an improved kinetic simulated annealing (IKSA) method for minimizing a target function U. To analyze its convergence\, we utilize the framework recently introduced by [Monmarché. Hypocoercivity in metastable settings and kinetic simulated annealing. Probab. Theory Related Fields (2018)\, Volume 172 Page 1215-1248.] for the case of kinetic simulated annealing (KSA). The core idea of IKSA rests on introducing a parameter c > inf U\, which de facto modifies the optimization landscape and clips the critical height in IKSA at a maximum of c – inf U. Consequently IKSA enjoys improved convergence with faster logarithmic cooling than KSA. To tune the parameter c\, we propose an adaptive method that we call IAKSA which utilizes the running minimum generated by the algorithm on the fly\, thus avoiding the need to manually adjust c for better performance. We present positive numerical results on some standard global optimization benchmark functions that verify the improved convergence of IAKSA over other Langevin-based annealing methods.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-michael-choi/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211001T100000
DTEND;TZID=Asia/Singapore:20211001T110000
DTSTAMP:20260417T124136
CREATED:20210812T024540Z
LAST-MODIFIED:20210924T090458Z
UID:14188-1633082400-1633086000@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Hamsa Bastani
DESCRIPTION:Hamsa Bastani is an Assistant Professor of Operations\, Information\, and Decisions at the Wharton School\, University of Pennsylvania. Her research focuses on developing novel machine learning algorithms for data-driven decision-making\, with applications to healthcare operations\, social good\, and revenue management. She designs methods for sequential decision-making\, transfer learning and human-in-the-loop analytics. Her applied work uses large-scale\, novel data sources to inform policy around impactful societal problems. Her work has received several recognitions\, including the Pierskalla Award for the best paper in healthcare\, as well as first place in the George Nicholson and MSOM student paper competitions. \n\n\n\nName of Speaker\nDr Hamsa Bastani\n\n\nSchedule\nFriday 1 October 2021\, 10am\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZAtcO2rqT8jG9BLnqZzrhXlMX1cRbn316Hv\n\n\nTitle\nEfficient and targeted COVID-19 border testing via reinforcement learning\n\n\nAbstract\nThroughout the COVID-19 pandemic\, countries relied on a variety of ad-hoc border control protocols to allow for non-essential travel while safeguarding public health: from quarantining all travellers to restricting entry from select nations based on population-level epidemiological metrics such as cases\, deaths or testing positivity rates. Here we report the design and performance of a reinforcement learning system\, nicknamed ‘Eva’. In the summer of 2020\, Eva was deployed across all Greek borders to limit the influx of asymptomatic travellers infected with SARS-CoV-2\, and to inform border policies through real-time estimates of COVID-19 prevalence. In contrast to country-wide protocols\, Eva allocated Greece’s limited testing resources based upon incoming travellers’ demographic information and testing results from previous travellers. By comparing Eva’s performance against modelled counterfactual scenarios\, we show that Eva identified 1.85 times as many asymptomatic\, infected travellers as random surveillance testing\, with up to 2-4 times as many during peak travel\, and 1.25-1.45 times as many asymptomatic\, infected travellers as testing policies that only utilize epidemiological metrics. We demonstrate that this latter benefit arises\, at least partially\, because population-level epidemiological metrics had limited predictive value for the actual prevalence of SARS-CoV-2 among asymptomatic travellers and exhibited strong country-specific idiosyncrasies in the summer of 2020. Our results raise serious concerns on the effectiveness of country-agnostic internationally proposed border control policies that are based on population-level epidemiological metrics. Instead\, our work represents a successful example of the potential of reinforcement learning and real-time data for safeguarding public health.\nPaper Link: https://www.nature.com/articles/s41586-021-04014-z\n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-hamsa-bastani/
CATEGORIES:IORA Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211008T100000
DTEND;TZID=Asia/Singapore:20211008T110000
DTSTAMP:20260417T124136
CREATED:20210812T024626Z
LAST-MODIFIED:20211001T023034Z
UID:14190-1633687200-1633690800@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Van-Anh Truong
DESCRIPTION:Van-Anh Truong joined the Industrial Engineering and Operations Research Department at the Columbia University in 2010. She received a Bachelor’s degree from University of Waterloo in Mathematics in 2002\, and a Ph.D. from Cornell University in Operations Research in 2007. Before coming to Columbia\, she was a quantitative associate at Credit Suisse\, and a quantitative researcher at Google. \nShe is interested in a broad class of problems that arise in Supply Chain Management\, Healthcare\, and Business Analytics.   These problems address decision making under uncertainty in information-rich and highly dynamic environments.  Her recent work focuses on real-time optimization approaches for large e-commerce\, healthcare\, and service applications. \nHer research is supported by an NSF Faculty Early Career Development (CAREER) Award.  She is currently an Associate Editor for Operations Research\, MSOM\, and Naval Research Logistics. \n\n\n\nName of Speaker\nA/P Van-Anh Truong\n\n\nSchedule\nFriday 8 October 2021\, 10am (Singapore time)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZEpde2trzguGdY7aOovoJ5HUtpniis4z_MM\n\n\nTitle\nProphet Inequality with Correlated Arrival Probabilities\, with Application to Two Sided Matchings\n\n\nAbstract\nThe classical Prophet Inequality arises from a fundamental problem in optimal-stopping theory. In this problem\, a gambler sees a finite sequence of independent\, non-negative random variables. If he stops the sequence at any time\, he collects a reward equal to the most recent observation. The Prophet Inequality states that\, knowing the distribution of each random variable\, the gambler can achieve at least half as much reward in expectation\, as a prophet who knows the entire sample path of random variables (Krengel and Sucheston 1978). In this paper\, we prove a corresponding bound for correlated non-negative random variables. We analyze two methods for proving the bound\, a constructive approach\, which produces a worst-case instance\, and a reductive approach\, which characterizes a certain submartingale arising from the reward process of our online algorithm. We apply this new prophet inequality to the design of algorithms for a class of two-sided bipartite matching problems that underlie online task assignment problems. In these problems\, demand units of various types arrive randomly and sequentially over time according to some stochastic process. Tasks\, or supply units\, arrive according to another stochastic process. Each demand unit must be irrevocably matched to a supply unit or rejected. The match earns a reward that depends on the pair. The objective is to maximize the total expected reward over the planning horizon. The problem arises in mobile crowd-sensing and crowd sourcing contexts\, where workers and tasks must be matched by a platform according to various criteria. We derive the first online algorithms with worst-case performance guarantees for our class of two-sided bipartite matching problems.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-van-anh-truong/
CATEGORIES:IORA Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211015T100000
DTEND;TZID=Asia/Singapore:20211015T113000
DTSTAMP:20260417T124136
CREATED:20210812T024727Z
LAST-MODIFIED:20211008T074956Z
UID:14192-1634292000-1634297400@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Yilun Chen
DESCRIPTION:Yilun Chen is an assistant professor in the School of Data Science at CUHK Shenzhen (currently on leave)\, and a postdoctoral researcher at Columbia Business School. He obtained a PhD in Operations Research at Cornell University in 2021. Yilun’s research interest lies broadly in applied probability\, with a specific focus on decision-making under uncertainty and its wide applications in Operations Research/Operations Management. His work was awarded first place in the 2019 INFORMS Nicholson Best Student Paper Competition. \n  \n\n\n\nName of Speaker\nDr Chen Yilun\n\n\nSchedule\nFriday 15 October\, 10am – 11.30am \n(60 min talk + 30 min Q&A)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZcrde-rrDMsHNRtAlSfk8uzRR1XMiPuCd-f\n\n\n Title\nTractability of high-dimensional online decision-making with limited action changes\n\n\nAbstract\nData-driven online decision-making tasks arise frequently in various practical settings in OR/ OM\, finance\, healthcare\, etc. Such tasks often boil down to solving certain stochastic dynamic programs (DPs) with prohibitively large state space and complicated dynamics\, suffering from the computational challenge known as the curse of dimensionality. Existing approaches (e.g. ADP\, deep learning) typically focus on achieving practical success and have limited performance guarantees. We propose an algorithm that overcomes this computational obstacle for a rich class of problems\, subject only to a “limited-action-change’’ constraint\, which incorporate important special cases including optimal stopping and limited-price-change dynamic pricing. Assuming a black-box simulator of the problem’s dynamics\, our algorithm can return epsilon-optimal policies with sample and computational complexity scaling polynomially in T (the time horizon)\, and effectively independent of the underlying state space\, analogous to a “PTAS’’. We further demonstrate that the limited-action-change constraint is crucial for such efficient algorithms to exist through the construction of a hard instance. The recent algorithmic progress in high-dimensional optimal stopping of Chen and Goldberg 21 is a key building block of our algorithm.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-yilun-chen/
CATEGORIES:IORA Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211020T100000
DTEND;TZID=Asia/Singapore:20211020T110000
DTSTAMP:20260417T124136
CREATED:20211014T072811Z
LAST-MODIFIED:20211014T072811Z
UID:14569-1634724000-1634727600@iora.nus.edu.sg
SUMMARY:PhD Oral Defense: Tan Hong Ming
DESCRIPTION:Hong Ming is a final year PhD candidate at the Institute of Operations Research and Analytics\, National University of Singapore. His research interests include stochastic modelling\, information economics\, and decision making under uncertainty. He has won teaching awards and was previously a lecturer at Temasek Polytechnic where he was subject leader and also co-authored three textbooks. \n  \n\n\n\nName of Speaker\nTan Hong Ming\n\n\nSchedule\n20 October 2021\, 10am\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZAldeGqqjIqHN3Ww-3WJ8rbHo9m5QGIMuP-\n\n\n Title\nRisky Investments under Static and Dynamic Information Acquisition\n\n\nAbstract\nThis thesis studies risky investments under different information acquisition processes. Under a dynamic information acquisition process\, the investor’s unconditional expected optimal quantity of information and investment amount are higher than those under the corresponding static information acquisition process. However\, when the initial belief of the investment payoff is either high or low\, static and dynamic information acquisitions provide similar expected results. To incentivize firms to acquire information and invest when the initial belief of a risky investment payoff is mediocre\, governments should allow firms to obtain information dynamically. This has implications on\, e.g.\, clinical trials. Furthermore\, the falling marginal cost of information raises investment amounts and leverage\, which leads to higher losses during crises. Hence\, companies need to understand their tail risks better.
URL:https://iora.nus.edu.sg/events/phd-oral-defense-tan-hong-ming/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211022T100000
DTEND;TZID=Asia/Singapore:20211022T113000
DTSTAMP:20260417T124136
CREATED:20210812T025154Z
LAST-MODIFIED:20211015T034901Z
UID:14194-1634896800-1634902200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Kimon Drakopoulos
DESCRIPTION:Kimon Drakopoulos is an Assistant Professor of Data Sciences and Operations at USC Marshall School of Business\, where he researches complex networked systems\, control of contagion\, information design and information economics. During the Summer of 2020 he served as the Chief Data Scientist of the Greek National COVID-19 Taskforce and Data Science and Operations Advisor to the Greek Prime Minister.  He completed his Ph.D. in the Laboratory for Information and Decision Systems at MIT\, focusing on the analysis and control of epidemics within networks. His current research revolves around controlling contagion\, epidemic or informational as well as the use of information as a lever to improve operational outcomes in the context of testing allocation\, fake news propagation and belief polarization. \n  \n\n\n\nName of Speaker\nKimon Drakopoulos\n\n\nSchedule\n22 October 2021\, 10am – 11.30am \n(60 min talk + 30 min Q&A)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZEqdeqqqzwiEtTecjPommMjKlLsJKr4UArz\n\n\n Title\nWhy Perfect Tests May Not be Worth Waiting For: Information as a Commodity\n\n\nAbstract\nInformation products provide agents with additional information that is used to update their actions. In many situations\, access to such products can be quite limited. For instance\, in epidemics\, there tends to be a limited supply of medical testing kits or tests. These tests are information products because their output of a positive or a negative answer informs individuals and authorities on the underlying state and the appropriate course of action. In this paper\, using an analytical model\, we show how the accuracy of a test in detecting the underlying state affects the demand for the information product differentially across heterogeneous agents. Correspondingly\, the test accuracy can serve as a rationing device to ensure that the limited supply of information products is appropriately allocated to the heterogeneous agents. When test availability is low and the social planner is unable to allocate tests in a targeted manner to the agents\, we find that moderately good tests can outperform perfect tests in terms of social outcome. On the policy side\, we use a numerical study of an evolving epidemic to confirm our theoretically derived insight that in the early stages of an epidemic with low test availability\, releasing a moderately good test can be an optimal strategy. The work is forthcoming in Management Science (FastTrack).\n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-kimon-drakopoulos/
CATEGORIES:IORA Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211029T100000
DTEND;TZID=Asia/Singapore:20211029T113000
DTSTAMP:20260417T124136
CREATED:20210812T025426Z
LAST-MODIFIED:20211021T032942Z
UID:14196-1635501600-1635507000@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Junyu Zhang
DESCRIPTION:Junyu Zhang is an assistant professor at the Department of Industrial Systems Engineering and Management (ISEM)\, National University of Singapore. Before joining NUS\, he was a postdoctoral research fellow at Princeton University\, Department of Electrical and Computer Engineering. He received his Ph.D. degree from University of Minnesota\, Twin Cities\, in 2020. And he received his B.Sc. degree from Peking University in 2015. He is currently focusing on the convergence and complexity of various stochastic optimization and saddle point problems\, as well as their closed related problems in reinforcement learning. \n  \n\n\n\nName of Speaker\nDr Zhang Junyu\n\n\nSchedule\n29 October 2021\, 10am – 11.30am \n(60 min talk + 30 min Q&A)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZYuc-yqqT4tGN3bunp6tZ9-6SjY3JFy4lYq\n\n\n Title\nPrimal-Dual First-Order Methods for Affinely Constrained Multi-Block Saddle Point Problems\n\n\nAbstract\nWe consider the convex-concave saddle point problems where the decision variables are subject to certain multi-block structure and affine coupling constraints\, and the objective function possesses a certain separable structure. Although the minimization counterpart of such a problem has been widely studied under the topics of ADMM\, this minimax problem is rarely investigated. In this paper\, a convenient notion of $\epsilon$-saddle point is proposed\, under which the convergence rate of several proposed algorithms are analyzed. When only one of the decision variables has multiple blocks and affine constraints\, several natural extensions of ADMM are proposed to solve the problem. Depending on the number of blocks and the level of smoothness\, O(1/T) or O(1/\sqrt{T}) convergence rates are derived for our algorithms. When both decision variables have multiple blocks and affine constraints\, a new algorithm called ExtraGradient Method of Multipliers (EGMM) is proposed. Under desirable smoothness conditions\, an O(1/T) rate of convergence can be guaranteed regardless of the number of blocks in the decision variables. An in-depth comparison between EGMM (fully primal-dual method) and ADMM (approximate dual method) is made over the multi-block optimization problems to illustrate the benefits of the EGMM.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-junyu-zhang/
CATEGORIES:IORA Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211105T100000
DTEND;TZID=Asia/Singapore:20211105T113000
DTSTAMP:20260417T124136
CREATED:20210812T025601Z
LAST-MODIFIED:20211028T063827Z
UID:14198-1636106400-1636111800@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Yuqian Xu
DESCRIPTION:Yuqian Xu is an assistant professor of Operations Management at Kenan-Flagler Business School\, University of North Carolina\, Chapel Hill. Her research focuses on understanding worker and consumer behaviors in the banking industry and digital platforms\, in which she investigates both theoretical and empirical problems. Her focus of methodology includes applied probability\, stochastic modeling\, econometrics\, and machine learning. In her research\, she has been collaborating with different companies\, including JD.com\, Tencent\, Bank of China\, etc. She has given talks in different academic\, industry\, and government conferences and organizations\, such as Federal Reserve Bank and China Banking Regulatory Committee. \nHer research has been published in journals including Management Science\, Operations Research\, Production and Operations Management\, etc. She has a B.S. in Mathematics from the Kuang Yaming Honors School of Intensive Instruction in Science and Arts at Nanjing University\, China. She received her Ph.D. degree (Beta Gamma Sigma) in 2017 from NYU Stern School of Business with the Herman E. Krooss Dissertation Award. \n  \n\n\n\nName of Speaker\nDr Xu Yuqian\n\n\nSchedule\n5 November 2021\, 10am – 11.30am \n(60 min talk + 30 min Q&A)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0sdu-rqDMiHNTXwyxJUDNqD7WOMTf-u8dp\n\n\nTitle\nOperational Risk Management: Optimal Inspection Policy\n\n\nAbstract\nOperational risk is one of the major risks in the financial industry; major banks around the world lost nearly $210 billion from operational risk events between 2011 and 2016 (Huber and Funaro 2018) and inspection on operational risk is required by the Basel Committee on Banking Supervision. Motivated by the importance of operational risk and its current industry regulation\, we study how a financial firm can optimally design inspection policies to manage operational risk losses. Specifically\, we propose a continuous-time principal-agent model framework to examine a financial firm’s (principal) optimal inspection policy and their employees’ (agent) effort towards lowering the risk event occurrences. We first consider two commonly used inspection policies\, namely\, random and periodic policies\, and characterize the optimal inspection strategy under each policy. We identify conditions for two different modes of inspection (effort inducement and error correction) as well as nuanced interactions among inspection frequency\, penalty charged on errors\, and wage paid to employees. We then compare the performances of random and periodic policies. We find that contrary to conventional wisdom\, the random policy is not always optimal; it is dominated by the periodic policy if the inspection cost is sufficiently low. Furthermore\, we construct a hybrid policy that strictly dominates random policy and weakly dominates periodic policy\, which suggests that a proper reduction of the random element in the inspection policy can always improve its performance. Finally\, calibrating model parameters using operational risk data from a major bank in China\, we numerically show that our key insights about random\, periodic\, and hybrid policies are robust to various model extensions.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-yuqian-xu/
CATEGORIES:IORA Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211112T100000
DTEND;TZID=Asia/Singapore:20211112T113000
DTSTAMP:20260417T124136
CREATED:20210812T025706Z
LAST-MODIFIED:20211101T084312Z
UID:14199-1636711200-1636716600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Vijay Kamble
DESCRIPTION:Vijay Kamble is an Assistant Professor of Information and Decision Sciences in the College of Business Administration\, and of Computer Science (by courtesy)\, at the University of Illinois at Chicago. He previously obtained his Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2015) and was a postdoc in the Social Algorithms lab at the Management Science and Engineering Dept. of Stanford University (2015-17). \nHis current research interests are in the areas of machine learning\, statistical learning theory\, market design\, and optimization with applications to revenue management\, pricing\, and the design and optimization of online platforms and marketplaces. \n  \n\n\n\nName of Speaker\nDr Vijay Kamble\n\n\nSchedule\n12 November 2021\, 10am – 11.30am \n(60 min talk + 30 min Q&A)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZcsf-CqqT8rHt2NWfT9LgIr1u-ghDpdIlPc\n\n\nTitle\nPseudo-competitive games and algorithmic pricing\n\n\nAbstract\nWith recent advances in artificial intelligence methodologies\, algorithmic pricing in the face of unknown or uncertain demand has become ubiquitous in the practice of revenue management. While such algorithmic approaches are known to satisfy attractive revenue guarantees in well-behaved\, non-strategic environments\, the outcomes arising from such approaches in competitive settings remain poorly understood.  Motivated by the goal of studying outcomes of algortihmic price competition in practical environments\, we study a game of price competition amongst firms selling homogeneous goods\, defined by the property that a firm’s revenue is independent of any competing prices that are strictly lower. We call this the pseudo-competitive property and the games of price competition induced by such revenue functions pseudo-competitive games. \nWe show that this property is induced by any customer choice model involving utility-maximizing choice from an adaptively determined consideration set\, encompassing a variety of empirically validated choice models studied in the literature. For these games\, we show a one-to-one correspondence between pure-strategy local Nash equilibria with distinct prices and the prices generated by the firms sequentially setting local best-response prices in different orders. In other words\, despite being simultaneous-move games\, they have a sequential-move equilibrium structure. Although this structure is attractive from a computational standpoint\, we find that it makes these games particularly vulnerable to the existence of strictly-local Nash equilibria\, in which the price of a firm is only a local best-response to competitors’ prices when a globally optimal response with a potentially unboundedly higher payoff is available. We moreover show\, both theoretically and empirically\, that price dynamics resulting from the firms utilizing gradient-based dynamic pricing algorithms to respond to competition may often converge to such an undesirable outcome. To address this concern\, we finally propose an algorithmic approach that incorporates global experimentation under certain regularity assumptions on the revenue curves. \nThis is joint work with Chamsi Hssaine and Sid Banerjee\, both from Cornell ORIE.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-vijay-kamble/
CATEGORIES:IORA Seminar Series
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211210T140000
DTEND;TZID=Asia/Singapore:20211210T163000
DTSTAMP:20260417T124136
CREATED:20211203T032006Z
LAST-MODIFIED:20211203T032803Z
UID:14663-1639144800-1639153800@iora.nus.edu.sg
SUMMARY:Seminar on IORA Industry Engagement
DESCRIPTION:In conjunction with the launch of our second collaboration project with Kulicke and Soffa\, we would like to invite you to attend a seminar on IORA Industry Engagement.  This will be an in-person seminar at Innovation 4.0 Seminar Room. \n  \n\n\n\nTime\nSchedule\n\n\n2.00 to 2.05pm\nOpening Address\nby IORA Research Director\, Prof Jussi Keppo\n\n\n2.05 to 2.25pm\n\nOverview of Industry Engagements Activities for IORA\nby Executive Director\, Prof Teo Chung Piaw \n\nWith increasing demand from industry and government agencies for research collaboration and technical assistance\, how can IORA align its activities to respond to these challenges\, while maintaining its core focus on pushing the frontier in research?\nOverview of recent projects completed by staff  in the institute\, and key lessons learned\, say from the development of the SIA-NUS Corp Lab\n\n\n\n\n2.45 to 3.45pm\n\nProjects with K&S \n\nApplication of Machine Learning in Smart Manufacturing\nSmart Manufacturing through Predictive Process Monitoring using Machine Learning\n\n\n\n\n3.45 to 4.00pm\n\nLaunch of Second Collaborative Project with K&S\n\n\n4.00 to 4.30pm\n\nTour of SIA-NUS Digital Aviation Corporate Laboratory\n\n\n\nRegister here https://forms.office.com/r/PLvWfVR8sy \n \n 
URL:https://iora.nus.edu.sg/events/seminar-on-iora-industry-engagement/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220218T100000
DTEND;TZID=Asia/Singapore:20220218T113000
DTSTAMP:20260417T124136
CREATED:20211216T014818Z
LAST-MODIFIED:20220211T061320Z
UID:14703-1645178400-1645183800@iora.nus.edu.sg
SUMMARY:IORA Seminar Series  - Spyros Zoumpoulis
DESCRIPTION:Spyros Zoumpoulis is an assistant professor of Decision Sciences at INSEAD. His research is on using analytics to optimize decision making\, with applications in marketing\, healthcare\, and revenue management. His current focus is on investigating how to design\, and use data from\, experiments in order to make optimal personalized decisions\, as well as how to evaluate policies that make personalized decisions\, such as targeting decisions in marketing and personalized treatment decisions in healthcare. \nMore generally\, he is broadly interested in problems at the interface of learning with data and decision making. His research has appeared in leading management science academic journals such as Management Science and Operations Research. \nSpyros has worked with companies including Microsoft\, LinkedIn\, IBM\, Oracle\, and Accenture and serves on the advisory board of start-ups in the areas of his expertise. At INSEAD\, he teaches the MBA core course on uncertainty\, data and judgment\, the MBA electives on data science for business and decision models\, the MBA business foundations course on quantitative methods\, the PhD courses on probability and statistics\, and the INSEAD-Sorbonne business foundations course on uncertainty\, data and judgment. He has won the Dean’s Commendation for Excellence in MBA Teaching award numerous times and has been nominated for the best MBA elective professor award. \nSpyros received the B.S.\, M.Eng.\, and Ph.D. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology. \n  \n\n\n\nName of Speaker\n Dr Spyros Zoumpoulis\n\n\nSchedule\n18 February 2022\, 10am – 11.30am \n(60 min talk + 30 min Q&A) \n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZIocu2rpzwtGNDoe-8hOR8eA_-nL7H_Fp6r\n\n\nTitle \nQuantifying the Benefits of Targeting for Pandemic Response\n\n\nAbstract\nProblem definition: To respond to pandemics such as COVID-19\, policy makers have relied on interventions that target specific population groups or activities. Since targeting is potentially contentious\, rigorously quantifying its benefits is critical for designing effective and equitable pandemic control policies. \nMethodology/results: We propose a flexible modeling framework and a set of associated algorithms that compute optimally targeted\, time-dependent interventions that coordinate across two dimensions of heterogeneity: age of different groups and the specific activities that individuals engage in during the normal course of a day. We showcase a complete implementation focused on the Île-de-France region of France\, based on commonly available public data. We find that targeted policies generate substantial complementarities that lead to Pareto improvements\, reducing the number of deaths and the economic losses\, as well as the time in confinement for each age group. Optimized dual-targeted policies are interpretable: by fitting decision trees to our raw policy’s decisions across many problem instances\, we find that a feature corresponding to the ratio of marginal economic value prorated by social contacts is highly salient in explaining the confinements that any group – activity pair experiences. We also quantify the impact of fairness requirements that explicitly limit the differential treatment of distinct groups\, and find that satisfactory trade-offs are achievable through limited targeting. \nImplications: Given that some amount of targeting of activities and age groups is already in place in real-world pandemic responses\, our framework highlights the significant benefits in explicitly and transparently modelling targeting and identifying the interventions that rigorously optimize overall societal welfare.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-spyros-zoumpoulis/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/Spyros-Zoumpoulis-pic.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220225T100000
DTEND;TZID=Asia/Singapore:20220225T113000
DTSTAMP:20260417T124136
CREATED:20211216T014656Z
LAST-MODIFIED:20220221T025401Z
UID:14701-1645783200-1645788600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Yehua Wei
DESCRIPTION:Yehua Wei is an Associate Professor in the Decision Sciences area at Fuqua School of Business. He received his Ph.D. in Operations Research from MIT in 2013. His research interest can be broadly defined as decisions under uncertainty\, including optimization problems on operational and strategical levels. More recently\, he has been working on topics in dynamic resource allocation\, vehicle routing\, strategic routing\, and e-commerce fulfillment. \n\n\n\nName of Speaker\n  Wei Yehua\n\n\nSchedule\n 25 February 2022\, 10am – 11.30am \n(60 min talk + 30 min Q&A) \n\n\nLink to register\nhttps://nus-sg.zoom.us/meeting/register/tZctdOmorjIjGdUto_SJO3EzN_kdorcJyJVr\n\n\nTitle\n Approximate Submodularity in Network Design\n\n\nAbstract\nNetwork design problems are ubiquitous in long term planning for modern marketplaces\, where firms constantly innovate new ways to match supply and demand. They are often challenging to solve due to the problem scale and the uncertainties that affect the decisions. In this talk\, we establish a novel structural property for a large class of network design problems. The property can be interpreted as an approximate form of submodularity\, where local changes in the objective function can be used to bound global changes. We use this structure to analyze simple heuristics and establish theoretical guarantees for network design problems in e-retailing\, online market platforms\, and manufacturing. Further\, using our analysis\, we identify new heuristics for solving network design problems that lead to an order of magnitude gains in computational efficiency\, without loss of optimization performance. This is a joint work with Levi DeValve (Chicago) and Sasa Pekec (Duke).
URL:https://iora.nus.edu.sg/events/iora-seminar-series-yehua-wei/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/Pic-Wei-YH.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220311T100000
DTEND;TZID=Asia/Singapore:20220311T113000
DTSTAMP:20260417T124136
CREATED:20211216T015014Z
LAST-MODIFIED:20220307T054220Z
UID:14705-1646992800-1646998200@iora.nus.edu.sg
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:20260417T124136
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:20260417T124136
CREATED:20211216T015847Z
LAST-MODIFIED:20220311T081817Z
UID:14711-1647597600-1647603000@iora.nus.edu.sg
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:20260417T124136
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220401T100000
DTEND;TZID=Asia/Singapore:20220401T113000
DTSTAMP:20260417T124136
CREATED:20211216T020545Z
LAST-MODIFIED:20220323T080818Z
UID:14715-1648807200-1648812600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Ruoxuan Xiong
DESCRIPTION:Ruoxuan Xiong is an assistant professor in the Department of Quantitative Theory and Methods at Emory University. She completed her Ph.D. in Management Science and Engineering from Stanford University in 2020\, and was a postdoctoral fellow at the Stanford Graduate School of Business from 2020 to 2021. Her research is at the intersection of econometrics and operations research\, focusing on factor modeling\, causal inference\, and experimental design\, and with applications in finance and healthcare. Her work was awarded the Honorable Mention in the 2019 INFORMS George Nicholson Student Paper Competition\, and was in the finalists of the 2020 MSOM Student Paper Competition. \n\n\n\nName of speaker\nRuoxuan Xiong\n\n\nSchedule \n1 April 2022\, 10am – 11.30am\n\n\nLink to register \nhttps://nus-sg.zoom.us/meeting/register/tZUpc-uqqzkvHNTjASt7gjozPEloElm7Uuzg\n\n\nTitle of talk\nOptimal experimental design for staggered rollouts\n\n\nAbstract\nIn this paper\, we study the problem of designing experiments that are conducted on a set of units\, such as users or groups of users in an online marketplace\, for multiple time periods such as weeks or months. These experiments are particularly useful to study the treatments that have causal effects on both current and future outcomes (instantaneous and lagged effects). The design problem involves selecting a treatment time for each unit\, before or during the experiment\, in order to most precisely estimate the instantaneous and lagged effects\, post experimentation. This optimization of the treatment decisions can directly minimize the opportunity cost of the experiment by reducing its sample size requirement. The optimization is an NP-hard integer program for which we provide a near-optimal solution\, when the design decisions are performed all at the beginning (fixed-sample-size designs). Next\, we study sequential experiments that allow adaptive decisions during the experiments\, and also potentially early stop the experiments\, further reducing their cost. However\, the sequential nature of these experiments complicates both the design phase and the estimation phase. We propose a new algorithm\, PGAE\, that addresses these challenges by adaptively making treatment decisions\, estimating the treatment effects\, and drawing valid post-experimentation inference. PGAE combines ideas from Bayesian statistics\, dynamic programming\, and sample splitting. Using synthetic experiments on real data sets from multiple domains\, we demonstrate that our proposed solutios for fixed-sample-size and sequential experiments reduce the opportunity cost of the experiments by over 50% and 70%\, respectively\, compared to benchmarks.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-ruoxuan-xiong/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/xiong-ruoxuan-pic.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220408T100000
DTEND;TZID=Asia/Singapore:20220408T113000
DTSTAMP:20260417T124136
CREATED:20211216T020639Z
LAST-MODIFIED:20220401T084157Z
UID:14717-1649412000-1649417400@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Stefanus Jasin
DESCRIPTION:Stefanus Jasin is an Associate Professor of Technology and Operations at Stephen M. Ross Business School\, University of Michigan. His research focuses on developing tools/algorithms for predictive and prescriptive analytics\, with recent applications in pricing and revenue management\, assortment optimization\, supply chain management\, e-commerce/omni-channel logistics\, and online learning. He is currently serving as the Department Editor for Revenue Management and Market Analytics at POMS. He also serves as an Associate Editor at Management Science\, Operations Research\, Manufacturing and Service Operations Management\, and Naval Research Logistics. \n\n\n\nName of speaker\nStefanus Jasin\n\n\nSchedule \n8 April 2022\, 10am – 11.30am\n\n\nLink to register  \n \nhttps://nus-sg.zoom.us/meeting/register/tZ0rcOmsrzkvEtbis9jZu_QqWm6iTqZVAnR5\n\n\nTitle of talk\nRevenue Management Meets Inventory Management\n\n\nAbstract\nHistorically\, Revenue Management (RM) and Inventory Management have developed as two separate fields. Although they sometimes share similar research questions (e.g.\, pricing)\, they do not always share either the same focus or methodology. In this talk\, I will give an overview of several recent works that focus on applying RM techniques to inventory management problems\, all of which are motivated by applications in retail. Given enough time\, the plan is to divide the talk into two parts. The first part will focus on a joint inventory and pricing problem with one warehouse and multiple stores\, in which the retailer needs to make a one-time decision on the amount of inventory to be placed at the warehouse at the beginning of the selling season\, followed by periodic joint replenishment and pricing decisions for each store throughout the season. We study the performance of heuristic controls based on a deterministic/fluid relaxation of the original stochastic problem. Our contributions are two-fold. We first show that simple re-optimization of deterministic/fluid problems may yield a very poor performance by causing a “spiraling down” movement in price trajectory\, which in turn yields a “spiraling up” movement in expected lost sales quantity (i.e.\, lost sales quantity keeps going up as we continue re-optimizing the model). This cautions against a naive use of simple re-optimizations in the joint inventory and pricing setting with lost sales. Second\, we propose a better heuristic by combining four ideas: (1) order-up-to control\, (2) linear rate adjustment\, (3) replenishment batching\, and (4) random errors averaging. We show for a particular choice of control parameters that the heuristic is close to optimal when demand is Poisson and the annual market size for each store is large. In the second part of the talk\, I will briefly discuss other works along the same theme\, including some recent papers that use Lagrangian-based methods and another paper that uses a fluid approximation for a joint pricing and inventory problem with stochastic purchase returns and lost sales. I will discuss some insights on lessons learned when applying RM techniques in inventory-related problems. Overall\, these works highlight the potential in adopting RM techniques in solving complex inventory problems.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-stefanus-jasin/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/12/JasinStefanus.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20220422T100000
DTEND;TZID=Asia/Singapore:20220422T113000
DTSTAMP:20260417T124136
CREATED:20220406T060607Z
LAST-MODIFIED:20220406T061314Z
UID:15262-1650621600-1650627000@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Chen Ying
DESCRIPTION:Dr. Ying Chen is a financial statistician and data scientist. She develops statistical modelling and machine learning methods customized for nonstationary\, high frequency and large dimensional complex data such as cryptocurrency\, limit order book\, and renewable energy. She also works on business intelligence\, forecasting\, text mining and sentiment analysis\, and network analysis. Dr. Chen is Associate Professor in Department of Mathematics and Joint Appointee in Risk Management Institute\, National University of Singapore. She also holds Courtesy Appointment in Econ and DSDS. \nWebpage: https://blog.nus.edu.sg/matcheny/ \n\n\n\nName of speaker\nChen Ying\n\n\nSchedule \n22 April 2022\, 10am – 11.30am\n\n\nLink to register  \n \nhttps://nus-sg.zoom.us/meeting/register/tZEucO2pqjosGNPn3FVvmPCZOZrX9T1faGx0\n\n\nTitle of talk\nPolicy Effectiveness on the Global Covid-19 Pandemic and Unemployment Outcomes: A Large Mixed Frequency Spatial Approach\n\n\nAbstract\nWe propose a mixed frequency spatial VAR (MF-SVAR) modeling framework to measure the effectiveness of policies conditional on the spillover and diffusion effects of the global pandemic and unemployment. We study the effects of two aspects of policy effectiveness\, namely policy start date and policy timeliness\, from a spatio-temporal perspective. The spatial panel data contain weekly new case growth rates and monthly unemployment rate changes for 68 countries across six continents at mixed frequencies from January 2020 to August 2021. We find that government policies have a significant impact on the growth of new cases\, but only a marginal effect on the change in unemployment rates. A policy’s start date is critical for its effectiveness. In terms of both immediate impact on the near term and total impact over the following four weeks\, starting a policy in the 4th week of a month is most effective at reducing the growth of new cases. At the same time\, starting in the 2nd or 3rd week is counterproductive for a one-time policy start date. In addition\, our estimates suggest that the spillover and diffusion effects are much stronger than a country’s temporal effect during a global pandemic\, both for new case growth and changes in unemployment. We also find that new case growth influences changes in unemployment\, but not vice versa. Counterfactual experiments provide further evidence of policy effectiveness in various scenarios and also reveal the main risk-vulnerable and risk-spillover countries. This is a joint work with Xiaoyi Han\, Yanli Zhu and Yijiong Zhang. The paper is available at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4049509
URL:https://iora.nus.edu.sg/events/iora-seminar-series-chen-ying/
CATEGORIES:IORA Seminar Series
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DTSTART;TZID=Asia/Singapore:20220506T100000
DTEND;TZID=Asia/Singapore:20220506T113000
DTSTAMP:20260417T124136
CREATED:20211216T020732Z
LAST-MODIFIED:20220430T132246Z
UID:14719-1651831200-1651836600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Basak Kalkanci
DESCRIPTION:Basak Kalkanci is an associate professor of operations management at the Scheller College of Business at Georgia Tech. Her research focuses on socially and environmentally responsible supply chain management\, and contracting and the role of information in decentralized supply chains. Her research aims to lay the necessary groundwork to enable real-time measurement and management of environmental and social impacts in global supply chains. She earned her Ph.D in Management Science and Engineering from Stanford University and was a postdoctoral associate at the Massachusetts Institute of Technology prior to joining Georgia Tech. Her work appeared in premier journals including Management Science\, Operations Research\, Manufacturing & Service Operations Management\, and Production and Operations Management\, and has been funded by the National Science Foundation. She is the recipient of the Paul Kleindorfer Award in Sustainability (2020)\, Alliance for Research on Corporate Sustainability Emerging Sustainability Scholar Award (2019)\, Georgia Power Professor of Excellence (2015)\, Management Science Meritorious Service Award (2015\, 2017\, 2019\, 2020)\, and M&SOM Meritorious Service Award (2014). She serves as a Senior Editor for the Production and Operations Management Journal and as an Associate Editor for M&SOM. \n\n\n\nName of speaker\nBasak Kalkanci\n\n\nSchedule \n6 May 2022\, 10am – 11.30am\n\n\nLink to register  \n \nhttps://nus-sg.zoom.us/meeting/register/tZcqfuGqqzsrG927xqVoLigsdXKlE82X8kKi\n\n\nTitle of talk\nHow Transparency into Internal and External Responsibility Initiatives Influences Consumer Choice\n\n\nAbstract\nAmid growing calls for transparency and social and environmental responsibility\, companies are employing different strategies to improve consumer perceptions of their brands. Some pursue internal initiatives that reduce their negative social or environmental impacts through responsible operations practices (such as paying a living wage to workers  or engaging in environmentally sustainable manufacturing). Others pursue external responsibility initiatives (such as philanthropy or cause-related marketing). Through two experiments conducted in the field and complementary online experiments\, we compare how transparency into these internal and external initiatives affects customer perceptions and sales. We find that transparency into both internal and external responsibility initiatives tends to dominate generic brand marketing in motivating consumer purchases\, supporting the view that consumers take companies’ responsibility efforts into account in their decision making. Furthermore\, the results provide converging evidence that transparency into a company’s internal responsibility practices can be at least as motivating of consumer sales as transparency into its external responsibility initiatives\, incrementally increasing a consumer’s probability of purchase by 6.40% and 45.85% across our two field experiments\, conducted in social and environmental domains\, respectively. Our results suggest that it may be in the interest of both business and society for managers to prioritize internal responsible operations initiatives to achieve both top- and bottom-line benefits while mitigating social and environmental harms.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-andrew-lim/
CATEGORIES:IORA Seminar Series
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