BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//IORA - Institute of Operations Research and Analytics - ECPv6.15.11//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:IORA - Institute of Operations Research and Analytics
X-ORIGINAL-URL:https://iora.nus.edu.sg
X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Singapore
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:+08
DTSTART:20210101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221209T140000
DTEND;TZID=Asia/Singapore:20221209T150000
DTSTAMP:20260417T132549
CREATED:20221201T075852Z
LAST-MODIFIED:20221201T080015Z
UID:16468-1670594400-1670598000@iora.nus.edu.sg
SUMMARY:DAO-IORA joint seminar: Ethan X. Fang
DESCRIPTION:Ethan X. Fang is an Assistant Professor of Biostatistics & Bioinformatics at Duke Medical School and affiliated with Decision Sciences of Fuqua Business School and Rhodes Information Initiative at Duke University. He works on different data science problems from computational and statistical perspectives. Before joining Duke\, he was an assistant professor of Statistics at Penn State. He got his PhD from Princeton University under the direction of Han Liu and Robert Vanderbei\, and got his Bachelor’s degree from National University of Singapore under the direction of Kim-Chuan Toh. His works have appeared at top venues in different areas such as statistics\, optimization\, machine learning\, and operations research. He received 2016 Best Paper Prize in Continuous Optimization for Young Researchers (1 paper selected in 3 years). \n  \n\n\n\nVenue \nBIZ1 – 0204\n\n\nLink to Register\nhttps://forms.office.com/r/WgcFdUQQYW\n\n\nTitle\nInference for Ranking Problems\n\n\nAbstract\nWe propose a novel combinatorial inference framework to conduct general uncertainty quantification in ranking problems. We consider the widely adopted Bradley-Terry-Luce (BTL) model\, where each item is assigned a positive preference score that determines the Bernoulli distributions of pairwise comparisons’ outcomes. Our proposed method aims to infer the general ranking properties of the BTL model. The general ranking properties include the “local” properties such as if an item is preferred over another and the “global” properties such as if an item is among the top K-ranked items. We further generalize our inferential framework to multiple testing problems where we control the false discovery rate (FDR) and apply the method to infer the top-K ranked items. We also derive the information-theoretic lower bound to justify the minimax optimality of the proposed method. We conduct extensive numerical studies using both synthetic and real data sets to back up our theory.\n\n\n\nYou may contact WONG Cecilia/TAN Dorothy at 6516 6225/6516 3067 for enquiries.
URL:https://iora.nus.edu.sg/events/dao-iora-joint-seminar-ethanfang/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20221213T100000
DTEND;TZID=Asia/Singapore:20221213T113000
DTSTAMP:20260417T132549
CREATED:20220812T035011Z
LAST-MODIFIED:20221211T084251Z
UID:15982-1670925600-1670931000@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Saif Benjaafar
DESCRIPTION:Saif Benjaafar is McKnight Presidential Endowed Professor and Distinguished McKnight University Professor at the University of Minnesota. He is Head of the Department of Industrial & Systems Engineering at the University of Minnesota\, where he also directs the Initiative on the Sharing Economy. He is a founding member of the Singapore University of Technology and Design where he served as Head of Engineering Systems and Design. He is the Editor in Chief of the INFORMS journal Service Science. He serves on the board of directors of Hourcar\, a social car sharing organization. His research is in the area of operations broadly defined\, with a current focus on sustainable operations and innovation in business models\, including sharing economy\, on-demand services\, and digital marketplaces. He is a Fellow of INFORMS and IISE. \n\n\n\nVenue \nBIZ 1 – 0304\n\n\nLink to Register \n(Hybrid session)\nhttps://nus-sg.zoom.us/meeting/register/tZEqdumsqTIqHdynlcuEWYlO-g6cmUD5yg1t\n\n\nTitle\nDimensioning and Pricing of Shared Vehicle Networks\n\n\nAbstract\nIn the first part of the talk\, we consider the problem of optimal fleet sizing (dimensioning) in an on-demand and one way vehicle sharing system. We leverage a property of closed queueing networks that relates the dynamics of a network with K items to one with K-1 items to obtain explicit and closed form lower and upper bounds on the optimal number of vehicles that are asymptotically exact. We use the bounds to show that buffer capacity (capacity in excess of the nominal load) can be expressed in terms of three explicit terms that can be interpreted as follows: (1) standard buffer capacity that is protection against randomness in demand and rental times\, (2) buffer capacity that is protection against vehicle roaming\, and (3) a correction term. We show that the capacity needed to buffer against vehicle roaming can be substantial even in systems with vanishingly small demand. In the second part of the talk\, give a fixed fleet size\, we consider the dynamic pricing in such a network and show that a static pricing policy that arises from solving a maximum flow relaxation of the problem guarantees a performance ratio of order 1- O(1/) where K is the number of vehicles. The approach used\, which leverages the same property of closed queueing networks used in the dimensioning problem\, is startingly simple and yields performance guarantees that are tighter than those previously obtained in the literature. Time permitting\, we will also discuss ongoing work that considers dimensioning and pricing in other settings with spatial queueing network features. \n  \n(The talk will draw on material from the following two papers: \nhttps://pubsonline.informs.org/doi/epdf/10.1287/mnsc.2021.3957\, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3998297 and \nhttps://papers.ssrn.com/sol3/papers.cfm?abstract_id=4130757. \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-saif-benjaafar/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230127T100000
DTEND;TZID=Asia/Singapore:20230127T113000
DTSTAMP:20260417T132549
CREATED:20221101T081834Z
LAST-MODIFIED:20230110T031345Z
UID:16411-1674813600-1674819000@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Ville Satopaa
DESCRIPTION:TBA
URL:https://iora.nus.edu.sg/events/iora-seminar-series-ville-satopaa/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230207T100000
DTEND;TZID=Asia/Singapore:20230207T113000
DTSTAMP:20260417T132549
CREATED:20230110T031633Z
LAST-MODIFIED:20230130T034859Z
UID:16647-1675764000-1675769400@iora.nus.edu.sg
SUMMARY:IORA Seminar Series - Timothy Chan
DESCRIPTION:Timothy Chan is the Associate Vice-President and Vice-Provost\, Strategic Initiatives at the University of Toronto\, the Canada Research Chair in Novel Optimization and Analytics in Health\, a Professor in the department of Mechanical and Industrial Engineering\, and a Senior Fellow of Massey College. His primary research interests are in operations research\, optimization\, and applied machine learning\, with applications in healthcare\, medicine\, sustainability\, and sports. He received his B.Sc. in Applied Mathematics from the University of British Columbia (2002)\, and his Ph.D. in Operations Research from the Massachusetts Institute of Technology (2007). Before coming to Toronto\, he was an Associate in the Chicago office of McKinsey and Company (2007-2009)\, a global management consulting firm. During that time\, he advised leading companies in the fields of medical device technology\, travel and hospitality\, telecommunications\, and energy on issues of strategy\, organization\, technology and operations. \n  \n\n\n\nName of Speaker\nTimothy Chan\n\n\nSchedule\nTuesday 7 February 2023\, 10am – 1130am\n\n\nVenue \nBIZ2 4-13A (BIZ 2\, level 4 Seminar Room)\n\n\nLink to Register \n(Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZcpdO2tpz8jEt3qhGZYuJ2GhZEuzV8r3skw\n\n\nTitle\nGot (optimal) milk?\n\n\nAbstract\nHuman donor milk is considered the ideal nutrition for millions of infants that are born preterm each year. Donor milk is collected\, processed\, and distributed by milk banks. The macronutrient content of donor milk is directly linked to infant brain development and can vary substantially across donations\, which is why multiple donations are typically pooled together to create a final product. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content of donor milk\, which means pooling is done heuristically. We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of deposits and then optimally combine them in pools\, respectively. In collaboration with our partner milk bank\, we collect a data set of milk to train our predictive models. We rigorously simulate milk bank practices to fine-tune our optimization models and evaluate operational scenarios such as changes in donation habits during the COVID-19 pandemic. Finally\, we conduct a year-long trial implementation\, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets between 31% to 76% more often than the baseline\, while taking 67% less recipe creation time. This is the first paper in the broader blending literature that combines machine learning and optimization. We demonstrate that such pipelines are feasible to implement and can yield significant improvements over current practices. Our insights can guide practitioners in any application area seeking to implement machine learning and optimization-based decision-support.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-timothy-chan/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230210T100000
DTEND;TZID=Asia/Singapore:20230210T113000
DTSTAMP:20260417T132549
CREATED:20230110T031729Z
LAST-MODIFIED:20230213T021652Z
UID:16650-1676023200-1676028600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Antoine Désir
DESCRIPTION:Antoine is an Assistant professor of Technology and Operations Management at INSEAD. His research applies mathematical modeling and analytics to operations management problems with an aim to: (1) quantify fundamental tradeoffs\, and (2) design efficient data-driven algorithms to support operational decisions. More precisely\, he focuses on revenue management and choice modeling with applications such as online advertising. He was an MSOM student paper finalist in 2014 and 2017 and a Nicholson student paper finalist in 2014 and 2015. He spent a year as a post-doctoral researcher at Google NYC. \n\n\n\nName of Speaker\nAntoine Desir\n\n\nSchedule\nFriday 10 February 2023\, 10.00am – 11.30am\n\n\nVenue \nI4-01-03 (Innovation 4.0\, level 1 Seminar Room)\n\n\nLink to Register \n(Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZwtdOyoqjIuGtIP444shDdv-KQrbeOQcFl3\n\n\nTitle\nRepresenting Random Utility Choice Models with Neural Networks\n\n\nAbstract\nMotivated by the successes of deep learning\, we propose a class of neural network-based discrete choice models\, called RUMnets\, which is inspired by the random utility maximization (RUM) framework. This model formulates the agents’ random utility function using the sample average approximation (SAA) method. We show that RUMnets sharply approximate the class of RUM discrete choice models: any model derived from random utility maximization has choice probabilities that can be approximated arbitrarily closely by a RUMnet. Reciprocally\, any RUMnet is consistent with the RUM principle. We derive an upper bound on the generalization error of RUMnets fitted on choice data\, and gain theoretical insights on their ability to predict choices on new\, unseen data depending on critical parameters of the dataset and architecture.  By leveraging open-source libraries for neural networks\, we find that RUMnets outperform other state-of-the-art choice modeling and machine learning methods by a significant margin on two real-world datasets. This is joint work with Ali Aouad (LBS). \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-antoine-desir/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230303T100000
DTEND;TZID=Asia/Singapore:20230303T113000
DTSTAMP:20260417T132549
CREATED:20230209T084917Z
LAST-MODIFIED:20230227T024024Z
UID:16703-1677837600-1677843000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series – Jing Wu
DESCRIPTION:Prof. Jing Wu is an Associate Professor in the Department of Decision Sciences and Managerial Economics at the Chinese University of Hong Kong (CUHK) Business School. He receives his Ph.D. (major in operations management\, minor in economics & finance) and MBA from the University of Chicago Booth School of Business and his bachelor’s degree in Electronic Engineering from Tsinghua University. Prof. Wu’s primary research fields are the operations-finance interface\, global supply chains\, FinTech\, and business intelligence. His papers are published in leading journals such as Management Science\, M&SOM\, and POMS. His articles appear in business magazines such as MIT Sloan Management Review\, the Economist\, and Forbes. In particular\, his quantitative research findings on the supply chain impact of COVID-19 and the Trade War have been reported by over 400 media outlets in over 20 countries worldwide. He is a Senior Editor for Production and Operations Management and serves on the Editorial Board for Journal of Operations Management. He has been a committee/track chair for leading international academic conferences such as INFORMS and POMS meetings. Before academia\, he worked as a quantitative strategist at Deutsche Bank New York. \n\n\n\nName of Speaker\nJing Wu\n\n\nSchedule\nFriday 3 March 2023\, 10.00am – 11.30am\n\n\nVenue \nI4-01-03 (Innovation 4.0\, level 1 Seminar Room)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZYkc-yurjsuGtSdKCtZD62yxhugAKzE-6Nh\n\n\nTitle\nThe Golden Revolving Door: Hedging through Hiring Government Officials\n\n\nAbstract\nUsing both the onset of the US-China trade war in 2018 and the most recent Russia-Ukraine conflict and associated trade tensions\, we show that government-linked firms increase their importing activity by roughly 33% (t=4.01) following the shock\, while non-government linked firms trading to the same countries do the opposite\, decreasing activity. These increases appear targeted\, in that we see no increase for government-linked supplier firms generally to other countries (even countries in the same regions) at the same time\, nor of these same firms in these regions at other times of no tension. In terms of mechanism\, government supplier-linked firms are nearly twice as likely to receive tariff exemptions as equivalent firms doing trade in the region who are not also government suppliers. More broadly\, these effects are increasing in level of government connection. For example\, firms that are geographically closer to the agencies to which they supply increase their imports more acutely. Using micro-level data\, we find that government supplying firms that recruit more employees with past government work experience also increase their importing activity more – particularly when the past employee worked in a contracting role. Lastly\, we find evidence that this results in sizable accrued benefits in terms of firm-level profitability\, market share gains\, and outsized stock returns.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-jing-wu/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230317T100000
DTEND;TZID=Asia/Singapore:20230317T113000
DTSTAMP:20260417T132549
CREATED:20230220T060513Z
LAST-MODIFIED:20230309T050139Z
UID:16733-1679047200-1679052600@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series – Karthyek Murthy
DESCRIPTION:Karthyek Murthy serves as an Assistant Professor in Singapore University of Technology & Design. His research interests lie in data-driven operations research. Prior to joining SUTD\, he was a postdoctoral researcher in Columbia University IEOR department. His research has been recognised with 2021 INFORMS Junior Faculty Forum (JFIG) Paper competition (Third place)\, 2019 WSC Best Paper Award\, and IBM PhD fellowship. Karthyek serves as an Associate Editor for the INFORMS journal Stochastic Systems and as a judge for the INFORMS Nicholson student paper competition. \n\n\n\nName of Speaker\nKarthyek Murthy\n\n\nSchedule\nFriday 17 March 2023\, 10.00am – 11.30am\n\n\nVenue \nI4-01-03 (Innovation 4.0\, level 1 Seminar Room)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0pd-mrqT8sHdHKwZT0EH5wYD4D-WJxsVNx\n\n\nTitle\nLocally robust models for optimization under tail-based data imbalance\n\n\nAbstract\nMany problems in operations and risk management require the familiar “estimate\, then optimize” workflow involving a model estimation from data in the first step before plugging in the trained model to solve various optimization tasks. In this talk\, we first give the ingredients for constructing locally robust optimization formulations in which the first step model estimation has no effect\, locally\, on the optimal solutions. Then delving specifically into optimization problems affected by tail-based data imbalance\, we show that this local sensitivity translates to improved out-of-sample performance freed from the first-order impact of model errors caused by model selection and misspecification biases that are especially difficult to avoid when performing estimation with imbalanced data. The key ingredient in achieving this local robustness is a novel debiasing procedure that adds a non-parametric bias correction term to the objective. The debiased objective retains convexity\, and the imputation of the correction term relies only on a non-restrictive large deviations behavior conducive for transferring knowledge from representative data-rich regions to the datascarce tail regions suffering from imbalance. The bias correction gets determined by the extent of model error in the estimation step and the specifics of the stochastic program in the optimization step\, thereby serving as a scalable “smart-correction” step bridging the disparate goals in estimation and optimization. Besides showing the empirical effectiveness of the proposed formulation in real datasets\, the numerical experiments bring out the utility of locally robust solutions in tackling model errors and shifts in distribution between training and deployment.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-karthyek-murthy/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230323T100000
DTEND;TZID=Asia/Singapore:20230323T113000
DTSTAMP:20260417T132549
CREATED:20230110T032726Z
LAST-MODIFIED:20230321T023308Z
UID:16655-1679565600-1679571000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series – Saed Alizamir
DESCRIPTION:Saed Alizamir is an Associate Professor of Operations Management at Yale School of Management. He joined Yale in 2013 after receiving a PhD in Decision Sciences from Duke University’s Fuqua School of Business. Professor Alizamir’s research interests lies in the area of social responsibility and public sector operations. In his research\, he focuses  on problems in public policy that involve private-public interactions and dynamic decision-making. The goal of his research is to provide normative recommendations that inform better policy decisions\, especially in areas where not enough data exists to run full-fledged empirical studies. He has worked on government subsidy instruments in renewable energy industry and electric vehicle markets\, agricultural supply chains\, demand management in residential electricity sector\, and optimal control of diagnostic systems such as nurse triage. In 2021\, Professor Alizamir was named as one of the World’s Best 40 Under 40 Business School Professors by Poets & Quants. He serves as an Associate Editor for the Operations Research journal\, and co-chaired the M&SOM cluster for the INFORMS conference in 2019. At Yale\, he teaches MBA level courses in core Operations\, Managing Sustainable Operations\, and Quantitative Decision Models. \n\n\n\nName of Speaker\nSaed Alizamir\n\n\nSchedule\nThursday 23 March 2023\, 10am – 11.30am\n\n\n Venue \nI4-01-03 Seminar Room\n\n\n Registration Link\nhttps://nus-sg.zoom.us/meeting/register/tZMkfuuurTwjGdfG2nYqcDCFI8vVGYD-qMR0\n\n\n Title\nSearch Delegation Policies for Compliance Enforcement\n\n\nAbstract\n  \nEnforcement of regulatory compliance over time often involves intermittent search in the form of inspection in order to reveal the compliance state of the regulated entity. To enable cost-effective enforcement of environmental compliance standards\, regulatory agencies encourage production firms to voluntarily discover and correct compliance violations. Although such self-regulation activities often bring desired benefits\, they create nontrivial challenges. To study this tradeoff\, we develop a model that captures the interactions between a regulator and a firm that unfold over time. Because constant monitoring is prohibitive\, the regulator and the firm perform costly inspections to discover the compliance state of production. If the regulator detects noncompliance\, the firm is required to pay penalty and restore compliance. To avoid penalty\, the firm performs self-inspections to preemptively detect noncompliance and restore compliance without reporting the action to the regulator. We show that inefficiency caused by the firm’s private action is amplified if the regulator adopts a policy of requiring permanent restoration. Under such a policy\, the firm’s self-inspections may leave the regulator and the environment worse off. By contrast\, self-inspections always bring a net benefit to the regulator if repeated temporary restorations are allowed. We also find that\, due to self-inspections\, a paradoxical situation arises where the regulator prefers mandating permanent restoration despite having a small chance of enforcing it.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-saed-alizamir/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230331T100000
DTEND;TZID=Asia/Singapore:20230331T113000
DTSTAMP:20260417T132549
CREATED:20230209T085028Z
LAST-MODIFIED:20230322T084550Z
UID:16706-1680256800-1680262200@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series – Philip Zhang
DESCRIPTION:Renyu (Philip) Zhang has been an Associate Professor (with tenure) at the Department of Decision Sciences and Managerial Economics\, The Chinese University of Hong Kong Business School since September 2022. He is also an economist and Tech Lead at Kwai\, one of the world’s largest online video-sharing and live-streaming platforms. Philip’s recent research focuses on developing data science methodologies (e.g.\, data-driven optimization\, causal inference\, and machine learning) to evaluate and optimize the operations strategies in the contexts of online platforms and marketplaces\, sharing economy\, and social networks\, especially their recommendation\, advertising\, pricing\, and matching policies. His research works have appeared in top business journals such as Management Science\, Operations Research\, and Manufacturing & Service Operations Management\, and have been recognized by various research awards of the INFORMS and POMS communities. His research projects have been funded by various funding agencies including HK RGC\, NSFC\, SMEC\, and STCSM.  Philip serves as a Senior Editor for Production and Operations Management\, and an Associate Editor for Naval Research Logistics. He has also developed data science and economics frameworks to evaluate and optimize the user growth strategy and the platform ecosystem of Kwai. Prior to joining CUHK\, Philip was an Assistant Professor of Operations Management at New York University Shanghai between 2016 and 2022. Please visit Philip’s personal website for more about him: https://rphilipzhang.github.io/rphilipzhang/ \n\n\n\nName of Speaker\nZhang Renyu\, Philip\n\n\nSchedule\nFriday 31 March 2023\, 10.00am – 11.30am\n\n\nVenue \nI4-01-03 (Innovation 4.0\, level 1 Seminar Room)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZEtduirqT4iG9L-B7cNDO-sjXX8YFw9Csoz\n\n\nTitle\nDeep Learning Based Causal Inference for Large-Scale Combinatorial Experiments: Theory and Empirical Evidence\n\n\nAbstract\nLarge-scale online platforms launch hundreds of randomized experiments (a.k.a. A/B tests) every day to iterate their operations and marketing strategies\, while the combinations of these treatments are typically not exhaustively tested. It triggers an important question of both academic and practical interests: Without observing the outcomes of all treatment combinations\, how to estimate the causal effect of any treatment combination and identify the optimal treatment combination? We develop a novel framework combining deep learning and doubly robust estimation to estimate the causal effect of any treatment combination for each user on the platform when observing only a small subset of treatment combinations. Our proposed framework (called debiased deep learning\, DeDL) exploits Neyman orthogonality and combines interpretable and flexible structural layers in deep learning. We prove theoretically that this framework yields efficient\, consistent\, and asymptotically normal estimators under mild assumptions\, thus allowing for identifying the best treatment combination when only observing a few combinations. To empirically validate our method\, we then collaborate with a large-scale video-sharing platform and implement our framework for three experiments involving three treatments where each combination of treatments is tested. When only observing a subset of treatment combinations\, our DeDL approach significantly outperforms other benchmarks to accurately estimate and infer the average treatment effect (ATE) of any treatment combination\, and to identify the optimal treatment combination.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-philip-zhang/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230428T100000
DTEND;TZID=Asia/Singapore:20230428T113000
DTSTAMP:20260417T132549
CREATED:20230110T032935Z
LAST-MODIFIED:20230405T020705Z
UID:16659-1682676000-1682681400@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series – Javad Nasiry
DESCRIPTION:Javad Nasiry is a professor of Operations Management at McGill’s Desautels Faculty of Management where he joined in 2019.  He is the director of Sustainable Growth Initiative (SGI) which is a cross-faculty initiative to mobilize the talent and expertise within McGill University to help businesses move towards more socially and environmentally sustainable business models. \nHis main research interests are in behavioural operations\, supply chain management\, sustainability\, retail operations\, and operations-marketing interface.  His research in sustainable operations focuses on the environmental consequences of new business models in apparel\, renewable energy\, and agriculture industries. \nPrior to joining McGill\, he was an associate professor of operations management in the School of Business and Management at the Hong Kong University of Science and Technology (HKUST) where he joined in 2010. \n\n\n\nName of Speaker\nJavad Nasiry\n\n\nSchedule\nFriday 28 April 2023\, 10.00am – 11.30am\n\n\nVenue \nI4-01-03 (Innovation 4.0\, level 1 Seminar Room)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZEsfuihqDgqHNL63FOwX-SW-XU0E_mfDTdE\n\n\nTitle\nSustainability in the fast fashion industry\n\n\nAbstract\nWe establish a much-needed link between the fast fashion business model and its environmental consequences. A fast fashion system allows firms to react quickly to changing consumer demand by replenishing inventory (via quick response) and introducing more fashion styles. We study the environmental impact of the fast fashion business model by analyzing its implications for product quality\, variety\, and inventory decisions. We find that a key driver of low product quality in the fast fashion industry is the firm’s incentive to offer variety to hedge against uncertain fashion trends. When variety is endogenous\, quality decreases as consumers become more sensitive to fashion or as the cost of introducing new styles decreases. \nWe assess the effectiveness of three environmental initiatives (waste disposal regulations\, consumer education\, and production tax schemes) in countering the environmental impact of fast fashion.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-zhengyuan-zhou/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230512T100000
DTEND;TZID=Asia/Singapore:20230512T113000
DTSTAMP:20260417T132549
CREATED:20230502T054823Z
LAST-MODIFIED:20230503T125438Z
UID:16850-1683885600-1683891000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Chun So Yeon
DESCRIPTION:So Yeon Chun is an Associate Professor of Technology & Operations Management at INSEAD. So Yeon’s data-driven research focuses on the interface between operations and marketing in consumer loyalty reward programs (consumer choices\, point currencies and monetization\, customer lifetime values\, and consumer behavior experiments)\, revenue management (pricing and forecasting)\, and risk management (risk measures\, portfolio optimization) with applications in industries such as retail\, transportation\, finance\, and hospitality. So Yeon holds a PhD in Operations Research and an MS in Applied Statistics from the School of Industrial and Systems Engineering at the Georgia Institute of Technology. \n\n\n\nName of Speaker\nChun So Yeon\n\n\nSchedule\nFriday 12 May 2023\, 10.00am – 11.30am\n\n\nVenue \nBIZ1-0202\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0vdO-sqDwvHtaug9Pj_1n9e035FOi3xDX-\n\n\nTitle\nPoint vs. Money: Monetization of Loyalty Currency and Consumer Payment Choice Behavior\n\n\nAbstract\nIn recent years\, companies have made various changes to the design and operational management of their loyalty programs to further monetize their virtual currency\, or points\, by making them more like money. However\, it remains unclear whether consumers treat points as they treat money when deciding whether to use them to pay for a purchase. In this talk\, we aim to address this question and explore how and why consumers spend loyalty points differently than money. In the first part of the talk\, we investigate the important factors that influence consumers’ decisions to pay with either points or money\, and we examine how a co-branded credit card partnership affects consumers’ attitudes toward point currency. We develop a model of consumers’ payment decisions and estimate it using proprietary transaction data from a major US airline company through a hierarchical Bayesian framework. In the second part of the talk\, we present a series of behavioral experiments that focus on the design and operation of the exchange rate between points and money.  We examine the behavioral bias that consumers exhibit toward point currency and explore whether the effects of the exchange rate design are unique to loyalty currencies or more generally apply to other foreign currencies.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-chun-so-yeon/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230804T100000
DTEND;TZID=Asia/Singapore:20230804T113000
DTSTAMP:20260417T132549
CREATED:20230802T041735Z
LAST-MODIFIED:20230802T041815Z
UID:17167-1691143200-1691148600@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Bar Light
DESCRIPTION:Bar Light is an assistant professor in the Department of Statistics and Operations Research in Tel Aviv University’s School of Mathematics. Bar was previously a Postdoctoral Researcher at Microsoft Research focusing on market design and designing ad-auctions. Bar obtained a PhD in Operations Research from Stanford university. His research mainly focuses on market design for platforms\, the analysis of large markets and systems\, and dynamic optimization. \n\n\n\nName of Speaker \nBar Light\n\n\nSchedule \n4 August 2023\, 10.00am – 11.30am\n\n\nVenue  \nBIZ1-0202\n\n\nLink to Register \nhttps://nus-sg.zoom.us/meeting/register/tZYkcOCgqTwrGNaBBd0TAVKzv8j4hP53YiPw\n\n\nTitle \nBudget Pacing in Repeated Auctions: Regret and Efficiency without Convergence\n\n\nAbstract \nWe study the aggregate welfare and individual regret guarantees of dynamic pacing algorithms in the context of repeated auctions with budgets. Such algorithms are commonly used as bidding agents in Internet advertising platforms. We show that when agents simultaneously apply a natural form of gradient-based pacing\, the liquid welfare obtained over the course of the learning dynamics is at least half the optimal expected liquid welfare obtainable by any allocation rule. Crucially\, this result holds without requiring convergence of the dynamics\, allowing us to circumvent known complexity-theoretic obstacles of finding equilibria. This result is also robust to the correlation structure between agent valuations and holds for any core auction\, a broad class of auctions that includes first-price\, second-price\, and generalized second-price auctions. For individual guarantees\, we further show such pacing algorithms enjoy dynamic regret bounds for individual value maximization\, with respect to the sequence of budget-pacing bids\, for any auction satisfying a monotone bang-for-buck property.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-bar-light/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230810T100000
DTEND;TZID=Asia/Singapore:20230810T113000
DTSTAMP:20260417T132549
CREATED:20230807T074110Z
LAST-MODIFIED:20230807T074316Z
UID:17206-1691661600-1691667000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Michelle Wu
DESCRIPTION:Michelle Xiao Wu is a Co-Director in the Data Science Lab at MIT Institute for Data\, Systems\, and Society (IDSS). Before joining IDSS\, she was an Assistant Professor at Carson College of Business\, Washington State University. She received her Ph.D. (major in operations management\, minor in economics) and MBA from the University of Chicago Booth School of Business and an M.Sc degree in Physics from Northwestern University. \nHer research interests focus on operations management in the digital economy\, including pricing for e-commerce platforms\, digital content release\, and the sharing economy. Her other research interests include machine learning\, the operations-finance interface\, and supply chain management. Her papers are published in leading journals such as Management Science\, M&SOM\, JMIS\, and IJPR. \nIn her consulting experience with various companies\, she provides implementable methods and strategies to optimize operational decisions\, in manufacturing\, e-commerce\, and brick & mortar retail. \n\n\n\nName of Speaker\nMichelle Wu\n\n\nDate\n10 August 2023\, 10am – 11.30am\n\n\nVenue \nBIZ1-0206\n\n\nRegistration Link \nhttps://nus-sg.zoom.us/meeting/register/tZAlcuqgpz4iEtYiqIsqyAVij6uSyYM-4GWH\n\n\nTitle \nEmpowering Businesses with Data\, Analytics\, and Automation\n\n\nAbstract\nWe present our work with a global online fashion retailer\, Zalando\, as an example of how a global retailer can utilize massive amount of data to optimize price discount decisions over a large number of products in multiple countries on a weekly basis.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-michelle-wu/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230825T100000
DTEND;TZID=Asia/Singapore:20230825T113000
DTSTAMP:20260417T132549
CREATED:20230817T020701Z
LAST-MODIFIED:20230817T020701Z
UID:17387-1692957600-1692963000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Yan Zhenzhen
DESCRIPTION:Dr. Zhenzhen Yan is an assistant professor at School of Physical and Mathematical Sciences\, Nanyang Technological University. She joined SPMS since 2018. Before that\, she received her PhD in Management Science from the National University of Singapore\, and her BSc and MSc in Management Science\, Operations Research from the National University of Defense and Technology in China. Her research interests mainly focus on the interplay between optimization and data analytics. Her first line of research is to solve various operations management problems and engineering problems from the distributionally robust perspective\, including supply chain design and operations\, and healthcare operations. The second line is to develop data-driven optimization approaches with applications to e-commerce operations and resource allocation. Her work has been published in leading operations management journals including Management Science\, Operations Research\, MSOM and POMS\, and top AI conferences including Neurips\, UAI and AAAI. Her work has received media coverage in various outlets including the Straits Times and ScienceDaily etc. She currently serves as an Associate Editor of Decision Sciences. \n\n\n\nName of Speaker\nYan Zhenzhen\n\n\nSchedule\n25 August 2023\, 10am\n\n\nVenue  \nBIZ1 03-07\n\n\nRegistration Link (Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZUvcOCqrD4uG9eJgleNTFTjjnTADqikckwd\n\n\nTitle \nSample-Based Online Generalized Assignment Problem with Unknown Poisson Arrivals\n\n\nAbstract\nWe study an edge-weighted online stochastic Generalized Assignment Problem with unknown Poisson arrivals. We provide a sample-based multi-phase algorithm by utilizing both pre-existing offline data (named historical data) and sequentially revealed online data. The developed algorithm employs the concept of exploration-exploitation to dynamically learn the arrival rate and optimize the allocation decision. We establish its parametric performance guarantee measured by a competitive ratio. We further provide a guideline on fine tuning the parameters under different sizes of historical data based on the established parametric form. By analyzing a special case which is a classical online weighted matching problem\, we also provide a novel insight on how the historical data’s quantity and quality (measured by the number of underrepresented agents in the data) affect the trade-off between exploration and exploitation in online algorithms and their performance. Finally\, we demonstrate the effectiveness of our algorithms numerically.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-yan-zhenzhen/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230908T100000
DTEND;TZID=Asia/Singapore:20230908T113000
DTSTAMP:20260417T132549
CREATED:20230831T074219Z
LAST-MODIFIED:20230831T074455Z
UID:17443-1694167200-1694172600@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Yaron Shaposhnik
DESCRIPTION:Yaron Shaposhnik is an Assistant Professor of Information Systems and Operations Management at the Simon School of Business in the University of Rochester. Most broadly\, he is interested in the optimization and analysis of mathematical models that capture real world problems\, and in developing decision support tools that leverage analytics to improve operations. \n\n\n\nName of Speaker \nYaron Shaposhnik\n\n\nSchedule  \n8 September 2023\, 10am – 11.30am\n\n\nRegistration Link \nhttps://nus-sg.zoom.us/meeting/register/tZcsdu6vrzwsGdC8u6_NyrnGumir0pnyZY21\n\n\nTitle of Talk \nGlobally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation\n\n\nAbstract  \nWe develop a method for understanding specific predictions made by (global) predictive models by constructing (local) models tailored to each specific observation (these are also called “explanations” in the literature). Unlike existing work that “explains” specific observations by approximating global models in the vicinity of these observations\, we fit models that are globally-consistent with predictions made by the global model on past data. We focus on rule-based models (also known as association rules or conjunctions of predicates)\, which are interpretable and widely used in practice. We design multiple algorithms to extract such rules from discrete and continuous datasets\, and study their theoretical properties. Finally\, we apply these algorithms to multiple credit-risk models trained on the Explainable Machine Learning Challenge data from FICO and demonstrate that our approach effectively produces sparse summary-explanations of these models in seconds. Our approach is model-agnostic (that is\, can be used to explain any predictive model)\, and solves a minimum set cover problem to construct its summaries.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-yaron-shaposhnik/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230915T100000
DTEND;TZID=Asia/Singapore:20230915T113000
DTSTAMP:20260417T132549
CREATED:20230907T034153Z
LAST-MODIFIED:20230907T034223Z
UID:17474-1694772000-1694777400@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Teo Chung Piaw & Wang Quanmeng
DESCRIPTION:Name of speakers \nTeo Chung Piaw & Wang Quanmeng\n\n\nSchedule \n15 September 2023\, 10am – 11.30am\n\n\nVenue\nBIZ1 – 0205\n\n\nRegistration \nhttps://nus-sg.zoom.us/meeting/register/tZIofumopzgoGtUtQiqUDX1hBKnar2DU7wzJ\n\n\nTitle of talk\nLast mile innovations: The case of the Locker Alliance Network\n\n\nAbstract \nIn this talk\, we’ll explore a collection of academic research we’ve conducted\, funded by IMDA\, focusing on Singapore’s “Locker Alliance Network” (LAN). This government-led initiative aims to establish a network of public lockers in residential areas and community hubs to improve the efficiency of last-mile parcel deliveries. Our research tackles key operational questions\, such as the ideal density\, coverage\, and impact of the LAN. \nTo address these questions\, we’ve employed locker usage data from a commercial courier service to calibrate a model that gauges how walking distance and other variables influence customer preferences for locker pickups versus traditional home or office deliveries. Additionally\, we’ve created a facility location model that leverages existing parcel delivery data to optimize the LAN’s design. Contrary to traditional thinking\, our results indicate that peak parcel volume areas are not necessarily the best locations for lockers. Instead\, our model recommends an optimal coverage radius of 250 meters for the LAN in Singapore. One unique challenge we faced was the absence of home-office pair information for residents\, leading us to develop a new type of facility location model where the choice set is unknown. Our findings suggest that under realistic assumptions—namely\, that home delivery will always be more popular than locker pickup—the lack of this specific information has minimal impact on the performance of our locker facility location model. \nIn related research\, we’ve also examined the LAN’s effects on routing efficiency and conducted empirical tests to understand how exposure and popularity influence adoption choices. We also discuss how the challenges in this public facility (that it is interoperable and used by many different LSPs) are partially addressed due to a “nested” pattern in the optimal solution to the facility location model.\n\n\nAbout the speakers \nChung Piaw Teo is Provost’s Chair Professor in NUS Business School and Executive Director of the Institute of Operations Research and Analytics (IORA) in the National University of Singapore\, and concurrently a co-director in the SIA-NUS Digital Aviation Corp Lab. With a focus on optimization and supply chain management\, Professor Teo is trying to bridge the gap between theoretical research and practical applications of OR and Analytics in business and engineering. \nHe was a fellow in the Singapore-MIT Alliance Program\, an Eschbach Scholar in Northwestern University (US)\, Professor in Sungkyunkwan Graduate School of Business (Korea)\, and a Distinguished Visiting Professor in YuanZe University (Taiwan). He is department editor for MS (Optimization)\, and a former area editor for OR (Operations and Supply Chains). He was elected Fellow of INFORMS and Chang Jiang Scholar (China) in 2019. He has also served on several international committees such as the Chair of the Nicholson Paper Competition (INFORMS\, US)\, member of the LANCHESTER and IMPACT Prize Committee (INFORMS\, US)\, Fudan Prize Committee on Outstanding Contribution to Management (China)\, and recently chaired the EIC search committee for Operations Research\, an INFORMS journal. \nQuanmeng Wang is a research fellow at Institute of Operations Research and Analytics\, where he also earned his PhD. His research mainly focus on model development for operation problems in logistics. He participated in several research projects collaborated with industry partner of IORA\, including a leading express company of China and a government public service sector of Singapore.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-teo-chung-piaw-wang-quanmeng/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230922T100000
DTEND;TZID=Asia/Singapore:20230922T113000
DTSTAMP:20260417T132549
CREATED:20230918T081639Z
LAST-MODIFIED:20230918T081639Z
UID:17770-1695376800-1695382200@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Alex Yang
DESCRIPTION:Name of speaker\nS. Alex Yang\n\n\nSchedule  \n22 September 2023\, 10am – 11.30am\n\n\nVenue \nI4-01-03 Seminar Room\n\n\nRegistration  \nhttps://nus-sg.zoom.us/meeting/register/tZ0sdOCuqzkoGNS7Zb8Byg3Kg8GrkOoxwvH8\n\n\nTitle of talk \nCrowd-judging on Two-sided Platforms: An Analysis of In-group Bias\n\n\nAbstract  \nDisputes over transactions on two-sided platforms are common and usually arbitrated through platforms’ customer service departments or third-party service providers. This paper studies crowd-judging\, a novel crowd-sourcing mechanism whereby users (buyers and sellers) volunteer as jurors to decide disputes arising from the platform. Using a rich dataset from the dispute resolution center at Taobao\, a leading Chinese e-commerce platform\, we aim to understand this innovation and propose and analyze potential operational improvements\, with a focus on in-group bias (buyer jurors favor the buyer\, likewise for sellers). Platform users\, especially sellers\, share the perception that in-group bias is prevalent and systematically sways case outcomes as the majority of users on such platforms are buyers\, undermining the legitimacy of crowd-judging. Our empirical findings suggest that such concern is not completely unfounded: on average\, a seller juror is approximately 10% likelier (than a buyer juror) to vote for a seller. Such bias is aggravated among cases that are decided by a thin margin\, and when jurors perceive that their in-group’s interests are threatened. However\, the bias diminishes as jurors gain experience: a user’s bias reduces by nearly 95% as their experience grows from zero to the sample-median level. Incorporating these findings and juror participation dynamics in a simulation study\, the paper delivers three managerial insights. First\, under the existing voting policy\, in-group bias influences the outcomes of no more than 2% of cases. Second\, simply increasing crowd size\, either through a larger case panel or aggressively recruiting new jurors\, may not be efficient in reducing the adverse effect of in-group bias. Finally\, policies that allocate cases dynamically could simultaneously mitigate the impact of in-group bias and nurture a more sustainable juror pool. \nLink to paper: https://pubsonline.informs.org/doi/10.1287/mnsc.2023.4818\n\n\nAbout the speaker\nS. Alex Yang is an Associate Professor of Management Science and Operations at London Business School. Alex holds a PhD and an MBA from the University of Chicago Booth School of Business\, an MS from Northwestern University\, and a BS from Tsinghua University. Alex’s primary research focus is on the interface of operations management and finance\, especially in trade credit\, supply chain finance\, and FinTech. His recent research focuses on platform governance and operations and value chain management and innovation. Alex’s research has appeared in academic journals in operations and finance\, such as Management Science\, M&SOM\, and Journal of Financial Economics\, and has received several best paper awards. He is the associate editor of several academic journals. An award-winning teacher\, Alex has taught on the MBA\, EMBA\, and executive education programmes in universities and business schools around the world. Beyond research and teaching\, Alex has working and consulting experience in banks\, Fintech and technology companies\, hedge funds\, airlines\, and international organizations. \nhttps://www.london.edu/faculty-and-research/faculty-profiles/y/yang-s \nhttps://salexyang.com
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-alex-yang/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20231006T100000
DTEND;TZID=Asia/Singapore:20231006T113000
DTSTAMP:20260417T132549
CREATED:20231003T143351Z
LAST-MODIFIED:20231003T143351Z
UID:18075-1696586400-1696591800@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Mabel Chou\, Sun Qinghe\, Li Wei
DESCRIPTION:Name of speakers \nMabel C. Chou\, Sun Qinghe\, Li Wei  \n\n\nSchedule  \n6 October 2023\, 10am – 11.30am  \n\n\nVenue  \nHon Sui Sen Memorial Library\, Seminar Room 4-7 \n\n\nZoom link  \nhttps://nus-sg.zoom.us/meeting/register/tZAlcu2pqzkvHNA_ldzyBArQnujX1H_xk8Tr  \n\n\nTitle of talk \nData driven bunker procurement planning: working with the maritime industry   \n\n\nAbstract \nIn this presentation\, we will recount our journey collaborating with the maritime industry\, discussing the challenges we encountered and elucidating how we transformed these challenges into gratifying experiences and impactful contributions. We will use our work on bunker procurement decisions with a global container shipping company as an example to illustrate the impact we made and the lessons we learned.    \nBunker refueling decisions in international shipping are crucial operational choices. Each ship acts like a movable storage unit navigating through diverse markets\, procuring bunker fuels from different ports to sustain its voyage. This involves grappling with challenges posed by varying bunker fuel prices over time and locations. To tackle this challenge\, we propose data-driven structure-prescriptive (SP) approaches that combine the strengths of modern machine learning with the insights from traditional OR modeling and optimization. Instead of predicting future marine fuel prices\, our approach directly learns the optimal refueling policy from data and adapts refueling decisions to the current market conditions\, including fuel prices\, crude oil price\, NYSE index\, etc.   \nOur focus lies in leveraging the well-established understanding that the optimal refueling decision adheres to a state-dependent base-stock refueling policy. This decision depends on factors such as the port of call\, fuel tank capacity\, market conditions\, and is finite-valued\, depending on the vessel’s schedule and voyage. We provide a practical framework to incorporate these structural properties into data-driven decision-making for bunker refueling operations. The proposed SP approaches successfully recovered the “true” optimal refueling policy in synthetic simulations. Moreover\, our experiments unveiled that incorporating more structural properties into the learning process significantly improved the out-of-sample (OOS) performance. In the case study\, we compared our proposed SP approach with the firm’s existing operation\, resulting in a noteworthy reduction of fuel expenses\, which amounts to approximately 2.52 million USD per year in savings for a fleet of six ships.   \nIn addition\, to facilitate our collaboration with industry\, we propose an eXplainable multi-stage bunker procurement planning (X-BPP) framework for the maritime industry. In this presentation\, we will showcase this framework\, discuss its performance\, and share the lessons we learn in implementing the system.    \n\n\nAbout the speakers \nMabel C. Chou is an associate professor in the Analytics and Operations department at National University of Singapore (NUS). She received the B.Sc. degree in mathematics from National Taiwan University\, the M.Sc. degree in mathematics and Ph.D. degree in industrial engineering and management sciences from Northwestern University. Her research focuses on production scheduling and supply chain analysis. Her current research interest is in the application of optimization tools and business analytics for engineering\, service\, and supply chain management problems. She is an associate editor for Operations Research\, a senior editor for Production and Operations Management and an associate editor for Pacific Journal of Optimization. She has also consulted for companies such as GSK\, Caterpillar\, P&G\, SIA Engineering Company\, National University Hospital\, Tan Tock Seng Hospital\, Lenovo\, Supreme Components International\, etc.    \nSun Qinghe is an Assistant Professor at the Department of Logistics and Maritime Studies (LMS)\, PolyU Business School.  Her research combines data with optimization to provide insights into risk management within supply chain systems\, particularly within the maritime logistics sector. Qinghe received her Ph.D. in Operations Research from the National University of Singapore (NUS) in 2022\, jointly advised by Mabel Chou and Qiang Meng\, and her B.Sc. in Maritime Studies from Nanyang Technological University (NTU)\, Singapore.   \nLi Wei is a Research Fellow at the National University of Singapore’s Institute of Operations Research and Analytics\, jointly advised by Mabel C. Chou and Chen Ying. He has a broad interest in model development for Financial Forecasting-related problems and his research is often motivated by industry initiatives. He obtained his Ph.D. in Computational Finance from the Norwegian University of Science and Technology before joining NUS.  
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-mabel-chou-sun-qinghe-li-wei/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20231120T100000
DTEND;TZID=Asia/Singapore:20231120T113000
DTSTAMP:20260417T132549
CREATED:20231114T041749Z
LAST-MODIFIED:20231114T041900Z
UID:18505-1700474400-1700479800@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Zhou Zhengyuan
DESCRIPTION:  \n\n\n\nName of Speaker\nZhengyuan Zhou\n\n\nSchedule\n20 November 2023\, 10am – 11.30am\n\n\nVenue  \nBIZ1 – 0302\n\n\nLink to Register \nhttps://nus-sg.zoom.us/meeting/register/tZIudu6qrTorE90DBkeYzCo1WC_rQEUdCldn\n\n\nTitle \nOptimal No-Regret Learning in Repeated First-Price Auctions\n\n\nAbstract\nFirst-price auctions have very recently swept the online advertising industry\, replacing second-price auctions as the predominant auction mechanism on many platforms for display ads bidding. This shift has brought forth important challenges for a bidder: how should one bid in a first-price auction\, where unlike in second-price auctions\, it is no longer optimal to bid one’s private value truthfully and hard to know the others’ bidding behaviors? In this paper\, we take an online learning angle and address the fundamental problem of learning to bid in repeated first-price auctions. We discuss our recent work in leveraging the special structures of the first-price auctions to design minimax optimal no-regret bidding algorithms.\n\n\nAbout the Speaker\nZhengyuan Zhou is currently an assistant professor in New York University Stern School of Business\, Department of Technology\, Operations and Statistics. Before joining NYU Stern\, Professor Zhou spent the year 2019-2020 as a Goldstine research fellow at IBM research. He received his BA in Mathematics and BS in Electrical Engineering and Computer Sciences\, both from UC Berkeley\, and subsequently a PhD in Electrical Engineering from Stanford University in 2019. His research interests lie at the intersection of machine learning\, stochastic optimization and game theory and focus on leveraging tools from those fields to develop methodological frameworks to solve data-driven decision-making problems.\n\n\n\n  \n 
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-zhou-zhengyuan/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20231128T100000
DTEND;TZID=Asia/Singapore:20231128T113000
DTSTAMP:20260417T132549
CREATED:20231119T141330Z
LAST-MODIFIED:20231119T141425Z
UID:18551-1701165600-1701171000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series- Karen Zheng
DESCRIPTION:Name of Speaker\nYanchong (Karen) Zheng\n\n\nSchedule\n28 November 2023\, 10am – 11.30am\n\n\nVenue \nI4-01-03 (Innovation 4.0\, level 1\, Seminar Room)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0vf-GpqzwvHN1xiFo9IOFYhdZZS-yp1RcZ\n\n\nTitle\nImproving Farmers’ Welfare via Digital Agricultural Platforms\n\n\nAbstract\nIn order to improve the welfare of smallholder farmers\, multiple countries (e.g.\, Ethiopia and India) have launched digital agricultural platforms to transform traditional markets. However\, there is still mixed evidence regarding the impact of these platforms and more generally how they can be leveraged to enable more efficient agricultural supply chains and markets. In this talk\, we describe a body of work that provides the first rigorous impact analysis of such a platform and demonstrates how innovative price discovery mechanisms could be enabled by digital agri-platforms in resource-constrained environments. The work is focused on the Unified Market Platform (UMP) that connects all the agricultural wholesale markets in the state of Karnataka\, India. Our impact assessment shows that the launch of the UMP has significantly increased the modal prices of certain commodities (2.6%-6.5%)\, while prices for other commodities have not changed. The analysis highlights operational and market factors that contribute to the variable impact of UMP on prices. Motivated by these insights\, we collaborate closely with the Karnataka government to design\, implement\, and assess the impact of a new two-stage auction on the UMP.  To ensure implementability and protect farmers’ revenue\, the design process is guided by practical operational considerations as well as semi-structured interviews with a majority of the traders in the field. A new behavioral auction model informed by the field insights is developed to determine when the proposed two-stage auction can generate a higher revenue for farmers than the traditional single-stage\, first-price\, sealed-bid auction. The new auction mechanism was implemented on the UMP for a major market of lentils in February 2019. By March 2020\, commodities worth more than $19 million (USD) had been traded under the new auction. A difference-in-differences analysis demonstrates that the implementation has yielded a significant 3.6% price increase  (corresponding to a 55%-94% profit gain)\, benefiting over 20\,000 farmers who traded in the treatment market. \nThis talk is based on joint work with Retsef Levi (MIT)\, Somya Singhvi (USC)\, Manoj Rajan (ReMS) and his team in Karnataka\, India. \nPapers: The talk will cover the following two papers with a focus on the second one: \nThe impact of unifying agricultural wholesale markets on prices and farmers’ profitability\, with Levi\, Rajan\, Singhvi. PNAS\, February 4\, 2020\, 117(5) 2366-2371. https://doi.org/10.1073/pnas.1906854117 \nImproving Farmers’ Income on Online Agri-platforms: Evidence from the Field\, with Levi\, Rajan\, Singhvi. https://ssrn.com/abstract=3486623\n\n\nAbout the Speaker\nYanchong (Karen) Zheng is the George M. Bunker Professor and an Associate Professor of Operations Management at the MIT Sloan School of Management. Her recent research focuses on two general topics: (I) the design of incentives\, technologies\, and behavioral interventions to enhance efficiency\, welfare\, and sustainability in food and agriculture systems\, with a focus on smallholder supply chains; and (II) the role of information transparency in driving environmentally and socially responsible behaviors. In her research\, Zheng employs a behavior-centric\, data-driven\, field-based approach\, and she collaborates with both public and private partners on the ground to create positive impacts to society.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-karen-zheng/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240102T100000
DTEND;TZID=Asia/Singapore:20240102T113000
DTSTAMP:20260417T132549
CREATED:20231224T131337Z
LAST-MODIFIED:20231224T131337Z
UID:19195-1704189600-1704195000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Chen Xi
DESCRIPTION:  \n\n\n\n\nName of Speaker\nChen Xi\n\n\nSchedule\n2 January 2024\, 10am – 11.30am\n\n\nVenue  \nI4-01-03 (Innovation 4.0\, level 1\, Seminar Room)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0tcuygqTstGtXSwYItVg9uaq46sPHW8Akl\n\n\nTitle \nDigital Privacy in Personalized Pricing and New Directions in DeFI\n\n\nAbstract\nAbstract: This talk has two parts. The first part is on digital privacy in personalized pricing. When involving personalized information\, how to protect the privacy of such information becomes a critical issue in practice. In this talk\, we consider a dynamic pricing problem with an unknown demand function of posted prices and personalized information. By leveraging the fundamental framework of differential privacy\, we develop a privacy-preserving dynamic pricing policy\, which tries to maximize the retailer revenue while avoiding information leakage of individual customers’ information and purchasing decisions. This is joint work with Prof. Yining Wang and Prof. David Simchi-Levi. \nThe second part is on my very recent research in decentralized finance. I will first discuss my work on delta hedging liquidity positions on the automated market maker (Uniswap V3) and then highlights some open problems in decentralized finance.\n\n\nAbout the Speaker\nXi Chen is the Andre Meyer Full professor at Stern School of Business\, New York University\, who is also an affiliated professor at Computer Science and Center for Data Science. Before that\, he was a Postdoc in the group of Prof. Michael Jordan at UC Berkeley and obtained his Ph.D. from the Machine Learning Department at Carnegie Mellon University. \nHe studies high-dimensional machine learning\, online learning\, large-scale stochastic optimization\,  and applications to operations management and FinTech. Recently\, he started a new research line on blockchain technology and decentralized finance. He is a recipient of COPSS Leadership Academy\, Elected Member of International Statistical Insititute (ISI)\, The World’s Best 40 under 40 MBA Professor by Poets & Quants\, and Forbes 30 under 30 in Science.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-chen-xi/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240112T100000
DTEND;TZID=Asia/Singapore:20240112T113000
DTSTAMP:20260417T132549
CREATED:20231228T080840Z
LAST-MODIFIED:20231228T080840Z
UID:19205-1705053600-1705059000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Zhao Jinglong
DESCRIPTION:Name of Speaker\nZhao Jinglong\n\n\nSchedule\n12 January 2024\, 10am – 11.30am\n\n\nVenue \nBIZ1-0203\n\n\nLink to Register \n \nhttps://nus-sg.zoom.us/meeting/register/tZEpcOGopz4jE91kG6vFGQ78zjTCRdz9iGFZ\n\n\nTitle\nAdaptive Neyman Allocation\n\n\nAbstract\nIn experimental design\, Neyman allocation refers to the practice of allocating subjects into treated and control groups\, potentially in unequal numbers proportional to their respective standard deviations\, with the objective of minimizing the variance of the treatment effect estimator. This widely recognized approach increases statistical power in scenarios where the treated and control groups have different standard deviations\, as is often the case in social experiments\, clinical trials\, marketing research\, and online A/B testing. However\, Neyman allocation cannot be implemented unless the standard deviations are known in advance. Fortunately\, the multi-stage nature of the aforementioned applications allows the use of earlier stage observations to estimate the standard deviations\, which further guide allocation decisions in later stages. In this paper\, we introduce a competitive analysis framework to study this multi-stage experimental design problem. We propose a simple adaptive Neyman allocation algorithm\, which almost matches the information-theoretic limit of conducting experiments. Using online A/B testing data from a social media site\, we demonstrate the effectiveness of our adaptive Neyman allocation algorithm\, highlighting its practicality especially when applied with only a limited number of stages.\n\n\nAbout the Speaker\nJinglong Zhao is an Assistant Professor of Operations and Supply Chain Management at Questrom School of Business at Boston University. He works at the interface between optimization and econometrics. His research leverages discrete optimization techniques to design field experiments with applications in online platforms. Jinglong completed his PhD in Social and Engineering Systems and Statistics at Massachusetts Institute of Technology.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-zhao-jinglong/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240119T100000
DTEND;TZID=Asia/Singapore:20240119T113000
DTSTAMP:20260417T132549
CREATED:20240115T021633Z
LAST-MODIFIED:20240115T021633Z
UID:19211-1705658400-1705663800@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Qin Hanzhang
DESCRIPTION:  \n\n\n\n\nName of Speaker\nQin Hanzhang\n\n\nSchedule\n19 January 2024\, 10am – 11.30am\n\n\nVenue  \nI4-01-03 Seminar Room\n\n\nLink to Register \n https://nus-sg.zoom.us/meeting/register/tZEpce-orz8rEtZcddjRXnFf3EACPJSUMiAt\n\n\nTitle \nLower Sample Complexity of Reinforcement Learning for Structured MDPs: Evidence from Inventory Control\n\n\nAbstract\nI will discuss the important open problems of 1) What is the sample complexity (i.e.\, how may number of data samples is needed) of learning nearly optimal policy for multi-stage stochastic inventory control when the underlying demand distribution is initially unknown; and 2) How to compute such a policy when the required number of data samples are given. For the first half of the talk\, without considering fixed ordering cost\, I will start answering the questions from the backlog setting via SAIL\, a novel SAmple based Inventory Learning algorithm. Then\, results for the more practical lost-sales setting will be discussed\, including the first sample complexity result for this more challenging setting with only mild assumptions (that ensures quality data)\, by leveraging both recent developments of variance reduction techniques for reinforcement learning and the structural properties of the dynamic programming formulation for inventory control settings. Numerical simulations show that SAIL significantly outperforms competing methods in terms of inventory cost minimization. Then in the second half\, I will discuss several recent developments on sample complexity related to all three types of the MDP formulations (finite-horizon MDPs\, infinite-horizon discounted/average-cost MDPs) for inventory control with fixed ordering cost. Somewhat surprisingly\, in all three cases\, it is found that sample complexity of the most naïve plug-in estimators is strictly lower than the “best possible” bounds derived for general MDPs. The first half will be based on joint work with David Simchi-Levi (MIT) and Ruihao Zhu (Cornell)\, and the second half will be based on joint work with Boxiao Chen (UIC)\, Xiaoyu Fan (NYU)\, Michael Pinedo (NYU) and Zhengyuan Zhou (NYU).\n\n\nAbout the Speaker \nHanzhang Qin is an Assistant Professor at the Department of Industrial Systems Engineering and Management at NUS. He is also an affiliated faculty member at the NUS Institute for Operations Research and Analytics. His research was recognized by several awards\, including INFORMS TSL Intelligent Transportation Systems Best Paper Award and MIT MathWorks Prize for Outstanding CSE Doctoral Research. Before joining NUS\, Hanzhang spent one year as a postdoctoral scientist in the Supply Chain Optimization Technologies Group of Amazon NYC. He earned his PhD in Computational Science and Engineering under supervision of Professor David Simchi-Levi\, and his research interests span stochastic control\, applied probability and statistical learning\, with applications in supply chain analytics and transportation systems. He holds two master’s\, one in EECS and one in Transportation both from MIT. Prior to attending MIT\, Hanzhang received two bachelor degrees in Industrial Engineering and Mathematics from Tsinghua University.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-qin-hanzhang/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240126T100000
DTEND;TZID=Asia/Singapore:20240126T113000
DTSTAMP:20260417T132549
CREATED:20240119T120215Z
LAST-MODIFIED:20240119T120215Z
UID:19350-1706263200-1706268600@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Zhan Ruohan
DESCRIPTION:  \n\n\n\nName of Speaker\nZhan Ruohan\n\n\nSchedule\n26 January 2024\, 10am – 11.30am\n\n\nVenue  \nI4-01-03\n\n\nLink to Register \nhttps://nus-sg.zoom.us/meeting/register/tZApdeirrD8uGdK8Z3tiW6Can4XywK0kVD4C\n\n\nTitle \nEstimation and Inference under Recommender Interference\n\n\nAbstract\nIn digital platforms\, recommender systems (RecSys) are essential for aligning content with viewer preferences. This work considers the evaluation of RecSys updates\, referred to as “treatments”\, by analyzing their “global treatment effect” (GTE) – the expected overall benefit of universally applying these treatments. Our focus is on treatments targeting content creators. We utilize A/B experiments on the creator side and identify that the conventional difference-in-mean estimator is biased for GTE estimation\, due to interference among creators competing for visibility. To address this challenge\, we introduce a semi-parametric model that combines a parametric choice model\, designed to streamline the recommendation process\, with a nonparametric component that employs machine learning to account for the heterogeneity among viewers and content. Using this model\, we approximate GTE with a doubly robust estimator that satisfies Neyman orthogonality\, ensuring consistency and asymptotic normality\, and supporting hypothesis testing. We show the robustness and semiparametric efficiency of our estimator even under model mis-specification. We demonstrate the efficacy of our method through simulations and practical applications on a leading short video platform. This is joint work with Shichao Han\, Yuchen Hu\, and Zhenling Jiang.\n\n\nAbout the Speaker \nRuohan Zhan is an assistant professor in the Department of Industrial Engineering and Decision Analytics at the Hong Kong University of Science and Technology. She earned her PhD from Stanford University and her BS from Peking University. Specializing in causal inference\, statistics\, and machine learning\, Ruohan develops new methods to solve problems from online marketplaces\, particularly on challenges related to causal effect identification\, economic analysis\, experimentation and operations. Her research has been published in top-tier journals including Management Science and Proceedings of National Academy of Sciences\, as well as leading machine learning conferences including NeurIPS\, ICLR\, WWW\, and KDD.\n\n\n\n  \n  \n 
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-zhan-ruohan/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240202T100000
DTEND;TZID=Asia/Singapore:20240202T113000
DTSTAMP:20260417T132549
CREATED:20240126T150453Z
LAST-MODIFIED:20240126T150604Z
UID:19497-1706868000-1706873400@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Canberk Ucel
DESCRIPTION:  \n\n\n\n\nName of Speaker\nCanberk Ucel\n\n\nSchedule\n2 February 2024\, 10am – 11.30am\n\n\nVenue\nI4-01-03 Seminar Room\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZcvcuivqDMjGdS7y3CPbCx_Y701qvieVxVJ\n\n\nTitle\nThe Value of Advice: Evidence from Thousands of Smallholder Farms in the Philippines\n\n\nAbstract\nIncreasing the productivity of Philippine coconut farms that are well below world standards could improve the livelihoods of 3.4 million farming families\, most suffering poverty. Government and supporting organizations have long promoted Good Agricultural Practices (GAPs)\, which decades of public research suggests would double farm productivity with little capital investment\, but have failed to achieve widespread adoption and productivity gains. We study the role of access to change agents in facilitating GAP adoption and effective implementation using proprietary data on the productivity\, granular farming practices and characteristics of 1\,998 smallholders. Our quantitative analysis leverages the pseudo-exogenous variation in agricultural extension office locations to find that being within 8 kilometers of an extension office is associated with greater awareness of central recommendations for 7 of 8 GAPs\, increased adoption rates for three most effective GAPs\, and 36% higher productivity\, on average\, on otherwise comparable farms. Our post-hoc analysis further suggests that physical interactions enable change agents to support complex practice adoption and local implementation decisions. Moreover\, we find significant heterogeneity in the effects of agent access\, and offer facility reallocation and farm visit schedules to improve service coverage and effectiveness using existing agent capacity. Our results suggest that supporting organizations should integrate change agent support or otherwise focus on developing better customized farming advice\, integrate farmer feedback\, and assist smallholders with the finer details of implementation leveraging emerging information technologies. Evidence-based provision of advisory services\, extended beyond the Philippine context\, could potentially benefit two billion people worldwide dependent on smallholder farms\, and redound benefits to small heterogenous firms that dominate vital functions in other industries. We suggest new avenues for research on data-driven\, evidence-based improvements in the provision and design of advisory services\, e.g. related to optimal facility allocation and agent visit schedules and the development and communication of effective operational recommendations.\n\n\nAbout the Speaker\nCanberk Ucel is an Assistant Professor at Bilkent University in Turkey and a Visiting Scholar at INSEAD.  He completed his PhD in Operations\, Information and Decisions at the Wharton School at the University of Pennsylvania in 2022\, and also holds an undergraduate degree in Industrial Engineering from Bilkent University. He is strongly interested in studying operational and organizational issues in understudied industries facing complex social\, economic and environmental challenges\, and his current research focuses on the agriculture industry\, which contributes significantly to environmental conservation and economic development\, and employs most of the world’s poorest workers. His research\, which has been recognized by several academic awards\, leverages proprietary\, granular farm operations data he collected through several industry partnerships he built during his doctoral studies\, as well as extensive field work\, to generate practical recommendations for farmers\, companies and policymakers to advance key economic\, social and environmental goals. He strives to translate his research into positive change in the industry\, including through large-scale randomized controlled field trials\, and advocates agriculture as a fruitful context for managerial and operational research with potential to generate significant societal impact. His teaching experience spans various MBA\, graduate and undergraduate courses at Wharton  and Bilkent related to operations\, supply chains\, and data analytics and statistics\, and includes writing teaching cases\, migrating courses online\, and designing and teaching new class sessions and courses.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-canberk-ucel/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240216T100000
DTEND;TZID=Asia/Singapore:20240216T113000
DTSTAMP:20260417T132549
CREATED:20240206T143510Z
LAST-MODIFIED:20240206T143549Z
UID:19647-1708077600-1708083000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Kai Hoberg
DESCRIPTION:  \n\n\n\n\nName of Speaker\nKai Hoberg\n\n\nSchedule\n16 February 2024\, 10am – 11.30am\n\n\nVenue\nI4-01-03 Seminar Room\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZYuceusrjouE9P7Pu7BAYbWh7quOmNJ-xcw\n\n\nTitle\nUsing (inaccurate) data to drive better supply chain decision making\n\n\nAbstract\nMore and more data is available to improve supply chain decision making but it needs to be carefully applied considering human limitations. Against this background\, I will present two studies that focus on the role of human judgment in supply chain management decision making\, first exploring the influence of planners’ adjustments to AI-generated demand forecasts and second examining the effectiveness of human decision-making in inventory management subject to inaccurate data. Study 1 investigates the role of human judgment in demand forecasting. We analyze planners’ adjustments to AI-generated forecasts using a dataset containing 30 million SKU-store-day level forecasts and associated variables. We employ random forest and decision tree approaches to understand the drivers and quality of adjustments. Our findings suggest product characteristics such as price\, freshness\, and discounts are important factors in adjustments. Large positive adjustments are frequent but often inaccurate\, while large negative adjustments are accurate but less common which indicates behavioral biases. In Study 2\, we focus on decisions made under the inaccurate inventory data due to shrinkage and loss. We explore the trade-off between cleaning inventory data centrally and allowing decision makers to adjust ordering decisions based on their judgment. In light of human biases in decision making\, we present a set of hypotheses on the cleaning-adjustment trade-off and test them in a laboratory setting. The study raises questions about the effectiveness of normative models in determining whether to clean data centrally or rely on decision makers’ judgments\, providing insights into optimizing human knowledge utilization in supply chain management.\n\n\nAbout the Speaker\nKai Hoberg is Professor of Supply Chain and Operations Strategy at the Kühne Logistics University in Hamburg. His research focuses on supply chain analytics\, the role of technology in supply chains\, and supply chain strategy. His research findings have been published in academic journals like Journal of Operations Management\, Production and Operations Management or Journal of Supply Chain Management. Kai was a visiting researcher at international universities such as the National University of Singapore\, Cornell University\, the Israel Institute of Technology and the University of Oxford.  Prior to his return to academia\, he was a project manager in the operations team at Booz & Company. For the past 10 years he has supported the McKinsey Supply Chain practice in teaching and research.  His team at KLU is closely working industry partners such as Bayer\, Procter & Gamble\, Jungheinrich or Infineon.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-kai-hoberg/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240226T100000
DTEND;TZID=Asia/Singapore:20240226T113000
DTSTAMP:20260417T132549
CREATED:20240221T025022Z
LAST-MODIFIED:20240221T025048Z
UID:20140-1708941600-1708947000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Mohamed Mostagir
DESCRIPTION:Name of Speaker\nMohamed Mostagir\n\n\nSchedule\n26 February 2024\, 10am – 11.30am\n\n\nVenue \nBIZ1- 0203\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZUrc–gqj4uE9WGbEbpRoBVA4DGCZes88wN\n\n\nTitle\nA Theory of Ghosting\n\n\nAbstract\nGhosting is a phenomenon where communication between two parties abruptly stops after one side becomes deliberately unresponsive. This occurs in a variety of settings\, but the term entered the mainstream after its usage to describe an important aspect of the dating experience that has been sparsely studied. Both online dating platforms and their users report that ghosting is one of the primary drivers hurting user experience and preventing good outcomes. We develop a model of ghosting and study the efficacy of different policies that platforms have implemented to deal with this problem.\n\n\nAbout the Speaker\nMohamed Mostagir is an associate professor of Technology and Operations at the University of Michigan Ross School of Business. He is interested in social learning and belief formation and their applications in a wide variety of settings.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-mohamed-mostagir/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240308T100000
DTEND;TZID=Asia/Singapore:20240308T113000
DTSTAMP:20260417T132549
CREATED:20240304T031806Z
LAST-MODIFIED:20240304T031806Z
UID:20807-1709892000-1709897400@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Kimon Drakopoulos
DESCRIPTION:Name of Speaker\nKimon Drakopoulos\n\n\nSchedule\n8 March 2024\, 10am – 11.30am\n\n\nVenue \nBIZ1- 0206\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0scuyqrT4vGtDnHl1AToXlPUExri0y7Suq\n\n\nTitle\nBlockchain Mediated Persuasion\n\n\nAbstract\nAn ex-post informed Sender wishes to persuade a rational Bayesian Receiver to take a desired action\, as in the classic Bayesian Persuasion model studied by Kamenica and Gentzkow (2011). However\, we consider settings in which Sender cannot reliably commit to a signal mechanism. An alternative approach is to consider a trustworthy mediator that receives a reported state of the world from Sender and then\, based on this report\, generates a signal realization for Receiver. Such mediation can be implemented via costly blockchain technology. Surprisingly\, we show that this cost differentiated mediation succeeds where free mediation fails. By requiring Sender to pay the mediator for different signal realizations\, we can effectively incentivize them to truthfully report\, which in turn allows for beneficial persuasion to take place. Joint with Justin Mulvany\, Irene Lo\n\n\nAbout the Speaker\nKimon Drakopoulos is an Associate Professor in Business Administration at the Data Sciences and Operations department at the USC Marshall School of Business. His research focuses on the operations of complex networked systems\, social networks\, stochastic modeling\, game theory and information economics. Kimon is currently serving in the high level advisory committee to the Greek government on AI regulation and implementation. In 2020 he served as the Chief Data Scientist of the Greek National COVID-19 Scientific taskforce and a Data Science and Operations Advisor to the Greek Prime Minister. He has been awarded the Wagner Prize for Excellence in Applied Analytics and the Pierskalla Award for contributions to Healthcare Analytics.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-kimon-drakopoulos/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240311T100000
DTEND;TZID=Asia/Singapore:20240311T113000
DTSTAMP:20260417T132549
CREATED:20240304T032033Z
LAST-MODIFIED:20240304T032033Z
UID:20810-1710151200-1710156600@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Arnoud den Boer
DESCRIPTION:Name of Speaker\nArnoud den Boer\n\n\nSchedule\n11 March 2024\, 10am – 11.30am\n\n\nVenue \nBIZ1- 0304\n\n\nLink to Register \n \nhttps://nus-sg.zoom.us/meeting/register/tZcrdeytpjgsGtEen4nnptCLRFl85dvfKFvR\n\n\nTitle\nCan price algorithms learn to form a cartel?\n\n\nAbstract\nCan price algorithms learn to form a cartel instead of compete against each other\, potentially leading to higher consumer prices and lower social welfare? The question is controversial among economists and competition policy regulators. One the one hand\, concerns have been expressed that self-learning price algorithms do not only make it easier to form price cartels\, but also that this can be achieved within the boundaries of current antitrust legislation – raising the question whether the existing competition law needs to be adjusted to mitigate undesired algorithmic collusion. On the other hand\, a number of economists believe that algorithms learning to collude is science fiction\, except by using forms of signaling or communication that are already illegal\, and argue that there is no need to change antitrust laws. Motivated by this discussion\, I will present work that shows that under some market conditions\, price algorithms can learn to collude. Based on joint work with Janusz Meylahn\, Thomas Loots\, Maarten Pieter Schinkel\, Ali Aouad.\n\n\nAbout the Speaker\nArnoud is Associate Professor at the Korteweg-de Vries Institute for Mathematics of the University of Amsterdam. He studied Mathematics at Utrecht University (2006)\, Mathematics for Industry at Eindhoven University of Technology (2008) and wrote his PhD thesis `Dynamic Pricing and Learning’ (2013) about data-driven price algorithms at the CWI Centrum for Wiskunde and Computer Science in Amsterdam. Arnoud’s research focuses on the interface of learning and optimization\, with applications in dynamic pricing and revenue management. He is the recipient of several awards and grants\, including the 2015 Gijs de Leve prize for best PhD Thesis in operations research defended in the Netherlands in the period 2012-2014\, personal grants from the Dutch Science Foundation\, and the INFORMS Revenue Management & Pricing Section Prize. Arnoud serves as editor for Management Science\, M&SOM\, and POMS\, is board member of the Euro Working Group on Pricing and Revenue Management and board member of the INFORMS Revenue Management and Pricing Section.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-arnoud-den-boer/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240313T100000
DTEND;TZID=Asia/Singapore:20240313T113000
DTSTAMP:20260417T132549
CREATED:20240307T031244Z
LAST-MODIFIED:20240307T031244Z
UID:20941-1710324000-1710329400@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Zhang Fuqiang
DESCRIPTION:  \n\n\n\nName of Speaker\nZhang Fuqiang\n\n\nSchedule\n13 March 2024\, 10am – 11.30am\n\n\nVenue \nBIZ1-0307\n\n\nLink to Register \n \nhttps://nus-sg.zoom.us/meeting/register/tZUvdu2grDItHdZl1LhIH4lgx0-lY42S7eks\n\n\nTitle\nThe Bright Side of Price Volatility in Global Commodity Procurement\n\n\nAbstract\nThis paper studies two competing firms’ choices between the contingent-price contract (CPC) and fixed-price contract (FPC) in global commodity procurement. The FPC price is determined when signing the contract\, whereas the CPC price is pegged to an underlying index and remains open until the delivery date. Under both contracts\, each firm determines its order quantity based on the updated belief about the market demand. The unrealized CPC price correlates with the market demand\, allowing a firm to update its belief about the CPC price using demand information\, thereby generating a price-learning effect. We find that\, contrary to conventional wisdom\, a larger price volatility could benefit the firms\, and\, under differentiated contracts\, a firm might benefit from the improvement of forecast accuracy at its rival. We further show that the price-learning effect plays a critical role in the firms’ contract choices. First\, significant price volatility forces the firms to pursue the responsiveness of the CPC. Second\, the firms may adopt differentiated contracts to enhance their responses to market changes and dampen competition\, and a higher competition intensity more likely leads to contract differentiation. Third\, the firms in a small market seek responsiveness and contract differentiation rather than cost efficiency. This study reveals the bright side of price volatility and takes a step toward understanding the effect of two-dimensional information updating.\n\n\nAbout the Speaker\nFuqiang Zhang is the Dan Broida professor of Supply Chain\, Operations\, and Technology (SCOT) at Olin Business School\, Washington University in St. Louis. He also serves as the SCOT area chair and academic director of MBA programs at Olin. Professor Zhang obtained his Ph.D. in Managerial Science and Applied Economics from the Wharton School\, University of Pennsylvania. His research interests focus on supply chain and technology innovation\, consumer analytics in operations management\, and sustainable operations. In recent years\, he has been working on research topics that are driven by empirical data. Professor Zhang’s research has appeared in top-tier academic journals such as Management Science\, Manufacturing & Service Operations Management\, Operations Research\, Marketing Science\, and Production and Operations Management.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-zhang-fuqiang/
CATEGORIES:IORA Seminar Series
END:VEVENT
END:VCALENDAR