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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230804T100000
DTEND;TZID=Asia/Singapore:20230804T113000
DTSTAMP:20260418T143655
CREATED:20230802T041735Z
LAST-MODIFIED:20230802T041815Z
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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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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:20260418T143655
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
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240319T100000
DTEND;TZID=Asia/Singapore:20240319T113000
DTSTAMP:20260418T143655
CREATED:20240311T030605Z
LAST-MODIFIED:20240311T030605Z
UID:21418-1710842400-1710847800@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Zhu Weiming
DESCRIPTION:  \n\n\n\n\nName of Speaker\nZhu Weiming\n\n\nSchedule\n19 March 2024\, 10am – 11.30am\n\n\nVenue\nBIZ1-0206\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZcvdu-rpzkuHNTHWtUz_oEaagwlXP1_FVHp\n\n\nTitle\nExtracting Efficiency from Chaos: Rider Behavior\, Performance and Negative Externality in Dockless Bike Sharing Systems\n\n\nAbstract\nIn recent years\, urban bike-sharing systems developed in two main forms: Dock-based systems that rely on fixed location docks to park at and pick up bicycles from\, and Dockless systems which allow users to pick up and drop off bikes anywhere. By eliminating reliance on fixed docks\, dockless systems provide significantly improved mobility and convenience to riders\, yet they have downsides such as high operational costs and occupying valuable sidewalk space. Collaborating with a major dockless bike-sharing platform\, we empirically analyze riders’ economic incentives\, explore the efficiency and measure the negative externality of this business model. Specifically\, we address the following questions: (i) What is the impact of the number of bicycles in the system on efficiency? (ii) How does the efficiency of dockless and dock-based systems compare? (iii) How can bike-share company minimize the negative externalities generated during service provision through smart relocation?\n\n\nAbout the Speaker\nWeiming Zhu is an Associate Professor in Innovation and Information Management at HKU Business School. Weiming obtained his bachelor’s degree in Physics from HKUST and Ph.D. in Operations Management from the Robert H. Smith School of Business at University of Maryland. Prior to joining the University of Hong Kong\, he was an Associate Professor in IESE’s Department of Production\, Technology and Operations Management. Weiming has also been a visiting professor in the Institute for Data\, Systems\, and Society (IDSS) at Massachusetts Institute of Technology and Kellogg School of Management at Northwestern University. Weiming’s research interests include operations in the platform economy\, urban mobility\, and supply chain finance. His work has been published at Management Science\, M&SOM and Journal of International Economics\, and has been recognized in M&SOM\, POMS\, Service Science and CSAMSE best paper award competitions.\n\n\n\n\n 
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-zhu-weiming/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240322T100000
DTEND;TZID=Asia/Singapore:20240322T113000
DTSTAMP:20260418T143655
CREATED:20240315T084423Z
LAST-MODIFIED:20240315T084423Z
UID:21570-1711101600-1711107000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Sasa Zorc
DESCRIPTION:  \n\n\n\n\nName of Speaker\nSasa Zorc\n\n\nSchedule\n22 March 2024\, 10am – 11.30am\n\n\nVenue\nBIZ1-0206\n\n\nLink to Register \n \nhttps://nus-sg.zoom.us/meeting/register/tZIpce2srDwrHdyCUf-w1Rtdh_3c-YlU9nTz\n\n\nTitle\nSearch with Recall and Gaussian Learning\n\n\nAbstract\nThe classic sequential search problem rewards the decision maker with the highest sampled value\, minus a cost per sample. If the sampling distribution is unknown\, then a Bayesian decision maker faces a complex balance between exploration and exploitation. We solve the stopping problem of sampling from a Normal distribution with unknown mean and unknown variance and a conjugate prior\, a longstanding open problem. The optimal stopping region may be empty (it may be optimal to continue the search regardless of the offer one receives\, especially at the early stages)\, or it may consist of one or two bounded intervals. While a single reservation price cannot describe the optimal rule\, we do find a standardized reservation rule: stop if and only if the standardized value of the current offer is sufficiently high relative to the standardized search cost. We also introduce the index function\, which provides a computationally practical way to implement the standardized stopping rule for any given prior\, sampling history\, and sampling horizon.\n\n\nAbout the Speaker\nSasa Zorc is an assistant professor at the Darden School of Business\, University of Virginia. Sasa obtained his PhD in Management from INSEAD. He studies incentives in multi-agent systems such as health care and matching markets (both centralized and decentralized). Methodologically\, his research relies on stochastic dynamic games\, search theory\, dynamic mechanism design\, contract theory and data-driven simulations.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-sasa-zorc/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240405T100000
DTEND;TZID=Asia/Singapore:20240405T110000
DTSTAMP:20260418T143655
CREATED:20240401T015600Z
LAST-MODIFIED:20240401T015649Z
UID:22117-1712311200-1712314800@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Wang Guihua
DESCRIPTION:  \n\n\n\nName of Speaker\nWang Guihua\n\n\nSchedule\n5 April 2024\, 10am – 11am\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZ0ucOmorj8sEtZEDn8rf411mXUZJEvpgeaO\n\n\nTitle\nThe Spillover Effect of Suspending Non-essential Surgery: Evidence from Kidney Transplantation\n\n\nAbstract\nOrgan transplantation is a life-saving procedure for patients with end-stage organ disease; delaying transplantation can have life-or-death consequences. Between March and April 2020\, in the midst of the COVID-19 pandemic\, multiple state governors issued orders to temporarily suspend non-essential surgery. Although such suspensions were not intended for essential surgery (e.g.\, deceased-donor kidney transplants)\, the literature on service operations management suggests that these suspensions may have either a positive or a negative spillover effect on essential surgery\, depending on whether hospitals maintain or reduce resources in response to such suspensions. Motivated by this dichotomy\, we estimate the potential spillover effect of suspending nonessential surgery on deceased-donor kidney transplantation. Through analyzing a dataset of all U.S. kidney transplant procedures\, we observe a steep decline in the transplant volume in almost all states during the early months of the pandemic. However\, states that suspended nonessential surgery experienced steeper declines than those that did not. Using a difference-in-differences approach\, we estimate a state-level suspension of non-essential surgery led to a 23.6% reduction in transplant volume. This negative spillover effect is particularly pronounced for low-efficiency transplant centers with long cold ischemia times (CITs)\, but less so for high-efficiency centers. Our mediation analysis shows 38.7% of the spillover effect can be attributed to the change in healthcare employment. Our study suggests that in the event of a future public health crisis\, policymakers should consider more nuanced approaches to securing the healthcare workforce critical to supporting essential services\, especially for transplant centers with long CITs.\n\n\nAbout the Speaker\nGuihua is an Assistant Professor of Operations Management and a Sydney Smith Hicks Faculty Fellow at the Naveen Jindal School of Management\, The University of Texas at Dallas. He obtained his PhD from the University of Michigan\, MSc from the Georgia Institute of Technology\, MSc and BEng from the National University of Singapore. Prior to his PhD study\, Guihua worked as a supervisor of the industrial engineering department at UPS Asia headquartered in Singapore.  Guihua’s research focuses on the intersection of empirical econometrics and machine learning with application to personalized healthcare. More specifically\, Guihua has developed new empirical machine learning techniques such as instrumental variable forest and first-difference causal tree for heterogeneous treatment effect analysis using observational healthcare data. Guihua’s research has been published at Management Science\, Manufacturing & Service Operations Management\, Production and Operations Management\, Advances in Applied Probability\, Surgery\, and Annals of Thoracic Surgery\, and received media coverage by Associate Press\, Crain’s Detroit\, Houston Chronicle\, Medical Xpress\, National Interest\, NPR\, PRI\, Science Daily\, Simply Flying\, The Conversation\, and Yahoo! News. His research was named a finalist of both the Pierskalla Best Paper Award and the MSOM Service Management SIG Best Paper Award\, a runner-up of both the Financial Times Responsible Business Education Award and the INFORMS Service Science Best Cluster Paper Award\, the winner of the INFORMS Health Applications Society Student Paper Competition\, two-time finalists of the MSOM Student Paper Competition\, and a finalist of the INFORMS Service Section Best Student Paper Competition.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-wang-guihua/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240412T100000
DTEND;TZID=Asia/Singapore:20240412T113000
DTSTAMP:20260418T143655
CREATED:20240403T063341Z
LAST-MODIFIED:20240403T063341Z
UID:22241-1712916000-1712921400@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Michael Freeman
DESCRIPTION:Name of Speaker\nMichael Freeman\n\n\nSchedule\n12 April 2024\, 10am – 11.30am\n\n\nVenue \nHon Sui Sen Memorial Library HSS 3-1\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZIofu-hqTosGdL3UD4H6gyub6ROFYIUrSpf\n\n\nTitle\nFrom full-time to flexi: Investigating the impact of workforce shifts in primary care on service provision and quality\n\n\nAbstract\nPrimary care practices worldwide are experiencing a shift away from traditional full-time roles toward increased part-time and temporary staffing models. This transformation necessitates understanding the implications for primary healthcare delivery. Our paper provides an empirical analysis examining the evolving work patterns and schedules of general practitioners (GPs) in the UK over the past decade. Using primary care records\, we first examine how GP work volume impacts healthcare service utilization and patient outcomes. We also explore factors that may mediate this relationship. Going further\, given the seeming inevitability of more flexible staffing models\, our investigation also aims to inform effective workforce strategies that optimise healthcare delivery. Specifically\, we examine scheduling approaches that may allow primary care practices to adapt to part-time and locum GPs. Our findings aim to inform healthcare leaders seeking to adapt to emerging flexible staffing models while maintaining high-quality and accessible primary care.\n\n\nAbout the Speaker\nMichael Freeman is an Assistant Professor of Technology and Operations Management at INSEAD. His research focuses on healthcare operations\, leveraging large empirical datasets and rigorous analysis to uncover actionable insights that inform practices to enhance healthcare delivery. This work centers on two key themes – improving patient routing and care continuity and assessing the impacts of organisational changes and predictive technologies on healthcare productivity and quality. He has published several papers in Management Science and M&SOM\, and his research has also been recognized with various awards\, including winning the 2016 MSOM Student Paper Competition. His research has also earned attention from media outlets (BBC\, The Guardian\, and others) and healthcare leaders\, including the UK’s Royal College of Emergency Medicine and College of General Practitioners. Alongside research\, Michael is an award-winning teacher at INSEAD\, where he directs and teaches in executive education\, the EMBA\, MBA\, and PhD programmes.
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-michael-freeman/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240531T100000
DTEND;TZID=Asia/Singapore:20240531T113000
DTSTAMP:20260418T143655
CREATED:20240517T034742Z
LAST-MODIFIED:20240517T034742Z
UID:22703-1717149600-1717155000@iora.nus.edu.sg
SUMMARY:DAO-IORA Seminar Series - Tauhid Zaman
DESCRIPTION:  \n\n\n\nName of Speaker\nTauhid Zaman\n\n\nSchedule\n31 May 2024\, 10am – 11.30am\n\n\nVenue \nBIZ1- 0307\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZMlduChqDoiGNbvqS_7q_XH-E66BMzMMYIl\n\n\nTitle\nSocial Media Suspensions and Shadow Banning: Political Bias or Genuine Disinformation Control?\n\n\nAbstract\nSocial media platforms like Twitter and TikTok wield significant influence over the visibility and reach of content\, raising critical questions: Are certain voices marginalized based on political leanings\, or are platform interventions primarily aimed at mitigating the spread of disinformation? Recent discussions highlight allegations of political bias in account suspensions and “shadow banning\,” a subtle mechanism where certain content is less visible to users\, often without their knowledge. \nDrawing from a field study on Twitter\, we investigate these claims. Our findings suggest that while some account suspensions may appear biased\, there is equally strong evidence indicating that these actions are strategic efforts to combat the spread of disinformation. \nWe then delve into the issue of shadow banning\, demonstrating how this mechanism can be used to shape opinions into arbitrary distributions by solving a linear program. Through simulations on real social network topologies\, we show that if shadow banning were driven by partisanship\, its implementation could be so subtle that overt political biases remain undetected. This underscores the imperative for rigorous transparency and oversight in the actions of social media platforms. \nThis talk is based on the following papers: \n\n Analysis: Trade-offs between reducing misinformation and politically-balanced enforcement on social media: https://osf.io/preprints/psyarxiv/ay9q5\n Shaping Opinions in Social Networks with Shadow Banning: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0299977\n\n\n\n\nAbout the Speaker\nTauhid is an Associate Professor of Operations Management at the Yale School of Management. He received his BS\, MEng\, and PhD degrees in electrical engineering and computer science from MIT.  His research focuses on information operations problems in the context of social media.  Some of the topics he has studied include combating online extremists\, identifying bots\, and designing influence campaigns.  His broader interests cover data driven approaches to investing in startup companies\, algorithmic sports betting\, and biometric data.  His work has been featured in the Wall Street Journal\, Wired\, Mashable\, the LA Times\, Bloomberg\, and Time Magazine. \nWebsite: https://www.zlisto.com/\n\n\n\n 
URL:https://iora.nus.edu.sg/events/dao-iora-seminar-series-tauhid-zaman/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240823T100000
DTEND;TZID=Asia/Singapore:20240823T113000
DTSTAMP:20260418T143655
CREATED:20240817T125552Z
LAST-MODIFIED:20240817T125552Z
UID:23044-1724407200-1724412600@iora.nus.edu.sg
SUMMARY:DAO-ISEM-IORA Seminar Series - Daewon Sun
DESCRIPTION:Name of Speaker\nDaewon Sun\n\n\nSchedule\n23 August 2024\, 10am – 11.30am\n\n\nVenue \nBIZ1- 0305\n\n\nLink to Register \n \nhttps://nus-sg.zoom.us/meeting/register/tZckdu-tqzsiGdVFbEmxw3iRRCw-MKTKz2pe\n\n\nTitle\nCo-Developing Technology Products by Asymmetric Competitors\n\n\nAbstract\nTechnology products often have vital components owned or patented by different firms\, each of whom also seeks to compete in the end-user market. Consequently\, firms engage in a hybrid of collaboration and competition\, where one firm might provide a crucial component or capability to another that sells a competing end-market product. For instance\, Samsung sells smartphones and supplies organic light-emitting diode displays for Apple’s smartphones. The component buyer firm (i.e.\, firm A) may consider co-investing to improve the quality of a shared component (made by firm B). But how do firms gain strategic benefits in such arrangements? What factors govern their investment level? Who benefits\, and who loses? This paper addresses these questions. We find that $B$’s own investment is motivated more by increased partnership profit than by higher profit in the end-user market because competition limits the gains from a better product. Indeed\, when A has sufficient end-product superiority\, B exits the end-user market and becomes more aggressive in improving the quality of the shared capability. A strategic reason for A to co-invest in the shared capability is the desire for a higher performance level than that chosen by B (on its own) which self-throttles its own investment because it cannot fully monetize the gains in a competitive market. In doing so\, both A and B make higher-quality end-user products and earn higher profits. Still\, B gains more because it can extract part of A’s gains through an higher component unit price. Crucially\, firm A co-invests even though the investment prolongs firm B’s ability to maintain an end-user market presence in direct competition with A. Finally\, we demonstrate that B could limit its Stackelberg leadership power to further improve the joint investment in an asymmetric partner relationship\, which can yield a win-win outcome for both firms.\n\n\nAbout the Speaker\nDaewon Sun is a professor in Department of IT\, Analytics\, and Operations at Mendoza College of Business\, University of Notre Dame. He holds a Ph.D. in Management Science and Information Systems from the Pennsylvania State University. Professor Sun’s primary research interests are in pricing strategies and resource management\, including IT Product Pricing and Launching Strategies\, eBusiness Strategies\, Operations Management and Marketing Interfaces\, and Supply Chain Conflict and Coordination. Professor Sun’s published papers appear in top-tier journals and his research was recognized by several professional awards including 2013 AIS Best Information Systems Publications Award of the Year. He is a senior editor for Production and Operations Management and an associate editor for Decision Sciences journals. Professor Sun teaches information systems\, operations management\, and business analytics core courses\, including Systems Analysis and Design\, IS Capstone Project\, Introduction to Process Analytics\, Business Intelligence\, and predictive analytics.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-daewon-sun/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20240828
DTEND;VALUE=DATE:20240830
DTSTAMP:20260418T143655
CREATED:20240808T022259Z
LAST-MODIFIED:20240808T023501Z
UID:22986-1724803200-1724975999@iora.nus.edu.sg
SUMMARY:Analytics for X 2024
DESCRIPTION:The Institute of Operations Research and Analytics (IORA)\, Department of Industrial Systems Engineering and Management (College of Design and Engineering) and Department of Analytics and Operations (NUS Business School) of the National University of Singapore are proud to jointly host the Analytics for X 2024 conference in conjunction with the  Next-Gen Scholars Symposium on 28-29 August 2024. \nAnalytics for X is an Annual Conference hosted by IORA that brings together researchers\, educators\, consultants\, practitioners\, and students to contribute to the Fourth Industrial Revolution conversation. After a successful international event in 2023 in Chongqing\, China\, this year\, the conference is back in Singapore. As an annual event\, the conference aims to promote the regular enhancement and dissemination of knowledge related to operations research and analytics\, as well as efficient industrial practices. \nDuring this year’s event\, we are excited to announce the inaugural Next-Gen Scholars Symposium. This symposium\, exclusive to PhD students\, aims to recognize outstanding scholars in Analytics\, Operations Research (OR) and Operations Management (OM) who are nearing the completion of their PhD\, typically within a year or two. It is dedicated to honoring and accelerating the paths of outstanding young scholars who find themselves at a pivotal stage in their academic journey. The symposium will feature the groundbreaking research of these emerging scholars\, and offer them a platform to engage with and learn from the Analytics & OR/OM community\, as well as efficient industrial practices through networking opportunities.
URL:https://iora.nus.edu.sg/events/analytics-for-x-2024/
LOCATION:Innovation 4.0\, Level 1\, 3 Research Link\, 117602\, Singapore
CATEGORIES:Analytics For X
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20240830T090000
DTEND;TZID=Asia/Singapore:20240830T130000
DTSTAMP:20260418T143655
CREATED:20240808T025522Z
LAST-MODIFIED:20240808T025522Z
UID:22997-1725008400-1725022800@iora.nus.edu.sg
SUMMARY:IORA Industry Day 2024
DESCRIPTION:The Institute of Operations Research and Analytics (IORA) is proud to host the inaugural IORA Industry Day 2024. This half-day event will highlight innovative applications of Operations Research and Data Analytics in addressing real-world industry challenges. \nThis year’s program will showcase six pioneering projects\, developed in partnership with research collaborators from the airline\, manufacturing\, charity/food\, and data center sectors. These projects address critical issues including the food distribution supply chain\, pricing engines\, human-AI collaboration\, pilot training\, and energy consumption optimization. \nWe are pleased to welcome guest speakers from Optimisation Analytics Technology\, who will present their innovative work with McDonald’s China. \nAdditionally\, the event will feature an engaging panel discussion with esteemed experts: Marc Dragon (Managing Director\, Reefknot Investments)\, Dr. Simon See (Senior Director\, NVIDIA Global Technology Centre)\, and Dr. Yang Nan (Senior Economist and Head of Economics and Decision Sciences at Amazon Japan). They will discuss future opportunities for Operations Research in solving industry problems. The session will be moderated by Prof. Jussi Keppo\, Research Director (Industry) at IORA. \nWe invite you to join us for this exciting event. Please register by 26th August 2024 (Mon) to reserve your seats.
URL:https://iora.nus.edu.sg/events/iora-industry-day-2024/
LOCATION:Innovation 4.0\, Level 1\, 3 Research Link\, 117602\, Singapore
CATEGORIES:IORA Industry Day
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2024/08/IORA-Industry-Day_web-banner.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20241003T100000
DTEND;TZID=Asia/Singapore:20241003T113000
DTSTAMP:20260418T143655
CREATED:20240926T024637Z
LAST-MODIFIED:20240926T024637Z
UID:23184-1727949600-1727955000@iora.nus.edu.sg
SUMMARY:DAO-ISEM-IORA Seminar Series: Song-Hee Kim
DESCRIPTION:Name of Speaker\nSong-Hee Kim\n\n\nSchedule\n3 October 2024\, 10am – 11.30am\n\n\nVenue \nHSS 3-2 (Hon Sui Sen Memorial Library\, level 3\, Seminar Room 2)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZMkfu2vqzovHdT3wuo-DasFqx2x7PnDzw1V\n\n\nTitle\nTo Each Their Own (Shifts): Incorporating Heterogeneous Worker Preferences into Shift Work Schedules\n\n\nAbstract\nShifts are the dominant way to organize work in many contexts requiring 24/7 coverage. While the detriments of shift work are well-documented both at the individual and organizational levels\, its deployment is often unavoidable given round-the-clock staffing needs. We explore a potential operational lever-incorporating heterogeneous preferences over shift characteristics\, which we refer to as the shift choice system-to mitigate ramifications of shift work on worker well-being and turnover. Leveraging rich and novel survey\, shift\, and administrative data\, we document that inpatient nurses exhibit heterogeneous preferences over shift schedules\, driven by both pecuniary and non-pecuniary considerations. We also show that nursing managers largely reflect preferences into scheduled shifts\, albeit imperfectly. We find that the shift choice system improves worker well-being\, as measured by self-reported fatigue and work-life balance. Using a difference-in-differences approach\, we also estimate a 0.58 p.p. decrease in probability of quitting\, but only among more experienced nurses. We find these effects are not driven by differences in the degree to which preferences are reflected in scheduled shifts\, but rather by corresponding improvements in fatigue and work-life balance that are concentrated among more experienced nurses. We do not find evidence to suggest that the shift choice system affects care quality. Our results indicate that allowing for shift choice is an effective responsible scheduling strategy that can improve worker well-being and reduce turnover for highly experienced nurses. Paper available at https://ssrn.com/abstract=4750664.\n\n\nAbout the Speaker\nSong-Hee Kim is the CS Wind Associate Professor of Operations Management at SNU Business School of Seoul National University in South Korea. Her research focuses on making data-driven and evidence-based decisions within service systems\, with an emphasis on problems related to healthcare delivery. She has received several academic awards\, including the Best OM Paper in Management Science Award (winner)\, MSOM Best Paper Award (finalist)\, and INFORMS Pierskalla Award (finalist). She currently serves as an associate/senior editor for the journals Management Science\, Operations Research\, Manufacturing & Services Operations Management\, Production and Operations Management\, Service Science\, and Health Care Management Science. She received her BS from Cornell University and her PhD from Columbia University. Prior to joining SNU Business School\, she was a postdoctoral associate at the Yale School of Management and an assistant professor at the Marshall School of Business\, University of Southern California.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-song-hee-kim/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20241011T100000
DTEND;TZID=Asia/Singapore:20241011T113000
DTSTAMP:20260418T143655
CREATED:20241002T062242Z
LAST-MODIFIED:20241002T062242Z
UID:23187-1728640800-1728646200@iora.nus.edu.sg
SUMMARY:DAO-ISEM-IORA Seminar Series : Negin Golrezaei
DESCRIPTION:  \n\n\n\nName of Speaker\nNegin Golrezaei\n\n\nSchedule\n11 October 2024\, 10am – 11.30am\n\n\nVenue \nBIZ1 – 0201 (Mochtar Riady Building\, level 2 Seminar Room)\n\n\nLink to Register \n \nhttps://nus-sg.zoom.us/meeting/register/tZYvd-iuqjksG937fGGVd2M-Dn16gT0CImgP\n\n\nTitle\nOnline Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization\n\n\nAbstract\nIn today’s digital landscape\, the ability to make timely and informed decisions is paramount. Our research addresses the challenge of adapting offline algorithms to dynamic online scenarios\, presenting a versatile framework with wide-reaching implications. We focus on combinatorial problems that lend themselves to efficient approximations through robust greedy algorithms. Our framework leverages the concept of “Blackwell approachability” to seamlessly transform these algorithms from offline to online settings. \nUnder full information conditions\, our online algorithms exhibit approximate regrets of 𝑂(√𝑇). Taking our approach further\, we introduce “Bandit Blackwell approachability\,” extending its applicability to dynamic online decision-making. In the bandit setting\, our framework achieves approximate regrets of 𝑂(𝑇^{2/3}). \nOur research extends to various domains\, including revenue management\, market design\, and online optimization. We tackle challenges such as product ranking optimization\, auction reserve price optimization\, and submodular maximization. Moreover\, we apply our techniques to first-order methods in continuous optimization\, facilitating efficient solutions in various contexts. Numerical simulations demonstrate that our approach outperforms theoretical expectations in real-world scenarios.\n\n\nAbout the Speaker\nNegin Golrezaei is the W. Maurice Young (1961) Career Development Associate Professor of Management and an Associate Professor of Operations Management at the MIT Sloan School of Management. Her research focuses on advancing online marketplaces—such as e-commerce\, online advertising\, and emissions trading systems—by designing and implementing data-driven strategies and algorithmic innovations. She aims to create more resilient\, equitable\, and sustainable digital ecosystems. Before joining MIT\, Negin was a postdoctoral fellow at Google Research in New York\, where she collaborated with the Market Algorithm team to develop and test new mechanisms for online marketplaces. She holds a BSc (2007) and MSc (2009) in electrical engineering from Sharif University of Technology\, Iran\, and a PhD (2017) in operations research from the University of Southern California. Negin serves as an associate editor for Operations Research\, Production and Operations Management\, Operations Research Letters\, and Naval Research Logistics. Her recognitions include the 2021 ONR Young Investigator Award\, the 2018 Google Faculty Research Award\, the 2017 George B. Dantzig Dissertation Award\, the INFORMS Revenue Management and Pricing Section Dissertation Prize\, and the USC Outstanding Teaching Award (2017).\n\n\n\n 
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-negin-golrezaei/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20241014T170000
DTEND;TZID=Asia/Singapore:20241014T180000
DTSTAMP:20260418T143655
CREATED:20241009T032344Z
LAST-MODIFIED:20241009T032344Z
UID:23205-1728925200-1728928800@iora.nus.edu.sg
SUMMARY:DAO-ISEM-IORA Seminar Series : Zhang Xiaoge
DESCRIPTION:Name of Speaker\nZhang Xiaoge\n\n\nSchedule\n14 October 2024\, 5pm\n\n\nVenue \nE1-07-21/22 – ISEM Executive Classroom\n\n\nLink to Register\nhttps://nus-sg.zoom.us/j/88657161589?pwd=lDmwUK7HFFbONVA8anheLis4tqTVrB.1\n\n\nTitle\nReliability Engineering in the era of AI: An Uncertainty Quantification Framework\n\n\nAbstract\nEstablishing trustworthiness is fundamental for the responsible utilization of medical artificial intelligence (AI)\, particularly in cancer diagnostics\, where misdiagnosis can lead to devastating consequences. However\, there is currently a lack of systematic approaches to resolve the reliability challenges stemming from the model limitations and the unpredictable variability in the application domain. In this work\, we address trustworthiness from two complementary aspects—data trustworthiness and model trustworthiness—in the task of subtyping non-small cell lung cancers using whole side images. We introduce TRUECAM\, a framework that provides trustworthiness-focused\, uncertainty-aware\, end-to-end cancer diagnosis with model-agnostic capabilities by leveraging spectral-normalized neural Gaussian Process (SNGP) and conformal prediction (CP) to simultaneously ensure data and model trustworthiness. Specifically\, SNGP enables the identification of inputs beyond the scope of trained models\, while CP offers a statistical validity guarantee for models to contain correct classification. Systematic experiments performed on both internal and external cancer cohorts\, utilizing a widely adopted specialized model and two foundation models\, indicate that TRUECAM achieves significant improvements in classification accuracy\, robustness\, fairness\, and data efficiency (i.e.\, selectively identifying and utilizing only informative tiles for classification). These highlight TRUECAM as a general wrapper framework around medical AI of different sizes\, architectures\, purposes\, and complexities to enable their responsible use.\n\n\nAbout the Speaker\nDr. Xiaoge Zhang is an Assistant Professor in the Department of Industrial and Systems Engineering (ISE) at The Hong Kong Polytechnic University. His research interests center on risk management\, reliability engineering\, and safety assurance of AI/ML systems using uncertainty quantification\, knowledge-enabled AI\, and fail-safe measures. He received his Ph.D. in Systems Engineering and Operations Research at Vanderbilt University\, Nashville\, Tennessee\, United States in 2019. He has won multiple awards\, including Peter G. Hoadley Best Paper Award\, Chinese Government Award for Outstanding Self-Financed Students Studying Abroad\, Bravo Zulu Award\, Pao Chung Chen Fellowship\, among others. He has published more than 70 papers in leading academic journals\, such as Nature Communications\, IEEE Transactions on Information Forensics and Security\, IEEE Transactions on Reliability\, IEEE Transactions on Cybernetics\, IEEE Transactions on Industrial Informatics\, Reliability Engineering & Systems Safety\, Risk Analysis\, Decision Support Systems\, and Annals of Operations Research\, among others. He is on the editorial board of Journal of Organizational Computing and Electronic Commerce\, Journal of Reliability Science and Engineering. He is a member of INFORMS\, IEEE and IISE.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-zhang-xiaoge/
CATEGORIES:IORA Seminar Series
END:VEVENT
END:VCALENDAR