BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//IORA - Institute of Operations Research and Analytics - ECPv6.15.11//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://iora.nus.edu.sg
X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Singapore
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:+08
DTSTART:20220101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230908T100000
DTEND;TZID=Asia/Singapore:20230908T113000
DTSTAMP:20260417T132352
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:20260417T132352
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:20260417T132352
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
END:VCALENDAR