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X-WR-CALNAME:IORA - Institute of Operations Research and Analytics
X-ORIGINAL-URL:https://iora.nus.edu.sg
X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
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TZID:Asia/Singapore
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DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20241003T100000
DTEND;TZID=Asia/Singapore:20241003T113000
DTSTAMP:20260419T231258
CREATED:20240926T024637Z
LAST-MODIFIED:20240926T024637Z
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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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20241011T100000
DTEND;TZID=Asia/Singapore:20241011T113000
DTSTAMP:20260419T231258
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20241014T170000
DTEND;TZID=Asia/Singapore:20241014T180000
DTSTAMP:20260419T231258
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
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