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X-ORIGINAL-URL:https://iora.nus.edu.sg
X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
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DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230804T100000
DTEND;TZID=Asia/Singapore:20230804T113000
DTSTAMP:20260418T074100
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230810T100000
DTEND;TZID=Asia/Singapore:20230810T113000
DTSTAMP:20260418T074100
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20230825T100000
DTEND;TZID=Asia/Singapore:20230825T113000
DTSTAMP:20260418T074100
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
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