BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//IORA - Institute of Operations Research and Analytics - ECPv6.15.11//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:IORA - Institute of Operations Research and Analytics
X-ORIGINAL-URL:https://iora.nus.edu.sg
X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Singapore
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:+08
DTSTART:20240101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20250815T100000
DTEND;TZID=Asia/Singapore:20250815T113000
DTSTAMP:20260507T045009
CREATED:20250818T044156Z
LAST-MODIFIED:20250818T044156Z
UID:26974-1755252000-1755257400@iora.nus.edu.sg
SUMMARY:DAO-ISEM-IORA Seminar Series : Daniel Russo
DESCRIPTION:Name of speaker\n\n\n\nDaniel Russo\n\n\n\n\n\nSchedule\n\n\n15 August 2025\, 10am – 11.30am\n\n\n\n\n\nVenue\n\n\n\nBIZ2 – 0413C\n\n\n\n\n\nLink to register\n\n\n\nhttps://nus-sg.zoom.us/meeting/register/rT3lbgWGQB-LnXjhOw84MA\n\n\n\n\n\nTitle\n\n\nActive Exploration via Autoregressive Generation of Missing Data\n\n\n\n\nAbstract\n\n\nWe cast the challenges of uncertainty quantification and exploration in online decision-making as a problem of training and generation from an autoregressive sequence model\, an area experiencing rapid innovation. Central to our approach is viewing uncertainty as arising from missing outcomes that would be revealed through appropriate action choices\, rather than from unobservable latent parameters of the environment. This reformulation aligns naturally with modern machine learning capabilities: we can i) train generative models through next-token prediction rather than fit explicit priors\, ii) assess uncertainty through autoregressive generation rather than parameter sampling\, and iii) adapt to new information through in-context learning rather than explicit posterior updating. To showcase these ideas\, we formulate a challenging informed bandit learning task where effective performance requires leveraging unstructured prior information (like text features) while exploring judiciously to resolve key remaining uncertainties. We validate our approach through both theory and experiments. Our theory establishes a reduction\, showing success at offline next-outcome prediction translates to reliable online uncertainty quantification and decision-making\, even with strategically collected data. Semi-synthetic experiments show our insights bear out in a news-article recommendation task where article text can be leveraged to minimize exploration.\n\n\n\n\n\nAbout the Speaker\n\n\nDaniel Russo is a Philip H. Geier Jr. Associate Professor in the Decision\, Risk\, and Operations division of the Columbia Business School. His research lies at the intersection of machine learning and online decision making\, mostly falling under the broad umbrella of reinforcement learning. Outside academia\, Dan works as an Amazon scholar applying reinforcement learning to supply chain optimization. He previously spent five years working with Spotify to apply reinforcement learning and large language models to audio recommendations.  Dan completed his undergraduate studies in Math and Economics at the University of Michigan\, doctoral studies at Stanford University under the supervision of Benjamin Van Roy\, and worked as a postdoctoral researcher at Microsoft Research in New England. His research has been recognized by the Erlang Prize\, the Frederick W. Lanchester Prize\, a Junior Faculty Interest Group Best Paper Award\, and first place in the George Nicholson Student Paper Competition.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-daniel-russo/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20250822T100000
DTEND;TZID=Asia/Singapore:20250822T113000
DTSTAMP:20260507T045009
CREATED:20250818T043934Z
LAST-MODIFIED:20250821T021329Z
UID:26971-1755856800-1755862200@iora.nus.edu.sg
SUMMARY:DAO-ISEM-IORA Seminar Series : Alminas Žaldokas
DESCRIPTION:Name of Speaker\n\n\n\nAlminas Žaldokas\n\n\n\n\n\nSchedule\n\n\n\n22 August 2025\, 10am – 11.30am\n\n\n\n\n\nVenue\n\n\n\nHSS 4 – 7 (Hon Sui Sen Memorial Library\, level 4 Seminar Room)\n\n\n\n\n\nLink to Register\n\n\n\nhttps://nus-sg.zoom.us/meeting/register/zUMnlP9MRy2HoqduBcxjmA\n\n\n\n\nTitle\n\n\nESG Shocks in Global Supply Chains\n\n\n\n\nAbstract\n\n\nWe show that U.S. firms cut imports by 31.8% when their international suppliers experience environmental and social (E&S) incidents. These trade cuts are larger for publicly listed U.S. importers facing high E&S investor pressure and lead to crosscountry supplier reallocation\, suggesting that E&S preferences in capital markets can be privately costly but have real effects for foreign suppliers. Larger trade cuts around the incident result in better supplier E&S performance in subsequent years\, and in the eventual resumption of trade. Our results highlight the role of investors in ensuring suppliers’ E&S compliance along global supply chains.\n\n\n\n\n\nAbout the Speaker\n\n\nAlminas Žaldokas is currently an Associate Professor in Finance at the National University of Singapore (NUS). Prior to this appointment\, Alminas Žaldokas has been teaching at the Hong Kong University of Science and Technology (HKUST) since 2012 with the primary focus on corporate finance and corporate valuation. Apart from the undergraduate and MSc courses\, he was teaching in the HKUST-NYU MSc in Global Finance\, HKUST bilingual EMBA\, and Kellogg-HKUST EMBA programmes. He has also previously taught corporate valuation for the MBAs at the University of Texas in Austin McCombs School of Business in 2017/8 academic year.\n\n\nProfessor Žaldokas received his PhD in Finance at INSEAD in 2012. His previous academic degrees include MSc in Finance and Economics from London School of Economics and BSc in Business and Economics from Stockholm School of Economics in Riga. \nProfessor Žaldokas’s research focuses on the interaction between firm decisions in the financial and in the product markets. In particular\, he studies corporate finance decisions that relate to the firm investment in innovation\, the formation of collusive arrangements between firms\, and the facilitation of ESG practices. This research has been published in top academic journals such as Journal of Financial Economics\, Review of Financial Studies\, Journal of Accounting Research\, Management Science\, RAND Journal of Economics\, Journal of International Economics\, and Journal of Financial Intermediation.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-alminas-zaldokas/
CATEGORIES:IORA Seminar Series
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20250829T100000
DTEND;TZID=Asia/Singapore:20250829T113000
DTSTAMP:20260507T045009
CREATED:20250821T021303Z
LAST-MODIFIED:20250821T021303Z
UID:26990-1756461600-1756467000@iora.nus.edu.sg
SUMMARY:DAO-ISEM-IORA Seminar Series: Sean Zhou
DESCRIPTION:Name of Speaker\nSean Zhou\n\n\nSchedule\n29 August 2025\, 10am – 11.30am\n\n\nVenue \nHSS 4 – 7 (Hon Sui Sen Memorial Library\, level 4 Seminar Room)\n\n\nLink to Register \n \nhttps://nus-sg.zoom.us/meeting/register/HB2NQ5ZjRpuRwFiBsxDOxg\n\n\nTitle\nLearning and Pricing for Consumer Electronics Trade-in Program\n\n\nAbstract\nWe consider a dynamic pricing problem for a two-sided consumer electronics trade-in program\, where a firm acquires and re-sells multiple types of pre-owned (used) products over a finite selling horizon. There are customers trading in their used products for new products at discounted prices and customers buying refurbished products. The firm sets trade-in prices and resale prices to maximize its total expected profit. We first discuss the scenario that the firm knows the choice models of customers. Due to the high-dimensional state space\, deriving the optimal policy using dynamic programming is computationally intractable. To circumvent this\, we develop simple and provably effective heuristic policies based on the solution to a deterministic upper-bound problem. We design a dynamic policy called the Batched-Adjustment Control (BAC) policy\, under which the selling horizon is divided into different consecutive and disjoint batches for different products and the prices in one batch are updated based on the realized uncertainties in the previous batch. The profit loss of BAC relative to the optimal one is in the order of  . When the firm does not know the choice model parameters of customers\, it has to learn while making pricing decisions over time. We develop an algorithm called Parametric-Batched-Adjustment Control (PBAC)\, in which the firm first uses Maximum Likelihood Estimation to learn the trade-in and demand models’ parameters\, and then adopt a similar pricing policy akin to BAC while using the estimated parameters. With carefully chosen algorithm parameters (e.g.\, length of exploration phase\, batch size)\, we show that PBAC has a regret in the order of  . This is based on joint work with Zhuoluo Zhang (Xiamen University)\, Murray Lei (Queen’s University)\, and Wenhao Li (SUFE).\n\n\nAbout the Speaker\nSean Zhou is Professor and Chair of Department of Decisions\, Operations and Technology\, CUHK Business School\, and Professor in Department of Systems Engineering and Engineering Management (by courtesy)\, at The Chinese University of Hong Kong (CUHK). He has held visiting positions at National University of Singapore and University of Toronto. He received his Ph.D. in Operations Research from North Carolina State University. His main research interests are inventory management\, pricing\, sustainable operations\, data-driven supply chain optimization\, and operations and marketing interface. He serves as Area Editor (Inventory and Supply Chain Optimization) of OR Letters and Associate Editor of various journals including Naval Research Logistics and Service Science.
URL:https://iora.nus.edu.sg/events/dao-isem-iora-seminar-series-sean-zhou/
CATEGORIES:IORA Seminar Series
END:VEVENT
END:VCALENDAR