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X-WR-CALNAME:IORA - Institute of Operations Research and Analytics
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X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
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DTSTART:20200101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211001T100000
DTEND;TZID=Asia/Singapore:20211001T110000
DTSTAMP:20260405T104532
CREATED:20210812T024540Z
LAST-MODIFIED:20210924T090458Z
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SUMMARY:IORA Seminar Series – Hamsa Bastani
DESCRIPTION:Hamsa Bastani is an Assistant Professor of Operations\, Information\, and Decisions at the Wharton School\, University of Pennsylvania. Her research focuses on developing novel machine learning algorithms for data-driven decision-making\, with applications to healthcare operations\, social good\, and revenue management. She designs methods for sequential decision-making\, transfer learning and human-in-the-loop analytics. Her applied work uses large-scale\, novel data sources to inform policy around impactful societal problems. Her work has received several recognitions\, including the Pierskalla Award for the best paper in healthcare\, as well as first place in the George Nicholson and MSOM student paper competitions. \n\n\n\nName of Speaker\nDr Hamsa Bastani\n\n\nSchedule\nFriday 1 October 2021\, 10am\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZAtcO2rqT8jG9BLnqZzrhXlMX1cRbn316Hv\n\n\nTitle\nEfficient and targeted COVID-19 border testing via reinforcement learning\n\n\nAbstract\nThroughout the COVID-19 pandemic\, countries relied on a variety of ad-hoc border control protocols to allow for non-essential travel while safeguarding public health: from quarantining all travellers to restricting entry from select nations based on population-level epidemiological metrics such as cases\, deaths or testing positivity rates. Here we report the design and performance of a reinforcement learning system\, nicknamed ‘Eva’. In the summer of 2020\, Eva was deployed across all Greek borders to limit the influx of asymptomatic travellers infected with SARS-CoV-2\, and to inform border policies through real-time estimates of COVID-19 prevalence. In contrast to country-wide protocols\, Eva allocated Greece’s limited testing resources based upon incoming travellers’ demographic information and testing results from previous travellers. By comparing Eva’s performance against modelled counterfactual scenarios\, we show that Eva identified 1.85 times as many asymptomatic\, infected travellers as random surveillance testing\, with up to 2-4 times as many during peak travel\, and 1.25-1.45 times as many asymptomatic\, infected travellers as testing policies that only utilize epidemiological metrics. We demonstrate that this latter benefit arises\, at least partially\, because population-level epidemiological metrics had limited predictive value for the actual prevalence of SARS-CoV-2 among asymptomatic travellers and exhibited strong country-specific idiosyncrasies in the summer of 2020. Our results raise serious concerns on the effectiveness of country-agnostic internationally proposed border control policies that are based on population-level epidemiological metrics. Instead\, our work represents a successful example of the potential of reinforcement learning and real-time data for safeguarding public health.\nPaper Link: https://www.nature.com/articles/s41586-021-04014-z\n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-hamsa-bastani/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/08/Hamsa-Bastani-photo.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211008T100000
DTEND;TZID=Asia/Singapore:20211008T110000
DTSTAMP:20260405T104532
CREATED:20210812T024626Z
LAST-MODIFIED:20211001T023034Z
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SUMMARY:IORA Seminar Series – Van-Anh Truong
DESCRIPTION:Van-Anh Truong joined the Industrial Engineering and Operations Research Department at the Columbia University in 2010. She received a Bachelor’s degree from University of Waterloo in Mathematics in 2002\, and a Ph.D. from Cornell University in Operations Research in 2007. Before coming to Columbia\, she was a quantitative associate at Credit Suisse\, and a quantitative researcher at Google. \nShe is interested in a broad class of problems that arise in Supply Chain Management\, Healthcare\, and Business Analytics.   These problems address decision making under uncertainty in information-rich and highly dynamic environments.  Her recent work focuses on real-time optimization approaches for large e-commerce\, healthcare\, and service applications. \nHer research is supported by an NSF Faculty Early Career Development (CAREER) Award.  She is currently an Associate Editor for Operations Research\, MSOM\, and Naval Research Logistics. \n\n\n\nName of Speaker\nA/P Van-Anh Truong\n\n\nSchedule\nFriday 8 October 2021\, 10am (Singapore time)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZEpde2trzguGdY7aOovoJ5HUtpniis4z_MM\n\n\nTitle\nProphet Inequality with Correlated Arrival Probabilities\, with Application to Two Sided Matchings\n\n\nAbstract\nThe classical Prophet Inequality arises from a fundamental problem in optimal-stopping theory. In this problem\, a gambler sees a finite sequence of independent\, non-negative random variables. If he stops the sequence at any time\, he collects a reward equal to the most recent observation. The Prophet Inequality states that\, knowing the distribution of each random variable\, the gambler can achieve at least half as much reward in expectation\, as a prophet who knows the entire sample path of random variables (Krengel and Sucheston 1978). In this paper\, we prove a corresponding bound for correlated non-negative random variables. We analyze two methods for proving the bound\, a constructive approach\, which produces a worst-case instance\, and a reductive approach\, which characterizes a certain submartingale arising from the reward process of our online algorithm. We apply this new prophet inequality to the design of algorithms for a class of two-sided bipartite matching problems that underlie online task assignment problems. In these problems\, demand units of various types arrive randomly and sequentially over time according to some stochastic process. Tasks\, or supply units\, arrive according to another stochastic process. Each demand unit must be irrevocably matched to a supply unit or rejected. The match earns a reward that depends on the pair. The objective is to maximize the total expected reward over the planning horizon. The problem arises in mobile crowd-sensing and crowd sourcing contexts\, where workers and tasks must be matched by a platform according to various criteria. We derive the first online algorithms with worst-case performance guarantees for our class of two-sided bipartite matching problems.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-van-anh-truong/
CATEGORIES:IORA Seminar Series
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211015T100000
DTEND;TZID=Asia/Singapore:20211015T113000
DTSTAMP:20260405T104532
CREATED:20210812T024727Z
LAST-MODIFIED:20211008T074956Z
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SUMMARY:IORA Seminar Series – Yilun Chen
DESCRIPTION:Yilun Chen is an assistant professor in the School of Data Science at CUHK Shenzhen (currently on leave)\, and a postdoctoral researcher at Columbia Business School. He obtained a PhD in Operations Research at Cornell University in 2021. Yilun’s research interest lies broadly in applied probability\, with a specific focus on decision-making under uncertainty and its wide applications in Operations Research/Operations Management. His work was awarded first place in the 2019 INFORMS Nicholson Best Student Paper Competition. \n  \n\n\n\nName of Speaker\nDr Chen Yilun\n\n\nSchedule\nFriday 15 October\, 10am – 11.30am \n(60 min talk + 30 min Q&A)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZcrde-rrDMsHNRtAlSfk8uzRR1XMiPuCd-f\n\n\n Title\nTractability of high-dimensional online decision-making with limited action changes\n\n\nAbstract\nData-driven online decision-making tasks arise frequently in various practical settings in OR/ OM\, finance\, healthcare\, etc. Such tasks often boil down to solving certain stochastic dynamic programs (DPs) with prohibitively large state space and complicated dynamics\, suffering from the computational challenge known as the curse of dimensionality. Existing approaches (e.g. ADP\, deep learning) typically focus on achieving practical success and have limited performance guarantees. We propose an algorithm that overcomes this computational obstacle for a rich class of problems\, subject only to a “limited-action-change’’ constraint\, which incorporate important special cases including optimal stopping and limited-price-change dynamic pricing. Assuming a black-box simulator of the problem’s dynamics\, our algorithm can return epsilon-optimal policies with sample and computational complexity scaling polynomially in T (the time horizon)\, and effectively independent of the underlying state space\, analogous to a “PTAS’’. We further demonstrate that the limited-action-change constraint is crucial for such efficient algorithms to exist through the construction of a hard instance. The recent algorithmic progress in high-dimensional optimal stopping of Chen and Goldberg 21 is a key building block of our algorithm.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-yilun-chen/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/08/Chen-Yilun-pic.jpg
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BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211020T100000
DTEND;TZID=Asia/Singapore:20211020T110000
DTSTAMP:20260405T104532
CREATED:20211014T072811Z
LAST-MODIFIED:20211014T072811Z
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SUMMARY:PhD Oral Defense: Tan Hong Ming
DESCRIPTION:Hong Ming is a final year PhD candidate at the Institute of Operations Research and Analytics\, National University of Singapore. His research interests include stochastic modelling\, information economics\, and decision making under uncertainty. He has won teaching awards and was previously a lecturer at Temasek Polytechnic where he was subject leader and also co-authored three textbooks. \n  \n\n\n\nName of Speaker\nTan Hong Ming\n\n\nSchedule\n20 October 2021\, 10am\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZAldeGqqjIqHN3Ww-3WJ8rbHo9m5QGIMuP-\n\n\n Title\nRisky Investments under Static and Dynamic Information Acquisition\n\n\nAbstract\nThis thesis studies risky investments under different information acquisition processes. Under a dynamic information acquisition process\, the investor’s unconditional expected optimal quantity of information and investment amount are higher than those under the corresponding static information acquisition process. However\, when the initial belief of the investment payoff is either high or low\, static and dynamic information acquisitions provide similar expected results. To incentivize firms to acquire information and invest when the initial belief of a risky investment payoff is mediocre\, governments should allow firms to obtain information dynamically. This has implications on\, e.g.\, clinical trials. Furthermore\, the falling marginal cost of information raises investment amounts and leverage\, which leads to higher losses during crises. Hence\, companies need to understand their tail risks better.
URL:https://iora.nus.edu.sg/events/phd-oral-defense-tan-hong-ming/
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211022T100000
DTEND;TZID=Asia/Singapore:20211022T113000
DTSTAMP:20260405T104532
CREATED:20210812T025154Z
LAST-MODIFIED:20211015T034901Z
UID:14194-1634896800-1634902200@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Kimon Drakopoulos
DESCRIPTION:Kimon Drakopoulos is an Assistant Professor of Data Sciences and Operations at USC Marshall School of Business\, where he researches complex networked systems\, control of contagion\, information design and information economics. During the Summer of 2020 he served as the Chief Data Scientist of the Greek National COVID-19 Taskforce and Data Science and Operations Advisor to the Greek Prime Minister.  He completed his Ph.D. in the Laboratory for Information and Decision Systems at MIT\, focusing on the analysis and control of epidemics within networks. His current research revolves around controlling contagion\, epidemic or informational as well as the use of information as a lever to improve operational outcomes in the context of testing allocation\, fake news propagation and belief polarization. \n  \n\n\n\nName of Speaker\nKimon Drakopoulos\n\n\nSchedule\n22 October 2021\, 10am – 11.30am \n(60 min talk + 30 min Q&A)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZEqdeqqqzwiEtTecjPommMjKlLsJKr4UArz\n\n\n Title\nWhy Perfect Tests May Not be Worth Waiting For: Information as a Commodity\n\n\nAbstract\nInformation products provide agents with additional information that is used to update their actions. In many situations\, access to such products can be quite limited. For instance\, in epidemics\, there tends to be a limited supply of medical testing kits or tests. These tests are information products because their output of a positive or a negative answer informs individuals and authorities on the underlying state and the appropriate course of action. In this paper\, using an analytical model\, we show how the accuracy of a test in detecting the underlying state affects the demand for the information product differentially across heterogeneous agents. Correspondingly\, the test accuracy can serve as a rationing device to ensure that the limited supply of information products is appropriately allocated to the heterogeneous agents. When test availability is low and the social planner is unable to allocate tests in a targeted manner to the agents\, we find that moderately good tests can outperform perfect tests in terms of social outcome. On the policy side\, we use a numerical study of an evolving epidemic to confirm our theoretically derived insight that in the early stages of an epidemic with low test availability\, releasing a moderately good test can be an optimal strategy. The work is forthcoming in Management Science (FastTrack).\n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-kimon-drakopoulos/
CATEGORIES:IORA Seminar Series
ATTACH;FMTTYPE=image/jpeg:https://iora.nus.edu.sg/wp-content/uploads/2021/08/Kimon-D-pic.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Singapore:20211029T100000
DTEND;TZID=Asia/Singapore:20211029T113000
DTSTAMP:20260405T104532
CREATED:20210812T025426Z
LAST-MODIFIED:20211021T032942Z
UID:14196-1635501600-1635507000@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Junyu Zhang
DESCRIPTION:Junyu Zhang is an assistant professor at the Department of Industrial Systems Engineering and Management (ISEM)\, National University of Singapore. Before joining NUS\, he was a postdoctoral research fellow at Princeton University\, Department of Electrical and Computer Engineering. He received his Ph.D. degree from University of Minnesota\, Twin Cities\, in 2020. And he received his B.Sc. degree from Peking University in 2015. He is currently focusing on the convergence and complexity of various stochastic optimization and saddle point problems\, as well as their closed related problems in reinforcement learning. \n  \n\n\n\nName of Speaker\nDr Zhang Junyu\n\n\nSchedule\n29 October 2021\, 10am – 11.30am \n(60 min talk + 30 min Q&A)\n\n\nLink to Register\nhttps://nus-sg.zoom.us/meeting/register/tZYuc-yqqT4tGN3bunp6tZ9-6SjY3JFy4lYq\n\n\n Title\nPrimal-Dual First-Order Methods for Affinely Constrained Multi-Block Saddle Point Problems\n\n\nAbstract\nWe consider the convex-concave saddle point problems where the decision variables are subject to certain multi-block structure and affine coupling constraints\, and the objective function possesses a certain separable structure. Although the minimization counterpart of such a problem has been widely studied under the topics of ADMM\, this minimax problem is rarely investigated. In this paper\, a convenient notion of $\epsilon$-saddle point is proposed\, under which the convergence rate of several proposed algorithms are analyzed. When only one of the decision variables has multiple blocks and affine constraints\, several natural extensions of ADMM are proposed to solve the problem. Depending on the number of blocks and the level of smoothness\, O(1/T) or O(1/\sqrt{T}) convergence rates are derived for our algorithms. When both decision variables have multiple blocks and affine constraints\, a new algorithm called ExtraGradient Method of Multipliers (EGMM) is proposed. Under desirable smoothness conditions\, an O(1/T) rate of convergence can be guaranteed regardless of the number of blocks in the decision variables. An in-depth comparison between EGMM (fully primal-dual method) and ADMM (approximate dual method) is made over the multi-block optimization problems to illustrate the benefits of the EGMM.
URL:https://iora.nus.edu.sg/events/iora-seminar-series-junyu-zhang/
CATEGORIES:IORA Seminar Series
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