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X-WR-CALDESC:Events for IORA - Institute of Operations Research and Analytics
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DTSTART;TZID=Asia/Singapore:20230207T100000
DTEND;TZID=Asia/Singapore:20230207T113000
DTSTAMP:20260420T015216
CREATED:20230110T031633Z
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SUMMARY:IORA Seminar Series - Timothy Chan
DESCRIPTION:Timothy Chan is the Associate Vice-President and Vice-Provost\, Strategic Initiatives at the University of Toronto\, the Canada Research Chair in Novel Optimization and Analytics in Health\, a Professor in the department of Mechanical and Industrial Engineering\, and a Senior Fellow of Massey College. His primary research interests are in operations research\, optimization\, and applied machine learning\, with applications in healthcare\, medicine\, sustainability\, and sports. He received his B.Sc. in Applied Mathematics from the University of British Columbia (2002)\, and his Ph.D. in Operations Research from the Massachusetts Institute of Technology (2007). Before coming to Toronto\, he was an Associate in the Chicago office of McKinsey and Company (2007-2009)\, a global management consulting firm. During that time\, he advised leading companies in the fields of medical device technology\, travel and hospitality\, telecommunications\, and energy on issues of strategy\, organization\, technology and operations. \n  \n\n\n\nName of Speaker\nTimothy Chan\n\n\nSchedule\nTuesday 7 February 2023\, 10am – 1130am\n\n\nVenue \nBIZ2 4-13A (BIZ 2\, level 4 Seminar Room)\n\n\nLink to Register \n(Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZcpdO2tpz8jEt3qhGZYuJ2GhZEuzV8r3skw\n\n\nTitle\nGot (optimal) milk?\n\n\nAbstract\nHuman donor milk is considered the ideal nutrition for millions of infants that are born preterm each year. Donor milk is collected\, processed\, and distributed by milk banks. The macronutrient content of donor milk is directly linked to infant brain development and can vary substantially across donations\, which is why multiple donations are typically pooled together to create a final product. Approximately half of all milk banks in North America do not have the resources to measure the macronutrient content of donor milk\, which means pooling is done heuristically. We propose a data-driven framework combining machine learning and optimization to predict macronutrient content of deposits and then optimally combine them in pools\, respectively. In collaboration with our partner milk bank\, we collect a data set of milk to train our predictive models. We rigorously simulate milk bank practices to fine-tune our optimization models and evaluate operational scenarios such as changes in donation habits during the COVID-19 pandemic. Finally\, we conduct a year-long trial implementation\, where we observe the current nurse-led pooling practices followed by our intervention. Pools created by our approach meet clinical macronutrient targets between 31% to 76% more often than the baseline\, while taking 67% less recipe creation time. This is the first paper in the broader blending literature that combines machine learning and optimization. We demonstrate that such pipelines are feasible to implement and can yield significant improvements over current practices. Our insights can guide practitioners in any application area seeking to implement machine learning and optimization-based decision-support.\n\n\n\n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-timothy-chan/
CATEGORIES:IORA Seminar Series
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DTSTART;TZID=Asia/Singapore:20230210T100000
DTEND;TZID=Asia/Singapore:20230210T113000
DTSTAMP:20260420T015216
CREATED:20230110T031729Z
LAST-MODIFIED:20230213T021652Z
UID:16650-1676023200-1676028600@iora.nus.edu.sg
SUMMARY:IORA Seminar Series – Antoine Désir
DESCRIPTION:Antoine is an Assistant professor of Technology and Operations Management at INSEAD. His research applies mathematical modeling and analytics to operations management problems with an aim to: (1) quantify fundamental tradeoffs\, and (2) design efficient data-driven algorithms to support operational decisions. More precisely\, he focuses on revenue management and choice modeling with applications such as online advertising. He was an MSOM student paper finalist in 2014 and 2017 and a Nicholson student paper finalist in 2014 and 2015. He spent a year as a post-doctoral researcher at Google NYC. \n\n\n\nName of Speaker\nAntoine Desir\n\n\nSchedule\nFriday 10 February 2023\, 10.00am – 11.30am\n\n\nVenue \nI4-01-03 (Innovation 4.0\, level 1 Seminar Room)\n\n\nLink to Register \n(Via Zoom)\nhttps://nus-sg.zoom.us/meeting/register/tZwtdOyoqjIuGtIP444shDdv-KQrbeOQcFl3\n\n\nTitle\nRepresenting Random Utility Choice Models with Neural Networks\n\n\nAbstract\nMotivated by the successes of deep learning\, we propose a class of neural network-based discrete choice models\, called RUMnets\, which is inspired by the random utility maximization (RUM) framework. This model formulates the agents’ random utility function using the sample average approximation (SAA) method. We show that RUMnets sharply approximate the class of RUM discrete choice models: any model derived from random utility maximization has choice probabilities that can be approximated arbitrarily closely by a RUMnet. Reciprocally\, any RUMnet is consistent with the RUM principle. We derive an upper bound on the generalization error of RUMnets fitted on choice data\, and gain theoretical insights on their ability to predict choices on new\, unseen data depending on critical parameters of the dataset and architecture.  By leveraging open-source libraries for neural networks\, we find that RUMnets outperform other state-of-the-art choice modeling and machine learning methods by a significant margin on two real-world datasets. This is joint work with Ali Aouad (LBS). \n 
URL:https://iora.nus.edu.sg/events/iora-seminar-series-antoine-desir/
CATEGORIES:IORA Seminar Series
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