Event Category: Operations Research Seminar Series

2/24/22 | 4:15pm | E25-111         Ozan Candogan Associate Professor of Operations Management Chicago Booth Abstract: Information can be used as a natural lever to improve outcomes in many settings of operational interest. For instance, by appropriately sharing information (about a payoff relevant state) a retailer can influence the production decisions of…

12/2/21 | 4:15pm | E25-111         Omar Skali Lami ORC PhD Student MIT Abstract: Predictive analytics is at the core of many operations management problems, ranging from understanding a customer’s propensity to buy a product to a patient’s length of stay in the emergency department. This talk introduces the Extended Sampled Trees…

11/18/21 | 4:15pm | E25-111         Jelena Diakonikolas Assistant Professor University of Wisconsin Abstract: One of the most fundamental facts in unconstrained convex optimization is that every point with zero gradient is also a global function minimum. However, the problem of efficiently computing points with small gradients is significantly different from the problem…

11/4/21 | 1:00pm | E51-149         Francis Bach Researcher INRIA Abstract: We consider the global minimization of smooth functions based solely on function evaluations. Algorithms that achieve the optimal number of function evaluations for a given precision level typically rely on explicitly constructing an approximation of the function which is then minimized with…

10/7/21 | 4:15pm | E25-111         Elad Hazan Professor Princeton University Abstract: In this talk we will discuss an emerging paradigm in differentiable reinforcement learning called “nonstochastic control”. The new approach applies techniques from online convex optimization and convex relaxations to obtain new methods with provable guarantees for classical settings in optimal and…

9/30/21 | 4:15pm | E25-111         David Shmoys Professor Cornell University Abstract: The American winner-take-all congressional district system empowers politicians to engineer electoral outcomes by manipulating district boundaries. To date, computational solutions mostly focus on drawing unbiased maps by ignoring political and demographic input, and instead simply optimize for compactness and other…

9/23/21 | 4:15pm | E51-149         Stephen Bates Postdoctoral Researcher University of California, Berkeley Abstract: While improving prediction accuracy has been the focus of machine learning in recent years, this alone does not suffice for reliable decision-making. Deploying predictive models in consequential settings also requires analyzing and communicating their uncertainty. To give…

9/9/21 | 4:15pm | E51-149         Hamsa Bastani Assistant Professor Wharton Abstract: A key aspect of human intelligence is their ability to convey their knowledge to others in succinct forms. However, despite their predictive power, current machine learning models are largely blackboxes, making it difficult for humans to extract useful insights. Focusing…

5/20/21 | 4:15pm | Online only         Jay Sethuraman Professor Columbia Abstract: Professor Sethuraman will discuss optimal stopping problems in which the player is free to choose the order of observation of the random variables as well as the stopping rule. Finding an optimal order is difficult, even for families of instances that look…

3/25/21 | 4:15pm | Online only         Nika Haghtalab Assistant Professor UC Berkeley Abstract: The widespread application of machine learning in practice necessitates the design of robust learning algorithms that can withstand unpredictable and even adversarial environments. The holy grail of robust algorithm design is to provide performance guarantees that do not…