Computational Sustainability Virtual Seminar Series
The Computational Sustainability Virtual Seminar Series will present talks by researchers and educators in Computational Sustainability, and is being sponsored by CompSustNet, with support from the National Science Foundation’s Expeditions in Computing program.
To sign up for seminar announcements, send an email to firstname.lastname@example.org with the word join as the subject (leave the message body empty).
For information about previous semesters, please see the past seminars list.
|Fri Mar 31, 2017, 1:30-2:30pm EDT (UTC-4)||TBA||TBA test|
|Fri Apr 14, 2017, 1:30-2:30pm EDT (UTC-4)||Alan Fern, Oregon State University||TBA|
|Fri Apr 28, 2017, 1:30-2:30pm EDT (UTC-4)||Mary Lou Zeeman, Bowdoin College||TBA|
|Fri May 19, 2017, 1:30-2:30pm EDT (UTC-4)||Angela Fuller, U.S. Geological Survey, Cornell University||TBA|
|Fri May 26, 2017, 1:30-2:30pm EDT (UTC-4)||Doug Fisher, Vanderbilt University||TBA||Past Talks|
|Fri Mar 3, 2017, 1:30-2:30pm EST (UTC-5)||Zico Kolter, Carnegie Mellon University||Task-based end-to-end learning in stochastic optimization|
|Fri Mar 17, 2017, 1:30-2:30pm EDT (UTC-4)||David Shmoys, Cornell University||Models and Algorithms for the Operation and Design of Bike-sharing Systems|
Zico Kolter, Carnegie Mellon University
Fri Mar 3, 2017, 1:30-2:30pm EST (UTC-5)
Title: Task-based end-to-end learning in stochastic optimization
Abstract: In this talk, I will present recent work in learning predictive models for use in stochastic optimization settings. In these domains, the goal of a probabilistic model is not merely to generate "accurate" predictions of the future, but also to make predictions that will result in an effective policy when integrated into decision-making processes. These two goals may seem well-aligned, but they often differ to a surprising degree when models do not reflect the true underlying system (the norm rather than the exception in machine learning). To address this challenge, we develop a technique we refer to as task-based end-to-end learning. The main idea is to update the predictive model itself through the task-specific loss, by differentiating through the policy decisions, in order to directly improve the policy performance under the true distribution of interest. This in turn requires techniques for differentiating through the solution of general optimization problems, a task for which we develop the algorithms and an efficient implementation. We apply this method to the task of scheduling generation under uncertain demand and ramping constraints, and shows that it can significantly outperform a naive maximum likelihood approach.
Bio: Zico Kolter is an Assistant Professor in the School of Computer Science at Carnegie Mellon University, with appointments in the Computer Science Department, the Institute for Software Research (in the Societal Computing program), and affiliated appointments with the Machine Learning Department, the Robotics Institute, and the Electrical and Computer Engineering Department. His work focuses on machine learning and optimization, with a specific focus on applications in smart energy systems. From an algorithmic standpoint, he has worked on fast optimization algorithms for a number of problems and for general convex programs, large-scale probabilistic modeling, stochastic optimization, and deep learning. On the application side, he has worked on energy disaggregation, probabilistic forecasting for energy systems, and model predictive control techniques for industrial control in the electrical grid.
David Shmoys, Cornell University
Fri Mar 17, 2017, 1:30-2:30pm EDT (UTC-4)
Title: Models and Algorithms for the Operation and Design of Bike-sharing Systems
Abstract: The sharing economy has helped to transform many aspects of our day-to-day lives, leveraging the IT revolution in increasingly novel ways. At the same time, the sharing economy presents new computational challenges to provide tools to support the operations of these emerging industries. Although perhaps not quite as visible in impact as Uber and Airbnb (and their competitors), bike-sharing systems have fundamentally changed the urban landscape as well. Even in a city as notoriously inhospitable to cycling as New York, Citibike has emerged as a significant player in the city's transportation network, supporting more than 1.5 million rides per month for a subscriber base of more than 100,000 individuals. We have been working with Citibike to develop analytics and optimization models and algorithms to help manage this system. The key challenge is to cope with huge rush-hour usage that simultaneously creates stark shortages of bikes in some neighborhoods, and surpluses of bikes (and consequently, shortages of parking docks) elsewhere. We will explain how mathematical models can be used to answer questions such as, how should we position the fleet of bikes at the start of a day, and how should we mitigate the imbalances that develop? We will survey the analytics we have employed for the former question, where we developed an approach based on continuous-time Markov chains combined with optimization models to compute daily stocking levels for the bikes, as well as methods employed for optimizing the capacity of the stations. For the question of mitigating the imbalances that result, we will describe algorithms that guide both mid-rush hour and overnight rebalancing, as well as for the positioning of corrals, which create "surge capacity" at stations, and have been one of the most effective means of creating adaptive capacity in the system.
This is a survey of several papers, but will focus on joint work with Daniel Freund, Shane Henderson, and Eoin O'Mahony.
Bio: David Shmoys is the Laibe/Acheson Professor at Cornell University in the School of Operations Research and Information Engineering, and also the Department of Computer Science at Cornell University, and is currently the Director of the School of Operations Research and Information Engineering. Shmoys's research has focused on the design and analysis of efficient algorithms for discrete optimization problems, with applications including scheduling, inventory theory, computational biology, computational sustainability, and most recently, on optimization models and algorithms for issues underlying the sharing economy. His graduate-level text, The Design of Approximation Algorithms, co-authored with David Williamson, was awarded the 2013 INFORMS Lanchester Prize. He is a Fellow of INFORMS, ACM, and SIAM. He has served on numerous editorial boards, having served as Editor-in-Chief of SIAM J. on Discrete Math, and Research in the Mathematical Sciences (for theoretical computer science), and is currently an Associate Editor of Mathematics of Operations Research.
Fri Mar 31, 2017, 1:30-2:30pm EDT (UTC-4)
Title: TBA test
Alan Fern, Oregon State University
Fri Apr 14, 2017, 1:30-2:30pm EDT (UTC-4)
Mary Lou Zeeman, Bowdoin College
Fri Apr 28, 2017, 1:30-2:30pm EDT (UTC-4)
Angela Fuller, U.S. Geological Survey, Cornell University
Fri May 19, 2017, 1:30-2:30pm EDT (UTC-4)
Doug Fisher, Vanderbilt University
Fri May 26, 2017, 1:30-2:30pm EDT (UTC-4)