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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 with the word join as the subject (leave the message body empty).

For information about previous semesters, please see the past seminars list.

Seminar Schedule

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
Fri Apr 28, 2017, 1:30-2:30pm EDT (UTC-4) Mary Lou Zeeman, Bowdoin College Modeling Challenges in the Food System
Fri May 5, 2017, 1:30-2:30pm EDT (UTC-4) Fei Fang, Harvard University Game-Theoretic Approaches for Sustainability Challenges
Fri May 19, 2017, 1:30-2:30pm EDT (UTC-4) Angela Fuller, U.S. Geological Survey, Cornell University Density-Weighted Connectivity for Landscape Management and Connectivity Conservation
Fri June 2, 2017, 1:30-2:30pm EDT (UTC-4) Doug Fisher, Vanderbilt University A survey of CompSustNet research, education, and outreach
Fri Jun 16, 2017, 1:30-2:30pm EDT (UTC-4) Alan Fern, Oregon State University Bringing Bayesian Optimization into the Lab: Reasoning about Resources and Actions

Seminar Details

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.

Mary Lou Zeeman, Bowdoin College

Fri Apr 28, 2017, 1:30-2:30pm EDT (UTC-4)

Title: Modeling Challenges in the Food System

Abstract: We will describe some challenges in food systems and in resilience that might lend themselves to a collaborative approach between computer scientists, mathematicians and domain scientists. We will also briefly discuss some of the mechanisms that the Mathematics and Climate Research Network has found helpful in creating and sustaining geographically dispersed online working groups and mentoring teams.

Bio: Dr. Mary Lou Zeeman uses mathematics within cross-disciplinary research communities to help understand sustainability, climate change, and protecting the health of the planet. Zeeman is a co-director of the Mathematics and Climate Research Network, a member of the executive council of the Computational Sustainability Network, and a co-leader of the Mathematics of Planet Earth Initiative. She is a professor of Mathematics at Bowdoin College.

Fei Fang, Harvard University

Fri May 5, 2017, 1:30-2:30pm EDT (UTC-4)

Title: Game-Theoretic Approaches for Sustainability Challenges

Abstract: The framework of game theory can be powerful when addressing resource allocation problems in security and sustainability domains, e.g., protecting critical infrastructure and cyber network, and protecting wildlife, fishery, and forest. Motivated by these problems, I propose models and algorithms to handle massive games with complex spatio-temporal settings, leading to real-world applications that have fundamentally altered current practices of security resource allocation. In this talk, I will focus on my work motivated by environmental sustainability challenges. First, for problems with repeated interaction such as preventing poaching and illegal fishing, I introduce the green security game model which accounts for adversaries' behavior change and provide algorithms to plan effective sequential defender strategies. Second, I incorporate complex terrain information and design PAWS (Protection Assistant for Wildlife Security) which generates patrol routes to combat poaching. PAWS has been deployed in Southeast Asia for tiger conservation. In addition, I will cover our recent work on adversary behavior modeling and forecasting with real-world poaching data.

Bio: Fei Fang is a Postdoctoral Fellow at the Center for Research on Computation and Society (CRCS), Harvard University and an Adjunct Assistant Professor at the Institute for Software Research at Carnegie Mellon University. She received her Ph.D. from the Department of Computer Science at the University of Southern California in June 2016, advised by Professor Milind Tambe. She received her bachelor degree from the Department of Electronic Engineering, Tsinghua University in July 2011. Her research lies in the field of artificial intelligence and multi-agent systems, focusing on computational game theory with applications to security and sustainability domains. Her dissertation is selected as the runner-up for IFAAMAS-16 Victor Lesser Distinguished Dissertation Award. Her work has won the Innovative Application Award at Innovative Applications of Artificial Intelligence (IAAI'16), the Outstanding Paper Award in Computational Sustainability Track at the International Joint Conferences on Artificial Intelligence (IJCAI'15). Her work on "Protecting Moving Targets with Mobile Resources" has been deployed by the US Coast Guard for protecting the Staten Island Ferry in New York City since April 2013. Her work on designing patrol strategies to combat illegal poaching has lead to the deployment of PAWS application in a conservation area in Southeast Asia for protecting tigers.

Angela Fuller, U.S. Geological Survey, Cornell University

Fri May 19, 2017, 1:30-2:30pm EDT (UTC-4)

Title: Density-Weighted Connectivity for Landscape Management and Connectivity Conservation

Abstract: Many conservation efforts are focused on maintaining connectivity of protected areas or reserves as a biodiversity or species conservation strategy. The intended purpose of such corridors is to provide regions of the landscape that facilitate movement of individuals. Specific objectives include increasing gene flow, reducing isolation and inbreeding, increasing fitness and survival of species, and allowing species to move and adapt to changes in the landscape. Corridor conservation typically focuses on either 1) conserving areas that support high abundance of species to reduce the risk of demographic stochasticity or 2) conserving areas that allow individuals to move between reserve areas to maintain gene flow. Most corridor design applications focus on patterns of habitat and landscape structure (structural connectivity). However, the impetus of corridor design is the process of animal movement (functional connectivity). Functional connectivity considers the degree to which the landscape facilitates or impedes the movement of organisms and is the product of landscape structure and the response of organisms to this structure. However, maintenance of spatially structured populations requires considerations of both species abundance as well as functional landscape connectivity. I present a model for corridor design in the Chocó-Andean region of Ecuador, home to the endangered Andean bear (Tremarctos ornatus) and numerous endemic and threatened birds and describe a novel metric related to biodiversity conservation and corridor design. We use the ecological distance-based spatial capture-recapture model that simultaneously estimates species density and spatial aspects of animal population structure. The density-weighted connectivity metric is derived from encounter history data commonly collected in capture-recapture studies. I highlight how this metric can be used in reserve design or landscape management frameworks to inform conservation decision making.

Bio: Angela Fuller is the Leader of the New York Cooperative Fish and Wildlife Research Unit and an Associate Professor at Cornell University. Angela's research focuses on applied conservation and management of mammals, specifically related to population dynamics and the influence of human-induced landscape changes on populations. The second major program area of her research is applying structured decision making and adaptive management for aiding natural resource management and policy decisions. Her recent work has focused on informing agency decision making for managed species such as black bears, white-tailed deer, wild turkeys, and fishers; designing resilient and sustainable landscapes that support human quality of life and conserve biodiversity, with a focus on endangered Andean bears in Ecuador; and developing new methods for sampling and monitoring wildlife populations such as black bear, moose, mink, and fisher.

Doug Fisher, Vanderbilt University

Fri June 2, 2017, 1:30-2:30pm EDT (UTC-4)

Title: A survey of CompSustNet research, education, and outreach

Abstract: The talk will survey research, education, and outreach in CompSustNet. There will be unintentional gaps in my coverage of CompSustNet activities in this "draft talk", so there will be opportunities for the audience to help fill in the gaps in my coverage. There will also be opportunities to suggest how CompSustNet, and computational sustainability generally, might grow by filling in real gaps in its current coverage of research, education, and outreach. I will also survey other large centers and networks in related spaces of computing and/or sustainability, which might also inform CompSustNet's plans.

Bio: Doug Fisher is an Associate Professor of Computer Science at Vanderbilt University. His research has spanned unsupervised and supervised machine learning for prediction and problem solving, cognitive modeling, and more recently computational creativity. He served as a Program Director at NSF from 2007 - 2010, and was responsible for areas of AI (to include ML, MAS, KR, Planning) and he was a primary representative on sustainability for CISE. He received a Director's award in 2010 for all these activities. See his experience at NSF summarized at: Doug has also worked in the online learning space, and was the founding Director of the Vanderbilt Institute for Digital Learning. He is the founding Faculty Director of Warren (residential) College at Vanderbilt University. Doug is the Director for Outreach, Education, Diversity, and Synthesis (OEDS) of CompSustNet.

Alan Fern, Oregon State University

Fri Jun 16, 2017, 1:30-2:30pm EDT (UTC-4)

Title: Bringing Bayesian Optimization into the Lab: Reasoning about Resources and Actions

Abstract: Bayesian optimization (BO) aims to optimize costly-to-evaluate functions by running a limited number of experiments that each evaluate the function at a selected input. Typical BO formulations assume that experiments are selected one at a time, or in fixed batches, and that experiments can be executed immediately upon request. This setup fails to capture many real-world domains where the execution of an experiment requires setup and preparation time. In this talk, we will present our research on extending the BO setting to incorporate these concerns. The result is a novel BO problem formulation that explicitly models the resources and actions needed to prepare and run experiments. Our algorithmic approach to this problem involves integrating BO principles with a Monte-Carlo tree search. A crucial ingredient is to exploit problem structure in order to design a heuristic function with approximation guarantees that can be used to effectively guide the search. Our experiments demonstrate the effectiveness of this approach and illustrate the more general promise of combining ideas from automated planning and BO.

Bio: Alan Fern is Professor of Computer Science and Associate Head of Research for the School of EECS at Oregon State University. He received his Ph.D. (2004) and M.S. (2000) in computer engineering from Purdue University, and his B.S. (1997) in electrical engineering from the University of Maine. He is an associate editor of the Machine Learning Journal, the Journal of Artificial Intelligence Research, and serves on the executive council of the International Conference on Automated Planning and Scheduling. His research interests span a range of topics in artificial intelligence, including machine learning and automated planning/control, with a particular interest in the intersection of those areas.