Past 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 email@example.com with the word join as the subject (leave the message body empty).
For current seminar information, please see the Spring 2017 listing.
2016 Fall Seminar Details
Daniel Sheldon, University of Massachusetts Amherst and Mount Holyoke College
Thu Dec 15, 2016, 4-5pm EST (UTC-5)
Title: Advances in Probabilistic Inference and Machine Learning for Ecosystem Monitoring
Abstract: Machine learning combined with large and novel data resources can contribute to our understanding of ecosystems in a variety of ways. This talk will describe two different applications of machine learning to ecosystem monitoring. First, I will describe our ongoing work to measure continent-scale bird migration using archived weather radar data. Machine learning algorithms automate the complex process of interpreting radar imagery and allow us to access high-level biological information in this massive data archive. Second, I will describe advances in probabilistic inference for estimating animal population parameters from survey data. We present the first exact polynomial-time inference algorithms for a class of commonly used models that include latent count variables to represent unknown population sizes. Our approach uses probability generating functions to represent and manipulate the infinite sequences that one must reason about during inference, and is much faster than existing approximate approaches.
Bio: Daniel Sheldon is an Assistant Professor of Computer Science at the University of Massachusetts Amherst and Mount Holyoke College. He received his Ph.D. from the Department of Computer Science at Cornell University in 2009, and was an NSF Postdoctoral Fellow in Bioinformatics at the School of EECS at Oregon State University from 2010-2012. His research interests are in machine learning, probabilistic modeling, and optimization applied to large-scale problems in ecology, computational sustainability, and networks. His work was recognized by a Computational Sustainability Best Paper Award at AAAI 2013, and is supported by the NSF and MassDOT.
Warren B. Powell, Princeton University
Tue Nov 29, 2016, 4-5pm EST (UTC-5)
Title: A Unified Framework for Handling Decisions and Uncertainty In Energy and Sustainability
Abstract: Problems in energy and sustainability represent a rich mixture of decisions intermingled with different forms of uncertainty. These decision problems have been addressed by multiple communities from operations research (stochastic programming, Markov decision processes, simulation optimization, decision analysis), computer science, optimal control (from engineering and economics), and applied mathematics. In this talk, I will identify the major dimensions of this rich class of problems, spanning static to fully sequential problems, offline and online learning (including so-called "bandit" problems), derivative-free and derivative-based algorithms, with attention given to problems with expensive function evaluations. We divide solution strategies for sequential problems ("dynamic programs") between stochastic search ("policy search") and policies based on lookahead approximations (which include both stochastic programming as well as value functions based on Bellman's equations). We further divide each of these two fundamental solution approaches into two subclasses, producing four classes of policies for approaching sequential stochastic optimization problems. We use a simple energy storage problem to demonstrate that each of these four classes may work best, as well as opening the door to a range of hybrid policies. I will show that a single elegant framework spans all of these approaches, providing scientists with a more comprehensive toolbox for approaching the rich problems that arise in energy and sustainability.
Bio: Warren B. Powell is a professor in the Department of Operations Research and Financial Engineering at Princeton University, where he has taught since 1981 after receiving his BSE from Princeton University and Ph.D. from MIT. He is the founder and director of the laboratory for Computational Stochastic Optimization and Learning (CASTLE Labs), which spans contributions to models and algorithms in stochastic optimization, with applications to energy systems, health and medical research, and the sciences. He has two books and over 200 papers, and is working on a new book "Optimization under Uncertainty: A Unified Framework."
Bistra Dilkina, Georgia Institute of Technology
Tue Nov 8, 2016, 4-5pm EST (UTC-5)
Title: Network Design Approaches to Multi-species Biodiversity Conservation
Abstract: Curbing biodiversity loss is one of the key goals in achieving sustainable development. However, most conservation investments are done with limited budget, and in the face complex spatial variations in economic costs and ecological benefits. I address several hard spatial optimization problems that arise in the context of conservation planning, and show how network design and mixed-integer optimization can be leveraged for finding solutions and supporting effective and cost-efficient decision making. I will present a computational framework for finding optimal wildlife corridors serving multiple species. Our framework enables the systematically study of tradeoffs between economic costs and conservation benefits, tradeoffs between single-species and multi-species planning, as well as tradeoffs with respect to species prioritization. We apply our approach in western Montana to the conservation of grizzly bears and wolverines, and demonstrate economies of scale and complementarities conservation planners can achieve by optimizing corridor designs for financial costs and for multiple species connectivity jointly.
Bio: Bistra Dilkina is an assistant professor in the College of Computing at the Georgia Institute of Technology. She received her PhD from Cornell University in 2012, and was a Post-Doctoral associate at the Institute for Computational Sustainability until 2013. Her research focuses on advancing the state of the art in combinatorial optimization techniques for solving real-world large-scale problems, particularly ones that arise in sustainability areas such as biodiversity conservation planning and urban planning. Her work spans discrete optimization, network design, stochastic optimization, and machine learning. She is also the co-director of the Data Science for Social Good (DSSG) Atlanta summer program, which partners student teams with government and nonprofit organizations to help solve some of their most difficult problems using analytics, modeling, prediction and visualization.
Milind Tambe and Eric Rice, University of Southern California
Tue Oct 25, 2016, 4-5pm EDT (UTC-4)
Title: How Can AI be Used for Social Good? Key Techniques, Applications, and Results
Abstract: Discussions about the future negative consequences of AI sometimes drown out discussions of the current accomplishments and future potential of AI in helping us solve complex societal problems. At the USC Center for AI in Society, CAIS, our focus is on exploring AI research in tackling wicked problems in society. This talk will highlight the goals of CAIS and three areas of ongoing work. First, we will focus on the use of AI for assisting low-resource sections of society, such as homeless youth. Harnessing the social networks of such youth, we will illustrate the use of AI algorithms to help more effectively spread health information, such as for reducing risk of HIV infections. These algorithms have been piloted in homeless shelters in Los Angeles, and have shown significant improvements over traditional methods. Second, we will outline the use of AI for protection of forests, fish, and wildlife; learning models of adversary behavior allows us to predict poaching activities and plan effective patrols to deter them. These algorithms are in use in multiple countries, and we discuss concrete results we have obtained in a national park in Uganda. Finally, we will focus on the challenge of AI for public safety and security, discussing game theoretic algorithms for effective security resource allocation that are in actual daily use by agencies such as the US Coast Guard and the Federal Air Marshals Service to assist the protection of ports, airports, flights, and other critical infrastructure. These are just a few of the projects at CAIS, and we expect these and future projects at CAIS to continue to illustrate the significant potential that AI has for social good.
Bio: Milind Tambe is Founding Co-Director of CAIS, the USC Center for AI for Society, and Helen N. and Emmett H. Jones Professor in Engineering at the University of Southern California(USC). He is a fellow of AAAI and ACM, as well as recipient of the ACM/SIGART Autonomous Agents Research Award, Christopher Columbus Fellowship Foundation Homeland security award, INFORMS Wagner prize for excellence in Operations Research practice, Rist Prize of the Military Operations Research Society, IBM Faculty Award, Okawa foundation faculty research award, RoboCup scientific challenge award, and other local awards such as the Orange County Engineering Council Outstanding Project Achievement Award, USC Associates award for creativity in research and USC Viterbi use-inspired research award. Prof. Tambe has contributed several foundational papers in AI in areas such as multiagent teamwork, distributed constraint optimization (DCOP) and security games. For this research, he has received the "influential paper award" and a number of best paper awards at conferences such as AAMAS, IJCAI, IAAI and IVA. In addition, Prof. Tambe pioneering real-world deployments of "security games" has led him and his team to receive the US Coast Guard Meritorious Team Commendation from the Commandant, US Coast Guard First District's Operational Excellence Award, Certificate of Appreciation from the US Federal Air Marshals Service and special commendation given by LA Airport police from the city of Los Angeles. For his teaching and service, Prof. Tambe has received the USC Steven B. Sample Teaching and Mentoring award and the ACM recognition of service award. He has also co-founded a company based on his research, Avata Intelligence, where he serves as the director of research. Prof. Tambe received his Ph.D. from the School of Computer Science at Carnegie Mellon University.
Thomas Dietterich, Oregon State University
Tue Oct 11, 2016, 4-5pm EDT (UTC-4)
Title: Solving MDPs for Ecosystem Management: Lessons Learned
Abstract: Many ecosystem management problems can be formulated as MDPs in which the transition dynamics of the ecosystem is defined by a simulator (as opposed to a probability transition matrix). We have been studying two ecosystem management problems: wildfire management in ponderosa pine forests and tamarisk invasion of river networks. This talk will describe the methods we have developed and the lessons we have learned while working on these problems. For small simulator-defined MDPs, we have developed efficient algorithms that provide probabilistic (PAC) accuracy guarantees. For larger problems, we have found success using Bayesian optimization methods to search a parameterized policy space. The talk will conclude with a set of open research questions.
Bio: Dr. Dietterich (AB Oberlin College 1977; MS University of Illinois 1979; PhD Stanford University 1984) is Professor Emeritus and Director of Intelligent Systems Research in the School of Electrical Engineering and Computer Science at Oregon State University, where he joined the faculty in 1985. Dietterich is one of the pioneers of the field of Machine Learning and has authored more than 130 refereed publications and two books. His research is motivated by challenging real world problems with a special focus on ecological science, ecosystem management, and sustainable development. He is best known for his work on ensemble methods in machine learning including the development of error-correcting output coding. Dietterich has also invented important reinforcement learning algorithms including the MAXQ method for hierarchical reinforcement learning.
Stefano Ermon, Stanford University
Tue Sep 27, 2016, 4-5pm EDT (UTC-4)
Title: Measuring progress towards sustainable development goals with machine learning
Abstract: Recent technological developments are creating new spatio-temporal data streams that contain a wealth of information relevant to sustainable development goals. Modern AI techniques have the potential to yield accurate, inexpensive, and highly scalable models to inform research and policy. As a first example, I will present a machine learning method we developed to predict and map poverty in developing countries. Our method can reliably predict economic well-being using only high-resolution satellite imagery. Because images are passively collected in every corner of the world, our method can provide timely and accurate measurements in a very scalable end economic way, and could revolutionize efforts towards global poverty eradication. As a second example, I will present some ongoing work on monitoring agricultural and food security outcomes from space.
Bio: Stefano Ermon is currently an Assistant Professor in the Department of Computer Science at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory and the Woods Institute for the Environment. He completed his PhD in computer science at Cornell in 2015. His research interests include techniques for scalable and accurate inference in graphical models, statistical modeling of data, large-scale combinatorial optimization, and robust decision making under uncertainty, and is motivated by a range of applications, in particular ones in the emerging field of computational sustainability. Stefano has won several awards, including two Best Student Paper Awards, one Runner-Up Prize, and a McMullen Fellowship.