Past CompSust Open Graduate Seminar (COGS)
The COGS will focus on disseminating work of graduate students in the computational sustainability network. The format will be short (~30 minute) presentations with plenty of time for open discussion. All are welcome to attend. The series is sponsored by CompSustNet, with support from the National Science Foundation's Expeditions in Computing program.
The COGS Program Committee includes: Sebastian Ament (Cornell), Nirma Dolatnia (OSU), Priya Donti (CMU), Amrita Gupta (GATech), Neal Jean (Stanford), Bryan Wilder (USC), and Kevin Winner (UMass).
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 Fall 2018 listing.
See also the Computational Sustainability Virtual Seminar Series.
|Fri Apr 20, 2018, 1:30-2:30pm EDT (UTC-4)||Yexiang Xue, Cornell University||Avicaching: a Two-stage Game for Bias Reduction in Citizen Science|
|Fri May 4, 2018, 1:30-2:30pm EDT (UTC-4)||Haifeng Xu, University of Southern California||Strategic Coordination of Human Patrollers and UAVs with Signaling for Security Games|
|Fri Jun 1, 2018, 1:30-2:30pm EDT (UTC-4)||Kevin Winner, University of Massachusetts Amherst||Inference and Learning with Generating Functions for Models of Population Dynamics|
2018 Spring COGS Details
Yexiang Xue, Cornell University
Fri Apr 20, 2018, 1:30-2:30pm EDT (UTC-4)
Title: Avicaching: a Two-stage Game for Bias Reduction in Citizen Science
Abstract: In this talk, I will discuss how game theory can help reduce the data bias problem in citizen science. Citizen science projects have been very successful at collecting rich datasets across different domains. However, the data collected by the citizen scientists are often biased, aligned more directly with the participants' preferences rather than scientific objectives. We introduce a general methodology to improve the scientific quality of the data collected by citizen scientists. Our approach uses incentives to shift the interests of citizen scientists to be more aligned with the goal of obtaining unbiased samples from the field, thus improving the quality of the data collected. We formulate the problem using the so-called Principal-Agent framework, which requires an integration of learning, to obtain the parameters that govern the individual behavior of the citizen scientists (the agents), with reasoning, to search for an optimal incentive allocation to achieve the goal of the principal (the organizer of the citizen science program). We apply our methodology to eBird, a well-established citizen science program of the Cornell Lab of Ornithology for the collection of bird observations, as a game-based web application, called Avicaching. Our field results show that our Avicaching incentives are remarkably effective at steering the bird watchers' efforts to explore under-sampled areas and hence alleviate the data bias problem in eBird. At the end of my talk, I will briefly discuss how the Institute of Computational Sustainability enables me to develop this fruitful line of multidisciplinary research, collaborating with wonderful domain experts.
This is joint work with Ian Davies, Daniel Fink and Christopher Wood from the Cornell Lab of Ornithology and Carla P. Gomes from the Department of Computer Science, Cornell University.
Bio: Yexiang Xue is a Ph.D. candidate in the Department of Computer Science at Cornell University, working with Professors Carla Gomes and Bart Selman. Upon graduation, he will join Purdue University as an assistant professor in computer science starting Fall 2018. His research aims at combining large-scale constraint-based reasoning and optimization with state-of-the-art machine learning techniques to enable intelligent agents to make optimal decisions in high-dimensional and uncertain real-world applications. More specifically, his research focuses on scalable and accurate probabilistic reasoning techniques, statistical modeling of data, and robust decision-making under uncertainty. Mr. Xue's work is motivated by key problems across multiple scientific domains, including artificial intelligence, machine learning, renewable energy, materials science, citizen science, urban computing, and ecology, with an emphasis on developing cross-cutting computational methods for applications in the areas of computational sustainability and scientific discovery. Mr. Xue's work received the Innovative Application Award at IAAI-17 and was featured as the cover article and the Editor's Choice in the journal Combinatorial Science of the American Chemical Society.
Haifeng Xu, University of Southern California
Fri May 4, 2018, 1:30-2:30pm EDT (UTC-4)
Title: Strategic Coordination of Human Patrollers and UAVs with Signaling for Security Games
Abstract: The past decade has seen significant interest of using game theory to model the strategic interactions between defenders and attackers (a.k.a., security games). This has also led to the deployment of optimized defender strategies in several real-world applications including, e.g., patrol route design for wildlife conservation. Most of these works consider only the allocation of human patrollers. In this talk, I will discuss a different problem that concerns the optimal coordination of human patrollers and mobile sensors (e.g., UAVs) for better defense. This is partially motivated by the rapidly growing recent trend in using UAVs to combat poaching. I will explain the key differences between human patrollers and UAVs as security resources, and show how to improve defense by empowering human patrollers with the most natural two functionalities of UAVs: monitoring and strategic signaling.
Bio: Haifeng Xu is a PhD candidate in the Computer Science Department at the University of Southern California, advised by Milind Tambe and Shaddin Dughmi. His research interests include artificial intelligence, computational game theory, algorithms, and applied machine learning. He focuses on developing theoretically grounded approaches that also have real-world impact. Haifeng is a recipient of the 2017 Google PhD fellowship and the 2017 USC CAMS prize for excellence in research. His work has received the 2016 AAMAS best student paper award and the 2016 SecMas Workshop best paper award.
Kevin Winner, University of Massachusetts Amherst
Fri Jun 1, 2018, 1:30-2:30pm EDT (UTC-4)
Title: Inference and Learning with Generating Functions for Models of Population Dynamics
Abstract: Graphical models with integer-valued latent variables appear in numerous applications, but they are particularly ubiquitous when modeling the growth and decay of partially observed populations over time. However, typical inference and learning algorithms for discrete latent variable models fail when the support of the latent variables is unbounded, as it is in our scenario. Tractable approximate algorithms exist, but they require a priori assumptions about the population size and don't provide any theoretical results about the degree of the approximation, meaning it can be difficult to tune these assumptions or to detect when they fail.
By comparison, our new family of inference and learning algorithms based on generating functions are capable of solving these problems faster than existing techniques and are the first to do so exactly. The approach has two key ideas: 1) to adopt a representation of probability distributions based on probability generating functions, and 2) to use ideas from forward mode automatic differentiation (autodiff) to evaluate the generating functions. We have focused on the application of these concepts to modeling population dynamics, but the core concepts are broadly applicable to any model with unbounded integer latent variables (for idea #1) or to any function evaluation problem requiring nested derivatives (for idea #2).
In this talk, I will present the foundations of PGF-based inference and learning, including how to adapt your favorite inference/learning algorithm to use a generating function representation and how to evaluate the resultant complex recursive functions using high-order nested autodiff. I will also present some of our ongoing and planned work in this domain. The talk will run about 40 minutes, with an extended 20 minutes question and discussion segment following the talk.
We are also planning an upcoming code release, so if you'd like more information or to get an early release of the package, please contact me!