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).
See also the Computational Sustainability Virtual Seminar Series.
|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|
|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|
Yexiang Xue, Cornell University
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
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.