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

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

See also the Computational Sustainability Virtual Seminar Series.

COGS Schedule

Fri Sep 21, 2018, 1:30-2:30pm EDT (UTC-4) Di Chen, Cornell University End-to-End learning for the Deep Multivariate Probit Model
Fri Oct 5, 2018, 1:30-2:30pm EDT (UTC-4) Priya L. Donti, Carnegie Mellon University Inverse Optimal Power Flow: Assessing the Vulnerability of Power Grid Data
Fri Oct 19, 2018, 1:30-2:30pm EDT (UTC-4) David Abel, Brown University Bandit Based Solar Panel Control
Fri Nov 2, 2018, 1:30-2:30pm EDT (UTC-4) Andrew Perrault, University of Southern California Experiential Preference Elicitation for Autonomous HVAC Systems

Seminar Details

Di Chen, Cornell University

Fri Sep 21, 2018, 1:30-2:30pm EDT (UTC-4)

Title: End-to-End learning for the Deep Multivariate Probit Model

Abstract: Understanding multi-entity interactions is a central question in many real-world applications. For example, in computational sustainability, it is important to understand the spatial distribution of species and how species interact with each other and their environment, for developing conservation plans. In computer vision, the detections of multiple objects are often correlated because of the shared background and scenario. In natural language processing, a text often has several correlated labels in terms of its topic, emotion, and semantic meaning.

The multivariate probit model (MVP) is a popular classic model for studying binary responses of multiple entities. Nevertheless, the computational challenge of learning the MVP model, given that its likelihood involves integrating over a multi-dimensional constrained space of latent variables, significantly limits its application in practice.

In this talk, I will present a flexible deep generalization of the classic MVP, the Deep Multivariate Probit Model (DMVP), which is an end-to-end learning scheme that uses an efficient parallel sampling process of the multivariate probit model to exploit GPU-boosted deep neural networks. We show that when applied to multi-entity modelling problems, which are natural DMVP applications, DMVP trains faster than classical MVP, by at least an order of magnitude, captures rich correlations among entities, and further improves the joint likelihood of entities compared with several competitive models.

The talk will run about 30 minutes, with an extended 20 minutes question and discussion segment following the talk. I released my code on the bitbucket and you are welcome to use DMVP to explore the interesting multi-entity correlation in your own domain.

Bio: Di Chen is a second-year Ph.D. student in the Department of Computer Science at Cornell University, advised by Carla P. Gomes. His research includes solving structured prediction, multi-entity modeling and covariate shift using state-of-the-art deep learning techniques.

Priya L. Donti, Carnegie Mellon University

Fri Oct 5, 2018, 1:30-2:30pm EDT (UTC-4)

Title: Inverse Optimal Power Flow: Assessing the Vulnerability of Power Grid Data

Abstract: In deregulated electricity markets, gaining knowledge of critical information such as grid structure, generator bidding cost curves, and nodal power demands could pose risks to market efficiency and cybersecurity. It is thus in the best interest of grid operators to protect critical information from the general public, so as to ensure fair, efficient, and safe market operation. At the same time, system operators such as PJM and governmental agencies such as the EPA regularly publish information about public market quantities such as energy prices and generator power outputs, which could potentially expose private data.

We seek to investigate the question of whether and to what extent privately-held market information is potentially exposed by published market information, given our knowledge that private and public parameters are related via an optimization problem called AC optimal power flow (ACOPF). Specifically, we formulate an algorithm called "inverse optimal power flow" (Inverse OPF) that uses gradient descent-based methods implemented within a neural network to learn unknown market and grid parameters. The eventual goal is to quantify the potential risks of having this information exposed.

The talk will run about 30 minutes, with 30 minutes of discussion following the talk. I welcome any feedback about the research methods as they currently stand, as well as advice regarding quantification of the real-world effects of our findings.

Bio: Priya Donti is a third-year Ph.D. student in the Computer Science Department and the Department of Engineering & Public Policy at Carnegie Mellon University, co-advised by Zico Kolter and Inês Azevedo. Her research is at the intersection of deep learning and energy policy, exploring topics such as marginal emissions prediction, grid data vulnerability, and end-to-end task-based approaches for coordinating between grid forecasting and control.

David Abel, Brown University

Fri Oct 19, 2018, 1:30-2:30pm EDT (UTC-4)

Title: Bandit Based Solar Panel Control

Abstract: Solar panels sustainably harvest energy from the sun. To improve performance, panels are often equipped with a tracking mechanism that computes the sun's position in the sky throughout the day. Based on the tracker's estimate of the sun's location, a controller orients the panel to minimize the angle of incidence between solar radiant energy and the photovoltaic cells on the surface of the panel, increasing total energy harvested. Prior work has developed efficient tracking algorithms that accurately compute the sun's location to facilitate solar tracking and control. However, always pointing a panel directly at the sun does not account for diffuse irradiance in the sky, reflected irradiance from the ground and surrounding surfaces, power required to reorient the panel, shading effects from neighboring panels and foliage, or changing weather conditions (such as clouds), all of which are contributing factors to the total energy harvested by a fleet of solar panels. In this work, we show that a bandit-based approach can increase the total energy harvested by solar panels by learning to dynamically account for such other factors. Our contribution is threefold: (1) the development of a test bed based on typical solar and irradiance models for experimenting with solar panel control using a variety of learning methods, (2) simulated validation that bandit algorithms can effectively learn to control solar panels, and (3) the design and construction of an intelligent solar panel prototype that learns to angle itself using bandit algorithms. I further discuss my reflections on this project, and the experiences our group had in attempting to translate our prototype from the lab and into the hands of solar farms.

Bio: David Abel is a fourth year PhD candidate in CS at Brown University, advised by Michael Littman. His research studies the role abstraction plays in sequential decision making, with a focus on uniting the theories of computation, abstraction, and reinforcement learning. Before Brown, he received his bachelors in CS and Philosophy from Carleton College.

Andrew Perrault, University of Southern California

Fri Nov 2, 2018, 1:30-2:30pm EDT (UTC-4)

Title: Experiential Preference Elicitation for Autonomous HVAC Systems

Abstract: AI systems that act on behalf of users require knowledge of user preferences, which can be acquired by preference elicitation. In many settings, users can more easily and accurately respond to preference queries reflecting their current, or recently experienced, context (e.g., state of the environment), than those reflecting contexts further removed. We develop and study a formal model of experiential elicitation (EE) in which query costs and response noise are state-dependent. EE settings tightly couple the problems of control and elicitation. We provide some analysis of this abstract model, and illustrate its applicability in household heating/cooling management. We propose the use of relative value queries, asking the user to compare the immediate utility of two states, whose difficulty is related to the degree and recency of a user's experience with those states. We develop a Gaussian process-based approach for modeling user preferences in dynamic EE domains and show that it accrues higher reward than several natural baselines.

Bio: Andrew Perrault is a postdoctoral scholar at University of Southern California, supervised by Milind Tambe. His research focuses on decision-making and market design in environmental applications of artificial intelligence, especially in domains where gathering information is expensive. Andrew received his PhD in Computer Science from University of Toronto, advised by Craig Boutilier, and his BA from Cornell. He is the co-founder and co-lead developer at, a non-profit that crowdfunds scholarships for secondary school students in developing countries.