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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 compsustnet_seminar-l-request@cornell.edu 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

Date/TimeSpeakerTitle
Fri Jan 25, 2019, 1:30-2:30pm EST (UTC-5) Lynn Kaack, Carnegie Mellon University Truck traffic monitoring with satellite images
Fri Feb 6, 2019, 1:30-2:30pm EST (UTC-5) Roel Dobbe, New York University An Integrative Approach to Data-Driven Control in Electric Distribution Networks
Fri Mar 8, 2019, 1:30-2:30pm EST (UTC-5) Sherrie Wang, Stanford University Weakly Supervised Learning for Satellite Imagery: Applications in Crop Mapping
Fri Mar 22, 2019, 1:30-2:30pm EDT (UTC-4) Paulo Orenstein, Stanford University Improving Subseasonal Forecasting in the Western U.S. with Machine Learning
Fri Apr 19, 2019, 1:30-2:30pm EDT (UTC-4) Anson Kahng, Carnegie Mellon University Virtual Democracy and Food Rescue
Fri Apr 26, 2019, 1:30-2:30pm EDT (UTC-4) Amanda Coston, Carnegie Mellon University Counterfactual Risk Assessments for Child Welfare Screening
Fri May 3, 2019, 1:30-2:30pm EDT (UTC-4) Jackson Killian, University of Southern California Learning to Prescribe Interventions for Tuberculosis Patients using Digital Adherence Data

Seminar Details

Lynn Kaack, Carnegie Mellon University

Fri Jan 25, 2019, 1:30-2:30pm EST (UTC-5)

Title: Truck traffic monitoring with satellite images

Abstract: The road freight sector is responsible for a large and growing share of greenhouse gas emissions, but reliable data on the amount of freight that is moved on roads are scarce. Many low- and middle-income countries have limited ground- based traffic monitoring and freight surveying activities. We show that we can use an object detection network to count trucks in satellite images and predict average daily truck traffic from those counts. In this proof of concept, we describe a complete model, test the uncertainty of the estimation, and discuss the transfer to developing countries.

Bio: Lynn Kaack is a PhD Candidate in the Department of Engineering and Public Policy at Carnegie Mellon University (CMU). She holds a Master's in Machine Learning from CMU and a Bachelor and Master of Science in Physics from Free University of Berlin, Germany. Her research focuses on policy analysis and data science of problems related to energy and climate change mitigation.

Roel Dobbe, New York University

Fri Feb 6, 2019, 1:30-2:30pm EST (UTC-5)

Title: An Integrative Approach to Data-Driven Control in Electric Distribution Networks

Abstract: Ubiquitous computing, algorithms, better devices, connectivity, and the ability to collect, store and probe large amounts of data are all becoming new commodities -- commodities that are the key ingredients to enabling new forms of automation and innovation in critical infrastructures. The question is then: how can we integrate data-driven techniques to improve and extend the capabilities of existing critical infrastructure, while safeguarding important values such as safety or privacy? In this talk, this question forms the driver for modernizing electric distribution networks to deal with higher levels of renewable generation and electrification.

We integrate concepts from control theory, machine learning, optimization, information theory and differential privacy to make concrete contributions in this context:

(1) decentralizing optimal power flow, by reconstructing the solution to centralized problems with locally available information, (2) providing a formal framework to assess learning-based decentralized policies and determine optimal communication strategies, by adopting a rate distortion approach, and (3) enabling the protection of sensitive information in the objectives and constraints of distributed optimization and control problems, by integrating a differential privacy mechanism.

We conclude with an overview on the inherent accumulation of bias and error in data-driven decision-making, and a call for assessing one's epistemology and engaging with domain experts and beneficiaries to steer the design of systems towards promoting safe, beneficial and just outcomes.

Bio: Roel's research addresses the development, analysis, integration and governance of data-driven systems. His PhD work combined optimization, machine learning and control theory to enable monitoring and control of safety-critical systems, including energy & power systems and cancer diagnosis and treatment. His diverse background led him to examine the ways in which values and stakeholder perspectives are represented in the process of designing and deploying AI and algorithmic decision-making and control systems. Roel is passionate about developing practices to help engineers and computer scientists engage more closely both with impacted communities and scholars in the social sciences, and to better contend with serious questions of ethics and governance. Towards this end, Roel founded Graduates for Engaged and Extended Scholarship around Computing & Engineering (GEESE); a student organization stimulating graduate students across all disciplines studying or developing technologies to take a broader lens at their field of study and engage across disciplines.

Sherrie Wang, Stanford University

Fri Mar 8, 2019, 1:30-2:30pm EST (UTC-5)

Title: Weakly Supervised Learning for Satellite Imagery: Applications in Crop Mapping

Abstract: Feeding 7 billion people — a number likely to surpass 9 billion by 2050 — will require smarter agriculture. Knowing where crops grow worldwide is a crucial first step. Today, this information is acquired through on-the-ground surveys, which take a long time, require many people, and are tough to conduct in the countries where data is needed most. Tomorrow, it will be possible to harness satellite imagery and machine learning to decrease the cost and difficulty of mapping this information at scale. However, one main challenge in applying the tools of machine learning to crop type mapping is the low quantity of ground truth labels on which to train state-of-the-art methods (e.g. deep learning). This talk will offer a window into how unsupervised and weakly supervised learning methods can help us bridge this label gap and understand which crops are grown where.

Bio: Sherrie is a 4th year PhD student at Stanford's Institute for Computational and Mathematical Engineering (ICME), advised by Professor David Lobell at the Center on Food Security and the Environment. Her research focuses on developing semi-supervised and unsupervised methods for remote sensing data to enable understanding of food systems and their interaction with the environment at a large scale.

Paulo Orenstein, Stanford University

Fri Mar 22, 2019, 1:30-2:30pm EDT (UTC-4)

Title: Improving Subseasonal Forecasting in the Western U.S. with Machine Learning

Abstract: To improve the accuracy of long-term forecasts, the Bureau of Reclamation and the National Oceanic and Atmospheric Administration launched the Subseasonal Climate Forecast Rodeo, a year-long real-time forecasting challenge, in which participants aimed to skillfully predict temperature and precipitation in the western U.S. two to four weeks and four to six weeks in advance. Here we present and evaluate our machine learning approach to the Rodeo. Our system is an ensemble of two regression models, and exceeds that of the top Rodeo competitor as well as the government baselines for each target variable and forecast horizon.

Bio: Paulo Orenstein is a PhD Candidate in the Department of Statistics at Stanford University. He holds a Master's in Mathematics and a Bachelor of Science in Economics, both form PUC-Rio, in Brazil. His research focuses on the interplay between statistics, probability, and computation, particularly as they apply to high-dimensional Bayesian models and Monte Carlo methods.

Anson Kahng, Carnegie Mellon University

Fri Apr 19, 2019, 1:30-2:30pm EDT (UTC-4)

Title: Virtual Democracy and Food Rescue

Abstract: Virtual democracy is an approach to automating decisions, by learning models of the preferences of individual people, and, at runtime, aggregating the predicted preferences of those people on the dilemma at hand. One of the key questions is which aggregation method — or voting rule — to use; we offer a novel statistical viewpoint that provides guidance. Specifically, we seek voting rules that are robust to prediction errors, in that their output on people's true preferences is likely to coincide with their output on noisy estimates thereof. We prove that the classic Borda count rule is robust in this sense, whereas any voting rule belonging to the wide family of pairwise-majority consistent rules is not. Our empirical results further support, and more precisely measure, the robustness of Borda count. Lastly, we consider the application of virtual democracy to the domain of food rescue, or matching food donations to needy recipients.

Bio: Anson is a third-year PhD student in the Computer Science Department at Carnegie Mellon University, where he is advised by Ariel Procaccia. He works on theoretical problems at the intersection of computer science and economics, particularly in computational social choice. Recently, he has worked on voting aggregation for noisy votes, impartial voting mechanisms, virtual democracy, and liquid democracy.

Amanda Coston, Carnegie Mellon University

Fri Apr 26, 2019, 1:30-2:30pm EDT (UTC-4)

Title: Counterfactual Risk Assessments for Child Welfare Screening

Abstract: Risk assessments and other algorithmic decision-making systems are increasingly used in high-stakes applications such as criminal justice, consumer lending, and child welfare screening decisions. Many risk assessment models are trained on observational data where historical interventions may have affected the observed outcomes. Our research investigates how these observational risk models may be biased in the case of child welfare screening decisions, and we propose counterfactual risk assessments that account for the intervention affects.

Bio: Amanda is a joint PhD student in Machine Learning and Public Policy at Carnegie Mellon University. She is broadly interested in how machine learning can solve problems of societal interest, and her research areas include algorithmic fairness, causal inference, and machine learning for healthcare.

Jackson Killian, University of Southern California

Fri May 3, 2019, 1:30-2:30pm EDT (UTC-4)

Title: Learning to Prescribe Interventions for Tuberculosis Patients using Digital Adherence Data

Abstract: Digital Adherence Technologies (DATs) are an increasingly popular method for verifying patient adherence to many medications. We analyze data from one city served by 99DOTS, a phone-call-based DAT deployed for Tuberculosis (TB) treatment in India where nearly 3 million people are afflicted with the disease each year. The data contains nearly 17,000 patients and 2.1M phone calls. We lay the groundwork for learning from this real-world data, including a method for avoiding the effects of unobserved interventions in training data used for machine learning. We then construct a deep learning model, demonstrate its interpretability, and show how it can be adapted and trained in three different clinical scenarios to better target and improve patient care. In the real-time risk prediction setting our model could be used to proactively intervene with 21% more patients and before 76% more missed doses than current heuristic baselines. For outcome prediction, our model performs 40% better than baseline methods, allowing cities to target more resources to clinics with a heavier burden of patients at risk of failure. Finally, we present a case study demonstrating how our model can be trained in an end-to-end decision focused learning setting to achieve 15% better solution quality in an example decision problem faced by health workers.

Bio: Jackson Killian is a PhD student in computer science at the University of Southern California, where he is co-advised by Milind Tambe and Bistra Dilkina. His research focuses on explainable machine learning in healthcare, motivated by applications to interventions for traditionally underserved or difficult to treat populations. He is supported by a NSF Graduate Research Fellowship.