IMPORTANT NOTE: Due to precautions around the COVID-19 virus, this event has been postponed to a later date to-be-determined.

How can machine learning and data science tools improve our understanding of energy systems and manage them to be more accessible, affordable, reliable, and clean?  This single-track symposium will explore cutting-edge approaches addressing this question, highlighting the work of established experts as well as emerging scholars in the field. 

In particular, the symposium will examine how data science and machine learning are driving innovation in areas including: 

  • Remote sensing of energy resources and infrastructure;
  • Energy systems modeling;
  • Energy consumption end uses;
  • Energy and climate change; and
  • Energy access. 

Call for Abstracts

Our Call for Abstracts for emerging scholars (including Ph.D. students, postdoctoral fellows, junior faculty, and other early-career researchers) conducting research at the intersection of energy and data science has closed.  
Accepted abstracts will be invited to participate as lightning talks, session speakers, or poster presentations.



Dr. Mario Berges, Carnegie Mellon University 

  • Professor, Civil, and Environmental Engineering 
  • Director, Intelligent Infrastructure Research Lab 

Dr. Kyle Bradbury, Duke University 

  • Managing Director, Energy Data Analytics Lab 
  • Lecturing Fellow, Electrical and Computer Engineering 

Dr. Brian Min, University of Michigan 

  • Associate Professor, Political Science 
  • Faculty Affiliate for Energy Institute 

Martha Morrissey, Development Seed 

  • Machine Learning Engineer 

Dr. Elisabeth Moyer, University of Chicago 

  • Associate Professor, Atmospheric Science 
  • Co-Principal Investigator, Center for Robust Decision-making on Climate and Energy Policy 

John Pressley and Dylan Lustig, Duke Energy Corporation

  • Director, Information Management Solutions, Digital Transformation and Lead Analyst, Digital Transformation

Dr. Edward Rubin, University of Oregon 

  • Assistant Professor, Economics 

Dr. Cynthia Rudin, Duke University 

  • Professor, Computer Science 
  • Principal Investigator, Prediction Analysis Lab 

More speakers and details coming soon!

The symposium will also highlight research by two interdisciplinary cohorts of doctoral students who are part of Duke University's Energy Data Analytics Ph.D. Student Fellows Program, funded by the Alfred P. Sloan Foundation

Form and Format of Presentations

  • Session Speakers: 15-minute presentations, followed by 5 minutes of Q & A. Multiple speakers will present during each 1- to 1.5-hour session.
  • Lightning talks: 5-minute presentations that give emerging scholars a chance to showcase their work or share a big idea.  
  • Dissemination: To increase the impact of your work, speaker presentations will be recorded and videos will be made available online after the symposium. 

(Details subject to change)


This event is currently being rescheduled, possibly late summer/early fall 2020. More details and an agenda are coming soon.

Travel Funding

Funding up to $600 will be available to scholars with accepted abstracts to provide partial support in offsetting the cost of attendance to the symposium. Additional funding may be available by request (and upon abstract acceptance). More details will be provided by email upon acceptance of proposals. 

Organizers and Funding

Alfred P. Sloan Foundation LogoThe symposium is being organized by Dr. Kyle Bradbury, Dr. Jordan MalofDr. Brian MurrayDr. Billy Pizer, and Dr. Cynthia Rudin at Duke University's Energy Data Analytics Lab, a collaborative effort of the Duke University Energy Initiative (which houses it), the Rhodes Information Initiative at Duke, and the Social Science Research Institute. Funding support for the workshop is provided by a grant from the Alfred P. Sloan Foundation.


Contact Trey Gowdy at

Room TBD, Duke University, Durham, NC
Date & Time
Thursday, May 14, 2020 - 4:00 pm to Friday, May 15, 2020 - 5:00 pm
Location: Webinar
Time: 3:00 pm to 4:00 pm
Location: Webinar - Register for link
Time: 5:30 pm to 6:30 pm
Location: Webinar
Time: 12:00 pm to 1:00 pm