Teaching

Fall 2016:  CIS 700 - Integrated Intelligence for Robotics
Fall 2016:  CIS 419/519 - Introduction to Machine Learning
Spring 2016:  CIS 110 - Introduction to Computer Science
Fall 2015:  CIS 700 - Integrated Intelligence for Robotics
Fall 2015:  CIS 419/519 - Introduction to Machine Learning
Spring 2015:  CIS 110 - Introduction to Computer Science
Fall 2014:  CIS 419/519 - Introduction to Machine Learning
Spring 2014:  CIS 110 - Introduction to Computer Science
Fall 2013:  CIS 110 - Introduction to Computer Science
Spring 2013:  CMSC 380 - Relational Network Analysis
Spring 2013:  CMSC 246 - Programming Paradigms
Fall 2012:  CMSC 206 - Data Structures
Fall 2012:  CMSC 110 - Introduction to Computing
Spring 2012:    CMSC 372 - Artificial Intelligence
Spring 2012:    CMSC 206 - Data Structures

Past courses taught at Bryn Mawr, Swarthmore, and UMBC


Doug Fisher, Bistra Dilkina, Carla Gomes, and I started the online textbook Artificial Intelligence for Computational Sustainability:  A Lab Companion as an experiment in crowd-sourced textbook creation. This book is intended to supplement an AI course with assignments related to sustainability.    We presented papers on this project at AAAI'12 and Computational Sustainability 2012.

Contact Information

Mailing Address:
University of Pennsylvania
Computer and Information Science Dept.
Levine Hall
3330 Walnut Street
Philadelphia, PA 19104-6309

Office: Levine 264
E-mail:
Phone: 215-746-1734
Fax: 215-898-0587

      

Office Hours (Spring 2017):
Mon/Wed 11:00-11:55am
and by appointment

Administrative Assistant:
Laura Fox
Office: Levine 308
Phone: 215-898-3191
E-mail:

About Eric

Eric Eaton is a faculty member in the Department of Computer and Information Science at the University of Pennsylvania, and a member of the GRASP (General Robotics Automation, Sensing, Perception) lab. Prior to joining Penn, he was a Visiting Assistant Professor in the computer science department at Bryn Mawr College. His primary research interests lie in the fields of machine learning, artificial intelligence, and data mining with applications to robotics, search & rescue, environmental sustainability, and medicine. In particular, his research focuses on developing versatile AI systems that can learn multiple tasks over a lifetime of experience in complex environments, transfer learned knowledge to rapidly acquire new abilities, and collaborate effectively with humans and other agents through interaction. This research is funded by grants from the Office of Naval Research, the National Science Foundation, and Lockheed Martin.

Before moving into academia, Eric spent two years as a senior research scientist at Lockheed Martin Advanced Technology Laboratories working in applied research. At Lockheed Martin ATL, he led a number of machine learning research projects in the Artificial Intelligence Lab with a focus on their application for a variety of DoD organizations. While at Lockheed Martin, he was also part-time faculty in computer science at Swarthmore College.

Eric received his Ph.D. in computer science from the University of Maryland, Baltimore County (UMBC), focusing on artificial intelligence and machine learning. His dissertation developed methods for selective knowledge transfer between learning tasks and was advised by Marie desJardins. At UMBC, he was a member of the Multi-Agent Planning and LEarning (MAPLE) research group and also a part-time instructor.

Further details are provided in his curriculum vitae.

Research

My primary research interests are in the areas of artificial intelligence and machine learning, with a focus on the following topics:

  • Lifelong learning of multiple consecutive tasks over long time scales,
  • Knowledge transfer between learning tasks, and
  • Interactive AI methods that combine system-driven active learning with extensive user-driven control over learning and reasoning processes.

I focus on applications of these methods to robotics, precision medicine, and sustainability.

Details of my research on these topics can be found on my research and publications pages.

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Students and Postdocs

I've been fortunate to work with a number of talented students on these research projects.

Current Research Assistants

  • James Stokes (Postdoc)
  • David Isele (PhD student, CIS)
  • Jorge Mendez (PhD student, CIS)
  • Mohammad Rostami (PhD student, ESE)
  • Chris Clingerman (PhD student, CIS; Primary advisor: Dan Lee)

Alumni and Former Students

  • Jose Marcio Luna (Postdoc 2014-2016 at Penn):   lifelong learning, medical applications
    (Continued to a residency in medical physics at Penn Radiation Oncology)
  • Haitham Bou Ammar (Postdoc 2013-2015 at Penn):   lifelong learning, knowledge transfer
    (Continued to a 2nd postdoc at Princeton, then a faculty position at the American University of Beirut as an Assistant Professor)
  • Paul Ruvolo (Postdoc 2012-2013 at Bryn Mawr College):  lifelong learning, knowledge transfer
    (Continued to a faculty position at Olin College as an Assistant Professor)
  • Decebal Mocanu (Visiting scholar at Penn 2014; PhD student at TU Eindhoven)
  • Vishnu Purushothaman Sreenivasan (MS, University of Pennsylvania): multi-task reinforcement learning
  • Lisa Lee (BS 2014, Princeton University): multi-task reinforcement learning for robotics
  • Fangyu Xiong (BS 2015, Haverford College):  active transfer learning
  • Jacy Li (BS 2014, Bryn Mawr College):  lifelong learning
  • Rachel Li (BS 2014, Bryn Mawr College):  relational community detection using Gaussian processes
  • Caitlyn Clabaugh (BS 2013, Bryn Mawr College):  learning to create automatic A vs B music mashups
    (Continued to PhD studies at USC)
  • Rose Abernathy (BS 2013, Haverford College):  social gaming
  • Meagan Neal (BS 2013, Bryn Mawr College):  active multi-task learning
  • Gabriel Ryan (BS 2013, Swarthmore College):  lifelong RL with Horde
  • Ben Cutilli (BS 2013, Haverford College):  vision and UGV control in USARsim
  • Leila Zilles (BS 2012, Bryn Mawr College):  active transfer learning for sparse language translation
    (Continued to PhD studies at UWashington under an NSF Grad Fellowship)
  • David Wilikofsky (BS 2012, Swarthmore College):  bootstrapping RL with human demonstration
  • Emily Levine (BS 2012, Bryn Mawr College):  learning to predict Parkinson's At Risk Syndrome
  • Steven Gutstein (Postdoc 2011-2012):  lifelong learning, knowledge transfer (Continued to work for JPMorgan)
  • Kerstin Baer (BS 2011, Bryn Mawr College):  continual knowledge transfer
    (Continued to PhD studies at Stanford under an NSF Grad Fellowship)
  • Alexandra Lee (BS 2011, Bryn Mawr College):  visualizing community detection
    (Continued to a Masters program at UWashington)
  • Rachael Mansbach (BS 2011, Swarthmore College):  interactive community detection
    (Continued to PhD studies at UIUC)