Alan Fern



Associate Professor
School of Electrical Engineering and Computer Science
Oregon State University

Office Location: 2071 Kelley Engineering Center
(541) 737-9202 (office)(I never check phone messages, send an email instead)
(541) 737-1300 (fax)
E-mail: afern@eecs.oregonstate.edu
Postal Address: Kelley Engineering Center, Corvallis, OR 97330-5501, U.S.A.

Quick Links: Teaching Research  Publications  Students  Resources


Education

B.S. Electrical Engineering, University of Maine, 1997

M.S. Computer Engineering, Purdue University, 2000

Ph.D. Computer Engineering, Purdue University, 2004 (advised by Robert Givan)


Teaching


Research

My primary research interests are in the field of artificial intelligence, where I focus on the sub-areas of machine learning, automated planning, and computer vision. I am particularly interested in the intersection of these areas. Some example projects include:

·        Digital Scout Project: where we study computer vision and machine learning techniques for interpreting raw video of American football, basketball, and other sports.

·        Learning to Speedup Automated Planning Algorithms: where we study how to speed up planning performance in both deterministic and stochastic domains by learning from past planning experience.

·        Integrating Expressive Feedback into Reinforcement and Imitation Learning: where we study how to learn from richer forms of feedback and interaction than considered in traditional imitation and reinforcement learning.

·        Anomaly Detection and Explanation: were we study how to best detect and explain anomalies with a particular focus on insider threat detection.

·        Automated Planning for Complex Experimental Design: where we study planning and learning algorithms for planning out the experimental activities with the goal of maximizing some experimental objective. Bioengineering is one application domain that motivates this work.

·        Large Scale Stochastic Planning: where we study planning algorithms for scaling to very large stochastic domains.


Current Graduate Students

Former Students


Resources

·         ICAPS 2010 Monte-Carlo Planning Tutorial (PDF, PPT)