Alan Fern



Professor and Associate Head of Research
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: alan.fern@oregonstate.edu
Postal Address: Kelley Engineering Center, Corvallis, OR 97330-5501, U.S.A.

Quick Links: Teaching Publications Students


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 and automated planning. I am particularly interested in the intersection of these areas. Some example projects include:

Explainable Artificial Intelligence: It is becoming increasingly common for autonomous and semi-autonomous systems, such as UAVs, robots, and virtual agents, to be develop via a combination of traditional programming and machine learning. Currently, acceptance testing of these systems is problematic due to the black box nature of machine-learned components, which does not allow testers to understand the rationale behind the learned decisions. Our research will develop the new paradigm of explanation-informed acceptance testing (xACT), which will allow testers to not only observe and evaluate the behavior of machine-learned systems, but to also evaluate explanations of the decisions leading to that behavior. As a result, the xACT paradigm allows testers to determine whether machine-learned systems are making decisions "for the right reasons", which provides stronger justification for trusting the system in (semi-)autonomous operation. The public will benefit from this technology via the availability of more understandable and, in turn, trustworthy  (semi-)autonomous systems for complex applications in  defense, industry, and everyday life.

Learning for Sequential Decision Making: We study various aspects of learning in the context of sequential decision making. This includes reinforcement learning and learning for the purpose of speeding up planners (speedup learning) based on prior planning experience. 

Anomaly Detection and Explanation: We study how to best detect and explain anomalies with a particular focus on security applications and interaction with end-user analysts.

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

Digital Scout Project: We study computer vision and machine learning techiuqes for interpreting video of American football.


Current Graduate Students


Former Students