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

• CS 533: Intelligent Agents and Decision Making (Spring '17)

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

• Nima Dolatnia,  PhD
• Risheek Garrepalli (co-advised w/ Tom Dietterich), PhD
  
• Murugeswari Issakkimuthu, PhD
  
• Jeff Juozapaitis, PhD
  
• Anurag Koul, MS
  
• Erich Merril, PhD
  
• Md Amran Siddiqui, PhD
• Alex Turner, PhD
• Anthony Williams, PhD

Former Students

• Trevor Fiez, MS 2017 (co-advised w/ Sinisa Todorovic), Thesis: An Analysis of Training Methodologies for Deep Visual Tracking
  
• Jesse Hostetler, PhD 2017 (co-advised w/ Tom Dietterich),  Thesis:  Monte Carlo Tree Search with Fixed and Adaptive Abstractions
  
• Sheng Chen, PhD 2017, Thesis: Object Tracking-by-Segmentation in Videos
  
• Eric Marshall,  MS 2015, Project:  An Empirical Evaluation of Policy Rollout for Clue
• Jervis Pinto, PhD 2015, Thesis: Incorporating and Learning Behavior Constraints for Sequential Decision Making
  
• Vikedo Terhuja,  MS 2015, Thesis:  Automatic Detection of Possessions and Shots  from  Raw Basketball Video
• Qingkai Lu, MS 2015, Thesis: Offensive Direction Inference in Real-World Football Video
  
• Kshitij Judah, PhD 2014, Thesis: New Learning Modes for Sequential Decision Making
• Janardhan Rao ( Jana ) Doppa, PhD 2014 (co-advised w/ Prasad Tadepalli), Thesis: Integrating Learning and Search for Structured Prediction
• Kranti Kumar, MS 2013 (co-advised with Prasad Tadepalli), Thesis: Coactive Learning for Multi-Robot Search and Coverage
• Shikhar Mall, MS 2013, Project: Reinforcement Learning for P2P Backup Applications
• Joe Selman, MS 2012, Project: REPEL: An Inference Engine for Probabilistic Event Logic
• Aaron Wilson, PhD 2012 (co-advised with Prasad Tadepalli), Thesis: Bayesian Methods for Knowledge Transfer and Policy Search in Reinforcement Learning
• Rob Hess, PhD 2012, Thesis: Toward Computer Vision for Understanding American Football in Video
• Brian King, MS 2012, Thesis: Adversarial Planning by Strategy Switching in a Real-Time Strategy Game
• Yuehua Xu, PhD 2010, Thesis: Learning Ranking Functions for Efficient Search
• Paul Lewis, MS 2010,  Thesis: Ensemble Monte-Carlo Planning: An Empirical Study
• Ronny Bjarnason, PhD 2010 (co-advised with Prasad Tadepalli): Monte-Carlo Planning for Probabilistic Domains
• Guohua Hao, MS 2009, Thesis: Revisiting Output Coding for Sequential Supervised Learning
• Radha-Krishna Balla, MS, 2009, Thesis: UCT for Tactical Assaults in Real-Time Strategy Games
• Sean McDougal, MS 2008, Project: Automatic Panorama Stitching
• Benjamin Brewster, MS 2007, Project: Finding and Using Chokepoints in Stratagus
• Christopher Ventura, MS 2007, Thesis: A SAT-Based Planning Framework for Optimizing Resource Production
• Sungwook Yoon, PhD 2006 (co-advised with Robert Givan), Thesis: Learning Control Knowledge for AI Planning Domains
• Daman Oberoi, MS 2006, Project: "Simulation-Based Optimization of Football Defenses"
• Hema Jyothi, MS 2006 (co-advised with Thinh Nguyen), Project: "Reinforcement Learning for Network Routing"