Prasad Tadepalli

Associate Professor
Computer Science Department
Oregon State University
Corvallis, OR 97331

Voice: (541) 737-5552
Fax: (541)-737-3014
e-mail: tadepall at cs dot orst dot edu Office: 3069, Kelley Engineering Center
Office Hours: Tues, Fri, 4-5 PM
Postal Address: 1048, Kelley Engineering Center, Corvallis, OR 97331-3202, U.S.A.

Education

Teaching

• Artificial Intelligence (Fall '08)

Tutorials

• Reinforcement Learning: From Foundations to Advanced Topics , with Sridhar Mahadevan and Vivek Borkar, IJCAI 2007.
• Decision-Theoretic Planning and Learning in Relational Domains , with Alan Fern and Kristian Kersting, AAAI, 2008.

Research

• Workshop on Relational Reinforcement Learning
• Research Projects: Transfer Learning, Relational Reinforcement Learning, Assistantship Learning, Integrated Learning
• Publications
• Applications of research: Real-Time Strategy Games, Air-space Management, Fire and Emergency Response, Product Delivery, Proactive Assistance, Card Games

Useful Links

• Graduate Student Orientation at OSU Computer Science
• Machine Learning Reading Group
• Matrix cookbook
• Lisp lectures (Winter '06)
• Journal of AI Research
• The Harvard Guide to Happiness
• Graduate student resources maintained by Prof. Vasant Honavar
• What Every New Grad Student Should Know (Univ of Indiana)
• Department Dishonesty Policy
• Graduate Advising Guide
• Students with Disabilities Policy
• CSGSA home page

Current students

1. Scott Proper , PhD: Multi-agent Reinforcement Learning
2. Janardhan Rao Doppa , PhD: Integrated Learning
3. Aaron Wilson , PhD: Hierarchical Bayesian Reinforcement Learning
4. Ronny Bjarnason , PhD: Multi-level Rollout Reinforcement Learning
5. Neville Mehta , PhD: Hierarchical Reinforcement Learning

Previous students

1. Sriraam Natarajan , Ph.D.: Statistical Relational Learning
2. Charles Parker, Ph.D: Structured Gradient Boosting
3. Kiran Polavarapu, MS: Event and Sentiment Extraction in the Financial Domain
4. Thierry Donneaugolencer, MS: Planning by Sparse Sampling in Partially Observable Domains
5. Kim Mach, MS: Experimental Evaluation of Auto-exploratory Model-free Average-Reward Reinforcement Learning
6. Nimish Dharawat, MS: Learning Tree Patterns for Information Extraction
7. Sriraam Natarajan, MS: Multi-criterion Average-Reward Reinforcement Learning
8. Sandeep Seri, MS : Hierarchical Average-reward Reinforcement Learning.
9. Hong Tang, MS : Average-reward Reinforcement Learning for Product Delivery by Multiple Vehicles.
10. Tom Amoth, PhD : Exact Learning of Tree Patterns.
11. Ray Liere, PhD : Active learning with committees with applications to text categorization.
12. Chandra Reddy, PhD : Learning Hierarchical Decomposition Rules for Planning: an Inductive Logic Programming Approach.
13. DoKyeong Ok, PhD: A Study of Model-based Average Reward Reinforcement Learning.
14. Michael Chisholm, MS: Learning Classification Rules by Radomized Iterative Local Search.
15. Peter Drake, MS: Constructive Induction for Improved Learning of Boolean Functions
16. Yenong Qi, MS: Local Search Methods for Job Shop Scheduling
17. Silvana Roncagliolo, MS: Empirical Speedup Learning of Decomposition Rules for Planning
18. Ramana Isukapalli, MS: Learning Macro-operators for Planning Using Simulators

Vita