Prasad Tadepalli

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
Postal Address: 1048, Kelley Engineering Center, Corvallis, OR 97331-3202, U.S.A.

Office Hours:

Tuesday: 1:00-2:00, Thursday: 2:00-3:00


PhD, Computer Science, Rutgers University, U.S., 1990;
MTech, Computer Science, Indian Institute of Technology, Madras, India, 1981;
BTech, Electrical Engineering, Regional Engineering College, Warangal, India, 1979.


Special Courses and Tutorials


Conferences and Journals

International Planning Competition - Learning Track
Machine Learning Journal Special issue on Structured Prediction
International Conference on Machine Learning (ICML) 2007

Inductive Logic Programming (ILP) 2007

Useful Links

Current students

  1. Walker Orr , Topic: Event Detection and Inference from Texts
  2. Mandana Hamidi, Topic: Imitation Learning of Hiererachical Policies
  3. Chao Ma, Topic: Coreference Resoloution and Entity Linking
  4. Beatrice Moissinac, Topic: Algorithmic Teaching and Tutoring

Previous students

  1. Aswin Nadamuni Raghavan, PhD: Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces
  2. Qin Rui, MS: Information Extraction from Weather Reports
  3. Janardhan Rao Doppa , PhD: A Search-based Framework for Structured Prediction
  4. Kranti Kumar Potanapalli, MS: Learning for Search and Coverage
  5. Neville Mehta , PhD: Learning Hierarchies for Reinforcement Learning
  6. Aaron Wilson , PhD: Bayesian Optimization for Reinforcement Learning
  7. Scott Proper , PhD: Multi-agent Reinforcement Learning
  8. Ronny Bjarnason , PhD: Multi-level Rollout Reinforcement Learning
  9. Sriraam Natarajan , Ph.D.: Statistical Relational Learning
  10. Charles Parker, Ph.D: Structured Gradient Boosting
  11. Kiran Polavarapu, MS: Event and Sentiment Extraction in the Financial Domain
  12. Thierry Donneaugolencer, MS: Planning by Sparse Sampling in Partially Observable Domains
  13. Kim Mach, MS: Experimental Evaluation of Auto-exploratory Model-free Average-Reward Reinforcement Learning
  14. Nimish Dharawat, MS: Learning Tree Patterns for Information Extraction
  15. Sriraam Natarajan, MS: Multi-criterion Average-Reward Reinforcement Learning
  16. Sandeep Seri, MS : Hierarchical Average-reward Reinforcement Learning.
  17. Hong Tang, MS : Average-reward Reinforcement Learning for Product Delivery by Multiple Vehicles.
  18. Tom Amoth, PhD : Exact Learning of Tree Patterns.
  19. Ray Liere, PhD : Active learning with committees with applications to text categorization.
  20. Chandra Reddy, PhD : Learning Hierarchical Decomposition Rules for Planning: an Inductive Logic Programming Approach.
  21. DoKyeong Ok, PhD: A Study of Model-based Average Reward Reinforcement Learning.
  22. Michael Chisholm, MS: Learning Classification Rules by Radomized Iterative Local Search.
  23. Peter Drake, MS: Constructive Induction for Improved Learning of Boolean Functions
  24. Yenong Qi, MS: Local Search Methods for Job Shop Scheduling
  25. Silvana Roncagliolo, MS: Empirical Speedup Learning of Decomposition Rules for Planning
  26. Ramana Isukapalli, MS: Learning Macro-operators for Planning Using Simulators


Prasad Tadepalli,