Prasad Tadepalli


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


Education

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.

Teaching

Special Courses and Tutorials


Research


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. Janardhan Rao Doppa , Topic: Structured Prediction
  2. Walker Orr , Topic: Learning from Texts
  3. Aswin Nadamuni Raghavan, Topic: Factored and Multiagent Planning
  4. Mandana Hamidi, Topic: Imitation Learning of Hiererachical Policies
  5. Chao Ma, Topic: Coreference Resoloution
  6. Andrew Emmott, Topic: Anomaly Detection
  7. Kranti Kumar Potanapalli, Topic: Learning for Search and Coverage

Previous students

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

Vita


Prasad Tadepalli, tadepall@cs.orst.edu