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. Alexander Turner
    Topic: AI Safety
  2. Yilin Yang
    Topic: Explaining Neural Machine Translation
  3. Vivswan Shitole
    Topic: Structured Attention Graphs for Understanding Immage Classifications
  4. Prachi Rahurkar
    Topic: Natural Language Question Answering
  5. Parijat Bhatt
    Topic: Learning to Search
  6. Rajesh Mangannavar
    Topic: Reinforcement Learning

Past Advisees

  1. Walker Orr, PhD: Towards Narrative Understanding with Deep Networks and Hidden Markov Models
  2. Mandana Hamidi-Haines, PhD: Learning from Examples and Interactions
  3. Chao Ma, PhD: New Directions in Search-based Structured Prediction: Multi-task Learning and Integration of Deep Models
  4. Aswin Nadamuni Raghavan, PhD: Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces
  5. Qin Rui , MS: Information Extraction from Weather Reports
  6. Purbasha Chatterjee, MS: Answer Selection with Attentive Clustering
  7. Meghamala Sinha, MS: Pooling vs. Voting: An Empirical Study of Learning Causal Structures
  8. Durga "Harish" Dayapule , Project: Extending the Scope of Hindisight Optimization for Emergency Planning
  9. Janardhan Rao Doppa , PhD: A Search-based Framework for Structured Prediction
  10. Kranti Kumar Potanapalli, MS: Learning for Search and Coverage
  11. Neville Mehta , PhD: Learning Hierarchies for Reinforcement Learning
  12. Aaron Wilson , PhD: Bayesian Optimization for Reinforcement Learning
  13. Scott Proper , PhD: Multi-agent Reinforcement Learning
  14. Ronny Bjarnason , PhD: Multi-level Rollout Reinforcement Learning
  15. Sriraam Natarajan , Ph.D.: Statistical Relational Learning
  16. Charles Parker, Ph.D: Structured Gradient Boosting
  17. Kiran Polavarapu, MS: Event and Sentiment Extraction in the Financial Domain
  18. Thierry Donneaugolencer, MS: Planning by Sparse Sampling in Partially Observable Domains
  19. Kim Mach , MS: Experimental Evaluation of Auto-exploratory Model-free Average-Reward Reinforcement Learning
  20. Nimish Dharawat, MS: Learning Tree Patterns for Information Extraction
  21. Sriraam Natarajan, MS: Multi-criterion Average-Reward Reinforcement Learning
  22. Sandeep Seri, MS : Hierarchical Average-reward Reinforcement Learning.
  23. Hong Tang, MS : Average-reward Reinforcement Learning for Product Delivery by Multiple Vehicles.
  24. Tom Amoth, PhD : Exact Learning of Tree Patterns.
  25. Ray Liere, PhD : Active learning with committees with applications to text categorization.
  26. Chandra Reddy, PhD : Learning Hierarchical Decomposition Rules for Planning: an Inductive Logic Programming Approach.
  27. DoKyeong Ok, PhD: A Study of Model-based Average Reward Reinforcement Learning.
  28. Michael Chisholm, MS: Learning Classification Rules by Radomized Iterative Local Search.
  29. Peter Drake, MS: Constructive Induction for Improved Learning of Boolean Functions
  30. Yenong Qi, MS: Local Search Methods for Job Shop Scheduling
  31. Silvana Roncagliolo , MS: Empirical Speedup Learning of Decomposition Rules for Planning
  32. Ramana Isukapalli, MS: Learning Macro-operators for Planning Using Simulators


Prasad Tadepalli,