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

Teaching

• CS531: Artificial Intelligence (Fall 2013)
  
• CS517: Theory of Computation (Spring 2014)
  

Special Courses and Tutorials

• Monte-Carlo Methods in AI , with Tom Dietterich, Alan Fern, and Weng-Keen Wong, Corvallis, March 18-22, 2013.
• Monte-Carlo Methods in AI , with Tom Dietterich, Alan Fern, Kagan Tumer, and Weng-Keen Wong, Corvallis, 2012.
• 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

• Research Interests: Transfer Learning, Natural Language Understanding, Reinforcement Learning, Decision-Theoretic Planning, Structured Prediction
  
• Publications
• Research Projects:
  Active Transfer Learning
  Deep Reading and Learning
  Learning and Planning in Service Domains
  Search-based Structured Prediction
  
• Applications of research: Real-Time Strategy Games, Air-space Management, Fire and Emergency Response, Product Delivery, Proactive Assistance, Card Games, Information Extraction, Natural Language Understanding

Useful Links

• Graduate Student Orientation at OSU Computer Science
  
  
• Computer Science Student Resources
• 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. 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