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.

Office Hours:

Education

Teaching

• CS531: Artificial Intelligence
  
• CS516: Theory of Computation and Formal Languages (Fall 2016)
  
• CS507: EECS Ethics and Diversity (Fall 2017)
  
• CS519: Algorithms: Design, Analysis and Implementation (Fall 2018,2019)
  

Special Courses and Tutorials

• Monte-Carlo Methods in AI , with Tom Dietterich, Alan Fern, and Weng-Keen Wong, Corvallis, March 18-22, 2013.
• 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: Natural Language Understanding, Reinforcement Learning, Relational Learning, Causal Inference
  
• Publications (DBLP page)
• Research Projects:
  Explainable AI
  Machine Common Sense
  
• Applications of research: Computer Games, Fire and Emergency Response, Logistics, Biological networks

Useful Links

• 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. 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

Vita