Computer Science

3069 Kelley Engineering Center
Corvallis, OR 97331-5501
(541) 737-5552
(541) 737-1300


  • Ph.D., Computer Science, Rutgers University, U.S., 1990
  • M.Tech, Computer Science, Indian Institute of Technology, Madras, India, 1981
  • B.Tech, Electrical Engineering, Regional Engineering College, Warangal, India, 1979


Prasad Tadepalli has an M.Tech in Computer Science from Indian Institute of Technology, Madras, India and a Ph.D. from Rutgers University, New Brunswick. He joined Oregon State University, Corvallis, as an assistant professor in 1989. He is now a professor in the School of Electrical Engineering and Computer Science of Oregon State University.

He co-authored over a hundred papers in artificial intelligence and machine learning in various journals, conferences, and workshops. He organized many workshops and tutorials and co-chaired the international conference on inductive logic programming in 2007. He was a member of many conference program committees and is currently an action editor for the Journal of Artificial Intelligence Research, and the Machine Learning journal.


Research Interests

Research Areas
Artificial intelligence, machine learning, automated planning and reasoning, natural languageprocessing.

Research Description
My main research interest is to understand learning and thinking by simulating them in computers. My work ranges from theoretical analyses of learning problems and algorithms to their implementation, evaluation, and application to real-world problems.

One of my research thrusts is to learn to act intelligently by building models of actions, planning or reasoning with them, executing the plans, and learning better models and ways to plan with them. There are several interesting questions here such as how to model only what is needed, how to account for errors in the models, how best to combine knowledge and search in planning, how to learn from observation as well as practice, how to exploit hierarchies in planning and learning, and how to transfer knowledge from one domain to a related domain.

Another research thrust is to learn in structured contexts such as natural language processing where the examples have rich internal structure, and are noisy, incomplete, biased, and highly interconnected.

The interesting issues here include learning in expressive representations, learning multiple related concepts simultaneously, constraining learning by explicit prior knowledge, accounting for bias and incompleteness in the data, and integrated learning and reasoning.

Applications of Research
The applications of research include fire and medical emergency response, learning and training systems for expert tasks, intelligent assistants for disabled and elderly, and personalized medicine.