Algorithms, complexity theory, satisfiability, lower bounds, digital libraries, data mining, machine learning, and information technology (IT) education.
Mohan Paturi studies the theoretical underpinnings of computer science, efficient algorithms, and their complexity. He focuses on improved exponential-time algorithms for certain NP-complete (Nondeterministic Polynomial) problems and the interesting connection between efficient algorithms and lower bounds on computational efficiency. Paturi is also an expert in digital libraries, ontologies, and text data mining, and a company he founded (see bio) has created a number of digital libraries for professional societies such as IEEE and ACM. Paturi's other interests include learning theory and machine learning.
Mohan Paturi joined the UCSD faculty in 1986 after a post-doctoral fellowship at Harvard University. He obtained his Ph.D. from Pennsylvania State University in 1985. He is a past recipient of the Jacobs School's Teacher of the Year Award. Paturi founded Parity Computing, a San Diego-based digital library and data mining technology company.