Theoretical computer science and machine learning, including convergence rates for Markov chains, sampling algorithms for random combinatorial structures, physics of algorithms, and distributed algorithms for radio-enabled sensor networks
Computer science theory assesses which problems are possible and feasible to solve through theories of computability, undecidability, complexity, reducibility, and approximability.
Algorithms for data mining have a close relationship to methods of pattern recognition and machine learning. Focuses on developing fundamental techniques, prototype systems and applications in databases and data science.