Algorithms and Theory - overview
Our dual objective is to pursue basic research on a broad range of theoretical topics and to impact real-world issues by applying our expertise to solving problems for IBM and its clients.
We do basic research in a number of areas of theoretical computer science, including approximation algorithms, combinatorics, complexity theory, computational geometry, distributed systems, learning theory, online algorithms, cryptography and quantum computing.
IBM researchers have access to an extensive array of challenging problems that motivate innovative solutions and, at the same time, constantly push the theoretical state-of-the-art with the development of new algorithms and new optimization techniques. We provide innovative, custom solutions to business and industrial problems that are at the boundaries of what can be solved today.
- Ken Clarkson: Computational geometry, design and analysis of algorithms, optimization.
- Ronald Fagin (IBM Fellow): Logic, complexity theory, database principles, reasoning about knowledge, information retrieval.
- Vitaly Feldman: Learning theory, computational models of the brain, complexity theory.
- T. S. Jayram: Complexity theory, algorithms for massive data sets.
- Phokion Kolaitis: Logic in computer science, computational complexity, database theory.
- Nimrod Megiddo: Optimization, machine learning.
- Thomas Steinke (post-doctoral researcher): differential privacy, pseudorandomness, adaptive data analysis.
- David Woodruff: Algorithms, complexity theory, cryptography.
Watson Research Center
- Krzysztof Onak: Sublinear-time algorithms, property testing, streaming, algorithms for massive data sets.
- Baruch Schieber: Optimization, approximation algorithms.
- Shay Solomon (Goldstine Fellow): Design and analysis of algorithms and data structures.
IBM Research – India
- Venkat Chakaravarthy: Theory of computing, complexity theory.
- Vinayaka Pandit: Algorithms, combinatorial optimization, mathematical programming.
Rishi Saket: Approximability of problems in Combinatorial Optimization and Computational Learning
- Yogish Sabharwal: High performance computing, computational geometry, approximation algorithms.
Last updated on May 2, 2017