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.
- Amir Abboud: Complexity theory, Hardness in P, Fine-grained complexity
- Miklos Ajtai (emeritus): Complexity theory, cryptography, lattice-based algorithms.
- Ken Clarkson: Matrix computations, computational geometry, algorithms, optimization.
- Ronald Fagin (IBM Fellow): Logic, complexity theory, database principles, reasoning about knowledge, information retrieval.
- Phokion Kolaitis: Logic in computer science, computational complexity, database theory.
- Nimrod Megiddo: Optimization, machine learning.
- Thomas Steinke : Differential privacy, pseudorandomness, adaptive data analysis.
Watson Research Center
- Krzysztof Onak: Sublinear-time algorithms, property testing, streaming, algorithms for massive data sets.
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