Mathematics of AI - overview
This is the homepage of the IBM Mathematics of AI group. The group focuses on developing novel technologies leveraging Mathematics and its intersection with areas such as AI, Physics, and Quantum Computing.
- Lior Horesh (group manager)
- Amir Abboud
Complexity theory, hardness in P, fine-grained complexity.
- Vernon Austel
- Francisco Barahona
Optimization, algorithmic game theory, machine learning.
- Ken Clarkson
Matrix computations, computational geometry, algorithms, optimization.
- Sanjeeb Dash
Discrete optimization, integer and linear programming, with applications in interpretable machine learning.
- Soumyadip Ghosh
Stochastic optimization algorithms for decision making under uncertainty, with applications in machine learning to train large heterogeneous deep neural network models from areas such as speech recognition and natural language processing; Distributionally robust[DR] optimization, for example DR training of AI models.
- Tayfun Gokmen
- Joao Goncalves
- Oktay Gunluk
Mixed-integer programming, combinatorial optimization, multicommodity flows. Modeling, optimization and computation.
- Anshul Gupta
Sparse matrix algorithms, parallel direct sparse solvers, preconditioners for Krylov subspace methods, HPC.
- Wilfried Haensch
- Phokion Kolaitis
Logic in computer science, computational complexity, database theory.
- Vanessa Lopez-Marrero
Computational and applied mathematics, computational science and engineering.
- Yingdong Lu
Computational and applied mathematics, stochastic processes and models.
- Nimrod Megiddo
Optimization, machine learning.
- Tomasz Nowicki
- Krzysztof Onak
Algorithms for big data, massive parallel computation, sublinear algorithms, theory of computer science.
- Parikshit Ram
Large scale algorithms, similarity search, kernel methods, automated machine learning and data science.
- Malte Rasch
Algorithms for and simulation of analog AI hardware, neuromorphic computing, computational neuroscience.
- Mattia Rigotti
Training algorithms for neural networks, neuromorphic computing, computational neuroscience.
- Alberto Sassi
- Mark Squillante
- Thomas Steinke
Differential privacy, pseudorandomness, adaptive data analysis.
- Barry Trager
Symbolic computation, error correcting codes, computational algebraic geometry.
- Yuhai Tu
- Shashanka Ubaru (Goldstine Postdoctoral Fellow)
Machine learning, numerical linear algebra, error correcting codes.
- Chai Wah Wu
Image processing, dynamical systems, synchronization and control of chaotic and networked systems.
Former permanent members: Roy Adler, Ramesh Agarwal, Haim Avron, Nikhil Bansal, Bob Brayton, Andrew Conn, Dan Connors, Don Coppersmith, JP Fasano, Lisa Fleisher, David Gamarnik, John Gunnels, Fred Gustavson, Alan Hoffman, Raya Horesh, Giuseppe Italiano, Satyen Kale, Anju Kambadur, Aida Khajavirad, Tracy Kimbrel, Bruce Kitchens, Laszlo Ladanyi, Jon Lee, Leo Liberti, Konstantin Makarychev, Viswanath Nagarajan, Giacomo Nannicini, Bob Risch, Ted Rivlin, Katya Scheinberg, Baruch Schieber, Mike Shub, Gregory Sorkin, Madhu Sudan, Maxim Sviridenko, Charles Tresser, Andreas Waechter, David Williamson, Shmuel Winograd, Laura Wynter.
Former postdocs (including Goldstine Fellows): Michael Kapralov, Rishi Saket, Shay Solomon.