## 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.

## People

**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**Differential privacy, pseudorandomness, adaptive data analysis.**Thomas Steinke****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.