Mathematics of AI     


Mathematics of AI - overview

The Mathematics of AI group focuses on developing novel technologies leveraging Mathematics and its intersection with areas such as AI, physics, and quantum computing.


  • Lior Horesh (group manager)
    Manager of the Mathematics of AI group of WRC. He is also an Adjunct Associate Professor in the Computer Science department of Columbia University, teaching graduate level advanced Machine Learning and Quantum Computing courses. His expertise lies at large-scale modeling, inverse problems, tensor algebra, experimental design, machine learning and quantum computing.

  • Amir Abboud
    Complexity theory, hardness in P, fine-grained complexity.
  • Francisco Barahona
    Optimization, algorithmic game theory, machine learning.

  • Clément Canonne (Goldstine Postdoctoral Fellow)
    Property testing with a focus on distribution testing, learning theory, online and approximation algorithms, stochastic processes.

  • Ken Clarkson
    Matrix computations, computational geometry, algorithms, optimization.

  • Sanjeeb Dash
    Discrete optimization, integer and linear programming, with applications in interpretable machine learning.

  • Ronald Fagin (IBM Fellow)
    Logic, complexity theory, database principles, reasoning about knowledge, information retrieval.

  • 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.

  • Phokion Kolaitis
    Logic in computer science, computational complexity, database theory.


  • Songtao Lu


  • 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

  • Barry Trager
    Symbolic computation, error correcting codes, computational algebraic geometry.

  • Yuhai Tu

  • Shashanka Ubaru
    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, Vernon Austel, Haim Avron, Nikhil Bansal, Bob Brayton, Andrew Conn, Dan Connors, Don Coppersmith, JP Fasano, Lisa Fleisher, David Gamarnik, John Gunnels, Fred Gustavson, Wilfried Haensch, Alan Hoffman, Raya Horesh, Giuseppe Italiano, Satyen Kale, Anju Kambadur, Aida Khajavirad, Tracy Kimbrel, Bruce Kitchens, Laszlo Ladanyi, Jon Lee, Leo Liberti, Vanessa Lopez-Marrero, Konstantin Makarychev, Viswanath Nagarajan, Giacomo Nannicini, Bob Risch, Ted Rivlin, Katya Scheinberg, Baruch Schieber, Mike Shub, Gregory Sorkin, Thomas Steinke, 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.