Mathematics of AI     

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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.

People

• 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.