Jesús A. De Loera is a Professor of Mathematics and the Chair of the Graduate Group in Applied Mathematics at the University of California, Davis. His work has touched on a wide range of topics, including geometric and topological combinatorics, convex and discrete geometry, algorithms, combinatorial optimization, and, of course, non-linear algebra. Most recently he has become enchanted by the exciting mathematical questions opened by data science.

In this his first visit to MPI Leipzig he came as co-organizer of MPI "Summer School on Randomness and Learning in Non-Linear Algebra". He was thrilled to discuss with many young enthusiasts how neural networks can help us with non-linear algebra computing. Jesús hopes that, in a not so distant future, software like Macaulay, Sage, etc. will be data-driven, and neural networks will help with tasks such as selecting the best term order of a Gröbner basis.

July 10, 2019

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