Hi, I'm a second year PhD student in the
Machine Learning Group
at the University of Geneva, supervised by
I am interested in generative models, geometric methods and applications of ML in the sciences. I'm also part of the
Flowification: Everything is a Normalizing flow
B. Máté*, S. Klein*, T. Golling, F. Fleuret
Deformations of Boltzmann Distributions
B. Máté and F. Fleuret
ML for Physics Workshop, NeurIPS 2022 [arXiv]
SUPA: A Lightweight Diagnostic Simulator for Machine Learning in Particle Physics
A. K. Sinha*, D. Paliotta*, B. Máté*, S. Pina-Otey, J. Raine, T. Golling, F. Fleuret
For a full list, please see my Google Scholar
I started my undergraduate education in engineering at the Budapest University of Technology and Economics
where I quickly gravitated towards control theory and nonlinear dynamics.
After graduation I started a master's in (applied) mathematics at CEU
with the idea of going deeper into control theory.
In my first semester, however, I had an introductory course about differental geometry that made me forget about the applied side
of mathematics and led me to focus on the fields of differential and algebraic geometry and topology.
I wrote my thesis about abelian Higgs-bundles with logarithmic singularities. Next, I spent 2 wonderful years at the University of Hamburg
to understand a bit more about how all this great geometry underpins theoretical physics.
While in Hamburg I also took a course on machine learning which I found so cool that
I am currently doing a PhD in machine learning at the University of Geneva.