Before starting my PhD at ETH Zurich, I graduated with a first-class honours in Mathematical Statistics from the University of Cambridge . I also studied one year at Sorbonne University with a Paris Graduate School of Mathematics scholarship from the Fondation Science Mathématique de Paris (FSMP). The FSMP fondation is an excellence network in mathematics and fundamental computer science in Paris, founded by CNRS, ENS, Univ. Pierre et Marie Curie, Univ. Paris-Diderot, and the 4 Mathematics Chairs of Collège de France, involving 5 Fields medalists, 20 members of Academy of Science, and many recipients of national and international prizes.
I obtained my Bachelor degree from the Univerité Libre de Bruxelles , graduating with the best results of my promotion.
My main interests include quantitative finance, algorithmic trading, machine learning, data mining, big data, time series analysis and applied statistics.
– Davy Paindaveine, Professor of Statistics at Université Libre de Bruxelles
Balint is one of the very best undergraduate students I could interact with in the last ten years. In my view, he shows all the qualities needed to produce research of the highest level.
PhD in Machine Learning applied to Mathematical Finance, 2018
Master of Advanced Studies in Mathematical Statistics (Part III of the Mathematical Tripos), 2018
University of Cambridge
Master 1 in Mathematics, 2017
Sorbonne University (formerly University Pierre and Marie Curie)
BSc in Mathematics, 2016
Université Libre de Bruxelles
Solving problems allowed me to approach the activity of the researcher and to perceive mathematics as a living
science. Additionally, because their resolution often requires more than routine, problems develop the ability to
control new situations. During my academic career, I had the privilege to receive several sholarships and awards from leading academic insitutions and private companies.
I was teaching instructor for the following courses:
Information on all courses and Q&A sessions of Group 3 of D-MATH can be found on the group homepage .
The link to the homepage of MFF course can be found here .
An alternative solution to exercise 1 part a) from problem sheet 1 can be found here .
Lecture notes for the course Probability Theory that might also be of use to some of you can be obtained in an electronic form on the homepage of the course with the password you received by mail from the course coordinator.