Quant Guide 2020: Sorbonne University/Ecole Polytechnique

The Master’s in Probability and Finance is jointly conducted by two French institutions: Ecole Polytechnique, an engineering college and grande école in the Parisian suburb of Palaiseau; and Sorbonne University, a Paris-based school established in 2018 via the merging of Paris-Sorbonne University and Pierre & Marie Curie University. The latter institution previously hosted the programme.

Appropriately for a collaborative degree, it is jointly led by three academic directors: Mathieu Rosenbaum, chair of analytics and models for regulation at Ecole Polytechnique; Emmanuel Gobet, professor of applied mathematics at the same; and Gilles Pagès, professor in the field of probability and random models group at Sorbonne University. The mathematician Nicole El Karoui, one of the leading experts on credit risk and credit derivatives pricing, is also an instructor on the master’s: she teaches a joint class, alongside Gobet, in stochastic processes and derivatives.

The one-year programme is an M2 degree – that is, a master’s designed for students who already hold a master’s. It is structured across two semesters, with an internship that begins in April. Last year’s cohort is above the average size for the quant guide population as a whole, with 75 students. The number of teaching staff is also fairly high, however – there are 34 instructors on the programme, 10 of whom have an industry affiliation.

The programme is popular among offer holders: of the 83 applicants that received offers for the latest course, 75 accepted – a yield of 90%. Successful candidates join the programme in September, unless they elect to complete a series of complementary courses beginning in the May of a given year. These refreshers include classes in statistics, probability, C++, and numerical methods.

Once the programme begins, students tackle coursework in optimisation and stochastic control; machine learning, neural networks and deep learning; Monte Carlo algorithms for Markov chains and particle methods; and parallel programming on GPU devices for big data. 

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