Monty (Montacer) Essid is an adjunct professor at NYU and a Quant. He earned his PhD in Mathematics from the Courant Institute of Mathematical Sciences, NYU. His research interests include theoretical and numerical aspects of Optimal Transport; generalizations, connections with stochastic optimal control, optimization algorithms, mean field games, and applications in Finance and Data Science.
Theoretical aspects: Regularized Optimal Transport (e.g. entropic, quadratic) both in discrete and continuous time, Schrodinger Bridges and extensions, Martingale Optimal Transport.
Numerical aspects: Regularization on graphs, sample based reformulations for data science applications (e.g. adaptive optimal transport), connections with GAN, non-linear regression, reinforcement learning.
Ph.D. in Mathematics, 2018
Courant Institute of Mathematical Sciences
M.Sc. in Applied Math and Applied Physics, 2013
Columbia University
Diplôme d'Ingénieur, 2013
Ecole Centrale Paris
An implicit gradient-descent procedure for minimax problems (preprint, with E.G. Tabak and G. Trigila)
Small feature change detection through Sample-Based Optimal Transport (preprint, with E.G. Tabak and D. Laefer)
Nov 3, 2017, Eastern Conference on Mathematical Finance
Feb 27, 2017, Bloomberg Quant Seminar
Nov 16, 2016, NYU Tandon FRE Department Seminar
Mar 15, 2016, Courant Institute Department Seminar
I was/am an instructor for the following classe at the Courant Institute
I was/am an instructor for the following classes at NYU Tandon
I was a TA/Grader/Recitation leader for the following classes at the Courant Institute