Patrick Saux

Patrick Saux

PhD Student in Reinforcement Learning

Inria Scool

CRIStAL (CNRS)

Biography

I am a PhD student at Inria Lille (team Scool, formerly SequeL) & CRIStAL (CNRS) under the supervision of Philippe Preux and Odalric-Ambrym Maillard, funded by the B4H project.

My research focuses on reinforcement learning and stochastic bandits in non-stationary and risky environments, with applications to patients follow-up and healthcare planning.

Interests
  • Stochastic Processes
  • Reinforcement Learning
  • Stochastic Bandits
  • Nonasymptotic statistics
  • AI for Health
Education
  • PhD in Computer Science, expected 2023

    Université de Lille and Inria Scool

  • MSc in Machine Learning (MVA), 2020

    École Normale Supérieure Paris-Saclay

  • MSc in Probability and Finance (El Karoui), 2016

    École Polytechnique and Sorbonne Université (Paris VI)

  • Bs in Mathematics (M1), 2015

    École Normale Supérieure Paris-Saclay

  • École Préparatoire MPSI/MP*, 2013

    Lycée Privé Sainte-Geneviève

Experience

 
 
 
 
 
Teaching Assistant
Mar 2021 – Apr 2021 Gif-sur-Yvette
Reinforcement learning class.
 
 
 
 
 
Data Scientist
Jun 2020 – Oct 2020 Paris
Quantitative trading strategies on crude oil based on estimated storage from satellite imaging.
 
 
 
 
 
Associate Strategist
Goldman Sachs
Apr 2016 – Sep 2019 London

Interest rates volatility intern, then credit flow and derivatives.

  • Modelling credit defaults and rates volatility using tools from stochastic processes and Monte Carlo simulation.
  • Implementation using Slang and SecDb proprietary tools, monitoring of real-time, distributed systems.
  • Part of the Illiquids trading desk.
 
 
 
 
 
Researcher Intern
Mar 2015 – Aug 2015 Oxford

Oxford-Man Institute is a research lab focused on mathematics, machine learning and quantitative finance, jointly led by Oxford University and Man AHL. My research was supervised by Terry Lyons.

  • Rough paths theory, an abstract analytical and algebraic framework to study complex and noisy signals.
  • Application of path signature to data analysis.
  • Prediction of seismic events on real-world mining data.

Projects

Stochastic

Stochastic

All sorts of stochastic simulations.

ito-diffusions

ito-diffusions

Libraries for stochastic processes simulation and visualization.

Bond pricer

Bond pricer

Interactive bond pricer, yield calculation and Monte Carlo pricing for callable.

Expander graphs

Expander graphs

Implementation of several expanders and empirical evidence of spectral and connectivity properties. Contribution to the networkx library.

Random matrices

Random matrices

Web app on spectral properties of large random matrices.

Markov Epidemic

Markov Epidemic

Markov stochastic models (SIS, SIR, SEIR…) to describe the evolution of epidemics on a network of connected individuals.

Neural exploration

Neural exploration

Analysis and implementation of neural approximator for contextual bandits and episodic MDP.

Continuity of Graph Embeddings

Continuity of Graph Embeddings

Theoretical and practical continuity of various graph embeddings (eigenmap, deep walk, graph kernels, random walk factorisation…)

Optimal Transport Correlation

Optimal Transport Correlation

Geometric study of correlation matrix via Frechet mean.

Connectivity Loss

Connectivity Loss

Train robust autoencoder with topological loss.

Fractal

Fractal

Fractal generation in Python using Just-In-Time (JIT) compilation and Numba.

Contact

  • firstname[dot]surname[at]inria[dot]fr
  • 40 avenue Halley, Villeneuve d'Ascq, 59650