CV

Download cv_mastane_achab.pdf

Education

• M.S. in applied maths, Ecole polytechnique, 2012-2015
• M.S. in machine learning (M2 MVA), Ecole normale superieure de Cachan, 2015-2016
• Ph.D. in machine learning, LTCI lab, Telecom Paris, 2016-2020
  • Thesis: Ranking and risk-aware reinforcement learning
  • Supervisors: Stephan Clémençon, Aurélien Garivier and Anne Sabourin

Work experience

• Spring and summer 2015: Research internship
  • SequeL team, INRIA Lille-Nord Europe, Villeneuve-d’Ascq, France
  • Supervisors: Michal Valko and Gergely Neu
• Spring and summer 2019: PhD internship
  • Google DeepMind, London, UK
  • Supervisor: Bilal Piot
• 2020-2021: Postdoc
  • AI group, DTIC, Universitat Pompeu Fabra, Barcelona, Spain
  • Supervisor: Gergely Neu
• 2022-2024: Researcher
  • Technology Innovation Institute (TII), Abu Dhabi, UAE

Skills

• Python
  • PyTorch
  • TensorFlow

Publications

Max K-armed bandit: on the ExtremeHunter algorithm and beyond

Ranking data with continuous labels through oriented recursive partitions

Profitable bandits

Dimensionality reduction and (bucket) ranking: a mass transportation approach

Weighted empirical risk minimization: sample selection bias correction based on importance sampling

[PhD thesis] Ranking and risk-aware reinforcement learning

Robustness and risk management via distributional dynamic programming

Checkered Regression

Distributional deep Q-learning with CVaR regression

A Nested Matrix-Tensor Model for Noisy Multi-view Clustering

One-Step Distributional Reinforcement Learning

Beyond Log-Concavity: Theory and Algorithm for Sum-Log-Concave Optimization

Investigating Regularization of Self-Play Language Models

A Bregman firmly nonexpansive proximal operator for baryconvex optimization