Zak Mhammedi

Zak Mhammedi

Postdoctoral Associate

Massachusetts Institute of Technology

About Me

I am a Postdoctoral Associate at MIT working with Sasha Rakhlin on theoretical aspects of Reinforcement Learning among other exciting topics. Before that I was a PhD student in computer science at the Australian National University, advised by Bob Williamson and Wouter Koolen.

Interests

  • Online Learning
  • Stochastic Optimization
  • Reinforcement Learning
  • Statistical Learning (Generalization Bounds)

Education

  • PhD in Computer Science, 2021

    Australian National University

  • MPhil in Computer Science, 2017

    The University of Melbourne

  • MEng in Mechanical Engineering, 2015

    The University of Melbourne

  • BSc, 2012

    Ecole Centrale Paris

Publications

${}^\dagger$About 1% of submissions at NeurIPS receive an Orale.

${}^\star$About 3% of submissions at NeurIPS receive a spotlight.

In all papers, authors are ordered according to their contribution.

Experience

 
 
 
 
 

Applied Scientist Intern

Amazon Berkeley

Jul 2021 – Oct 2021 Remote
Developed new machine learning techniques for customer query auto-complete.
 
 
 
 
 

Research Vendor

Microsoft Research NYC (via Aquent AUS)

Apr 2021 – Jun 2021 Remote
Developed and implemented online parameter-free algorithms such as FreeGrad to be used in a real-world Contextual Bandits application.
 
 
 
 
 

Research Intern

Microsoft Research

Feb 2020 – Jun 2020 New York, USA
Worked and published research on a problem of continuous control with rich observations.
 
 
 
 
 

Research Intern

Centrum Wiskunde & Informatica (CWI)

Jan 2019 – Mar 2019 Amsterdam, Netherlands
Worked on developing a new and improved PAC-Bayesian generalization bound.
 
 
 
 
 

Research Visit

Centrum Wiskunde & Informatica (CWI)

May 2018 – Aug 2018 Amsterdam, Netherlands
Worked on adding Lipschitz adaptivity to certain second-order adaptive online learning algorithms.
 
 
 
 
 

Research Assistant

CSIRO

Jul 2016 – Dec 2016 Tasmania, Australia
Published research on an efficient parametrization of orthogonal recurrent neural networks.
 
 
 
 
 

Research Intern

IBM Research

Jul 2015 – Oct 2015 Melbourne, Australia
Worked on developing a model for the theoretical spread of citrus greening in Australia. Implemented the model in C++ and performed simulations.

Services

Program Commitee: COLT 2021

Reviewer: COLT 2019, COLT 2020, NeurIPS 2020, ICML 2020, JMLR

Teaching

 
 
 
 
 

Tutor

Australian National University

Aug 2017 – Oct 2018 Canberra, Australia
Taught the Information Theory course COMP2610/COMP6261.
 
 
 
 
 

Maths Tutor

Simply Maths

Oct 2013 – Oct 2014 Melbourne, Australia
Tutored secondary-level mathematics students.