Portrait of Chris Pal

Chris Pal

Core Academic Member
Canada CIFAR AI Chair
Full Professor, Polytechnique Montréal, Department of Computer Engineering and Software Engineering
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Deep Learning

Biography

Christopher Pal is a Canada CIFAR AI Chair, full professor at Polytechnique Montréal and adjunct professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal. He is also a Distinguished Scientist at ServiceNow Research.

Pal has been involved in AI and machine learning research for over twenty-five years and has published extensively on large-scale language modelling methods and generative modelling techniques. He has a PhD in computer science from the University of Waterloo.

Current Students

Collaborating researcher - Formerly McGill University (but ending)
Collaborating researcher - McGill University
Principal supervisor :
Master's Research - Université de Montréal
PhD - Polytechnique Montréal
Collaborating Alumni - McGill University
Principal supervisor :
PhD - Université de Montréal
Principal supervisor :
PhD - Polytechnique Montréal
Master's Research - Université de Montréal
Co-supervisor :
Collaborating Alumni - Polytechnique Montréal
PhD - Polytechnique Montréal
Postdoctorate - McGill University
Master's Research - Polytechnique Montréal
PhD - Université de Montréal
Co-supervisor :
Master's Research - Concordia University
Co-supervisor :
Master's Research - Université de Montréal
PhD - Université de Montréal
PhD - Université de Montréal
PhD - Polytechnique Montréal
PhD - Polytechnique Montréal
PhD - École de technologie suprérieure
PhD - Université de Montréal
Principal supervisor :
Postdoctorate - HEC Montréal
Principal supervisor :
PhD - Polytechnique Montréal
Principal supervisor :
PhD - McGill University
Principal supervisor :
PhD - Polytechnique Montréal
PhD - Université de Montréal

Publications

Theano: A Python framework for fast computation of mathematical expressions
Rami Al-rfou'
Amjad Almahairi
Christof Angermüller
Frédéric Bastien
Justin S. Bayer
A. Belikov
A. Belopolsky
J. Bergstra
Josh Bleecher Snyder
Paul F. Christiano
Marc-Alexandre Côté
Myriam Côté
Julien Demouth
Sander Dieleman
M'elanie Ducoffe
Ziye Fan
Mathieu Germain
Ian J. Goodfellow
Matthew Graham
Balázs Hidasi
Arjun Jain
S'ebastien Jean
Kai Jia
Mikhail V. Korobov
Vivek Kulkarni
Pascal Lamblin
Eric P. Larsen
S. Lee
Simon-mark Lefrancois
J. Livezey
Cory R. Lorenz
Jeremiah L. Lowin
Qianli M. Ma
R. McGibbon
Mehdi Mirza
Alberto Orlandi
Colin Raffel
Daniel Renshaw
Matthew David Rocklin
Markus Dr. Roth
Peter Sadowski
John Salvatier
Jan Schlüter
John D. Schulman
Gabriel Schwartz
Iulian V. Serban
Samira Shabanian
Sigurd Spieckermann
S. Subramanyam
Gijs van Tulder
Joseph P. Turian
Sebastian Urban
Dustin J. Webb
M. Willson
Lijun Xue
Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficie… (see more)ntly. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.