Download Curriculum Vitae: Ian Goodfellow

Curriculum Vitae: Ian Goodfellow
[email protected]
http://www-etud.iro.umontreal.ca/~goodfeli/
410-4600 Chemin Queen Mary
Montréal, QC H3W 1W6
(514) 224-9283
(As I am often traveling internationally, e-mail is my preferred method of contact)
EDUCATION
• Ph.D. candidate, DIRO, Université de Montréal. Specialization in Machine Learning. GPA: 4.2
o Expected graduation: summer 2014
o Key courses: Learning algorithms, measure theory+Lebesgue integration
• M.Sc., Computer Science, Stanford University. GPA: 3.93. Specialization in Artificial
Intelligence. Graduated December 2009.
o Key courses: graphical models, theory of graphical models, machine learning, natural
language processing, computer vision, convex optimization, modern applied statistics (,
numerical methods
• B.Sc., Computer Science, Stanford University. GPA: 3.84. Graduated December 2009.
o Key courses: operating systems, algorithms, compilers, AI, graphics
RECENT WORK AND RESEARCH EXPERIENCE
Google: Mountain View, CA - Research Scientist. To commence Summer ’14. Supervisor: Jeff Dean
I have signed an offer to join the deep learning infrastructure team. I will divide my time
between deep learning applications, basic research, and infrastructure development.
Google: Mountain View, CA - Software Engineering intern. Summer ’13. Intern host: Julian Ibarz
Worked on the StreetSmart team using deep learning + computer vision on StreetView imagery.
LISA at Université de Montréal: Montréal, QC – Ph.D. candidate. Summer 2010-present. Thesis
advisors: Yoshua Bengio and Aaron Courville
Invented a new machine learning model (maxout networks), a new training algorithm (multiprediction training for DBMs), and a fast parallel inference algorithm for spike and slab sparse
coding. Used these techniques to improve the state of the art on several computer vision
benchmarks including Google’s Street View House Numbers dataset.
Université de Montréal: Montréal, QC – Teaching assistant. Winter 2013. Supervisor: Yoshua Bengio
Lectured on variational inference, maxout networks, and machine learning software. Provided
software support for class projects.
Stanford University: Stanford, CA –Research / course assistant. 2007-2010. Supervisor: Andrew Ng
Course assistant for courses including AI, machine learning, and compilers. Research assistant
on projects involving deep learning and computer vision.
Willow Garage: Menlo Park, CA –Intern. Summer 2009. Supervisor: Caroline Pantofaru
Researched indoor person detection and contributed to an autonomous robot waiter application
using the PR2 robot. Wrote rxgraph2, a robot software monitoring utility released with ROS.
RECENT AWARDS
• 2013 Google PhD Fellowship in Deep Learning. $33,000 + tuition and fees for up to two years.
• Winner of 2011 NIPS Workshop on Challenges in Learning Hierarchical Models: Transfer
Learning Challenge (won using my spike-and-slab sparse coding inference algorithm)
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Pascal2 Best UTL Challenge (Phase 2) Paper Award, 2011
Honorable Mention, National Science Foundation Graduate Research Fellowship Program,
2010
Bours du DIRO, Université de Montréal, 2010. $10,000
HONOR SOCIETY MEMBERSHIP
• Tau Beta Pi, Stanford chapter. Engineering honor society. Membership based on class rank
among engineering students and public service.
• Phi Beta Kappa, Stanford chapter. Membership based on GPA and breadth of courses taken.
SELECTED CONFERENCE PAPERS (full list on website)
“Multi-prediction deep Boltzmann Machines.” Ian J. Goodfellow, Mehdi Mirza, Aaron Courville, and
Yoshua Bengio. NIPS 2013. (Previously an oral presentation at the ICLR 2013 workshops)
“Maxout Networks.” Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville, and
Yoshua Bengio. ICML 2013. (Full length oral presentation)
“Measuring invariances in deep networks.” Ian J. Goodfellow, Quoc V. Le, Andrew M. Saxe, Honglak
Lee and Andrew Y. Ng. NIPS 2009.
JOURNAL PAPERS
“Scaling Spike-and-Slab Models for Unsupervised Feature Learning.” IEEE Transactions on Pattern
Analysis and Machine Intelligence, special issue on deep learning. Ian J. Goodfellow, Aaron Courville,
and Yoshua Bengio.
"Unsupervised and Transfer Learning Challenge: a Deep Learning approach." Journal of Machine
Learning Research, Volume 27. G. Mesnil, Y. Dauphin, X. Glorot, S. Rifai, Y. Bengio, I. Goodfellow,
E. Lavoie, X. Muller, G. Desjardins, D. Warde-Farley, P. Vincent, A. Courville, and J. Bergstra.
OPEN SOURCE CONTRIBUTIONS
• github account: https://github.com/goodfeli
• Wrote most of Pylearn2 (LISA’s machine learning library)
• Core developer of STAIR Vision Library
• Contributor to theano and ROS
MACHINE LEARNING COMMUNITY INVOLVEMENT
• Co-organizer of the ICML 2013 Workshop on Representation Learning.
• Reviewer for JMLR, ICML 2013-4, NIPS 2013, UAI 2013, ICLR 2013, + many IEEE journals
COMPUTER SCIENCE / SOFTWARE ENGINEERING SKILLS
• Machine learning skills: Experienced with using, implementing, and analyzing most textbook
machine learning algorithms. Experienced with developing new machine learning techniques.
• APIs, libraries, software frameworks: NumPy / SciPy, Theano, nVidia CUDA, boost, OpenGL,
OpenGL Shader Language, OpenCV, Eigen, gsl, Mac OS X Carbon API, HTML
• Programming languages: experienced in C/C++, Java, Python, MATLAB, R, JavaScript
References available upon request.