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Nick Frosst

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Co-Founder
Toronto, Ontario, Canada

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Résumé


Jobs verified_user 0% verified
  • Cohere
    co:founder
    Cohere
    Jan 2020 - Current (6 years 8 months)
  • Google
    RSWE - Google Brain
    Google
    Feb 2016 - Jan 2020 (4 years)
    I work on neural network research on Geoffrey Hinton's Toronto Brain Team.
  • Google
    Machine Learning Intern
    Google
    Jun 2015 - Aug 2015 (3 months)
    At Google I worked on the machine learning team. I was responsible for switching the team's main ML model from Boosting Trees to Neural Networks. This required working extensively with Google Brain and customizing it for our use case. Google Brain is an incredibly powerful neural network package and it was an incredible experience to work with and contribute to it.
  • Amazon
    SDE Intern
    Amazon
    May 2014 - Aug 2014 (4 months)
    I work on S3, a cloud storage service provided by AWS. The most enjoyable aspect of the work is the sheer scale at which amazon functions. S3 is a behemoth and it is thoroughly enjoyable to try to tame it..
  • University of Toronto
    Teaching Assistant
    University of Toronto
    Jan 2014 - Jan 2016 (2 years 1 month)
    My work has a teaching assistant has been both educational and rewarding. I am assisting with an introductory class for students with no prior math or computer knowledge. This has provided me with a unique opportunity to explain and explore ideas to people who have never thought about them before. To make matters even more interesting, the class is being taught in Racket! I also TA a second year computational theory course.
  • T
    Researcher
    Tsotsos Computer Vision Lab York University
    Sep 2013 - Dec 2015 (2 years 4 months)
    I work on visual saliency prediction algorithms, focusing on benchmarking and optimization. Vision science is a relatively new field for me, but i am thoroughly enjoying it thus far.
  • L
    Freelance Consultant
    Lavasoft
    May 2013 - Sep 2013 (5 months)
    I did a variety of consulting for Lavasoft, including work on the integration of a project with some existing open source software, and work on improving the corporate image of the company.
  • S
    Bartender, Waiter, and Board Game Instructor
    Snakes and Lattes
    Nov 2012 - Jan 2014 (1 year 3 months)
    My work at Snakes and Lattes, a board game cafe, is motivated by my love of board games, beer, coffee, and meeting people. Although it is removed from computer science it has been a very enjoyable and rewarding experience.
  • AlcatelLucent
    CLI Developer
    AlcatelLucent
    May 2012 - Sep 2013 (1 year 5 months)
    Over the course of the two summer terms during which I worked at Alcatel-Lucent, I was predominantly focused on writing perl tools that generate C code during the build process in order to eliminate the need for legacy code and to automate some of the work of the designers. I also worked on integrating this tool with and speeding up the build process using Make.
Education verified_user 0% verified
  • University of Toronto
    Bachelor's of Science, Double Majoring in Computer Science and Cognitive Science
    University of Toronto
    Jan 2011 - Jan 2015 (4 years 1 month)
Projects (professional or personal) verified_user 0% verified
  • P
    Pulse (1st Place at UofTHacks)
    Jan 2015 - Current (11 years 8 months)
    Pulse is a haptic feedback metronome for Android Wear smart watches. Pulse can talk to other smart watches running the app, and make sure all the users are synced to the same tempo.
  • F
    Frye Applications; Convertr
    Jul 2011 - Aug 2011 (2 months)
    Convertr was a first foray into app development. A close friendm Richard Ye, and I spent our free time during one summer playing around with the idea of intuitive conversion tools. The result was surprisingly usable.
Publications verified_user 0% verified
  • F
    On computational modeling of visual saliency: Examining what’s right, and what’s left,
    FEB
    In the past decade, a large number of computational models of visual saliency have been proposed. Recently a number of comprehensive benchmark studies have been presented, with the goal of assessing the performance landscape of saliency models under varying conditions. This has been accomplished by considering fixation data, annotated image regions, and stimulus patterns inspired by psychophysics. In this paper, we present a high-level examination of challenges in computational modeling of visual saliency, with a heavy emphasis on human vision and neural computation. This includes careful assessment of different metrics for performance of visual saliency models, and identification of remaining difficulties in assessing model performance. We
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