About me

I am Patrick J. Mineault. I’m an independent scientist and neural data scientist.

I did a PhD a visual neuroscience at McGill with Dr. Chris Pack and a postdoc at UCLA with Dr. Dario Ringach. I studied how the brain iteratively processes visual stimuli to produce meaningful representations which can drive behaviour. I worked at Google, studying at how people view and interact with webpages. I was also a Brain Computer Interface engineer at Facebook Reality Labs, building a BCI that allows you to type with your brain. This is how I got a job in industry.

I helped run the first edition of Neuromatch Academy as CTO. NMA is an online summer school in computational neuroscience, we launched our inaugural edition, which brought together 191 TAs and 1700 students from 60 countries learning computational neuroscience full-time for 3 weeks. You can see all of the materials we produced freely here.

I’m always happy to take on new, manifestly important projects that are nearly impossible. I am available as a speaker. My email is open.

Contact

Email: patrick DOT mineault AT gmail DOT com
LinkedIn
CV [PDF, 2 pages]
Twitter: @patrickmineault
Neurotree

Education

  • PhD (2014), Integrated Program in Neuroscience, McGill University. Thesis: Parametric modeling of visual cortex at multiple scales. [PDF]
  • B.Sc., Physics and Mathematics (2007). McGill University

Papers

[Always up to date list on Google Scholar]

Patent

Work

2017-2019 – Brain Computer Interface Engineer at Building 8 @ Facebook

Building a brain-computer interface that will allow people to type with their thought. Some press coverage: [1] [2] [3]

2015-2017 – Software Engineer and data scientist at Google

Helping organize the world’s information and make it universally accessible and useful. Psychophysics at scale.

2014-2015 – Postdoctoral researcher at the Dario Ringach lab, David Geffen School of Medicine, UCLA.

I studied representation in the visual cortex of mice, looking at how stimuli are differentially encoded during locomotion. I developed and applied Bayesian decoding methods to better understand how simple changes in gain can lead to adaptive neural codes that conserve energy in times of lethargy and perform better during times of high demand.

implemented and improved signal processing methods for 2-photon imaging, in particular constrained matrix factorization to extract meaning out of recordings in the photon shot-noise regime.

2008-2014 – Doctoral researcher at the Pack Lab, McGill University, Montreal.

I studied visual representation and decision making at the level of single neurons, populations of neurons, and psychophysical observers. I refined and applied systems identification methods to understand how humans classify noise images; how the brain processes optic flow; and what happens to visual representation around the time of saccades. I worked on signal processing methods for local field potentials, applicable to neural prosthetics and BCI. [PhD thesis].

Speaking experience

  • Panelist, OpenMR Benelux (2021) – What constitutes clean, scientific code?
  • Speaker, BrainHack Montreal (2021) – Writing code with confidence with test-driven development
  • Guest lecturer, Harvard PhD Program in Neuroscience (2021) –  Structuring code and data
    workshop
  • Speaker, Neuromatch Academy, How We Built This (2020) – Professional development at NMA

Teaching experience

  • Section leader, Code in Place, Stanford (2020) – introductory college-level CS class.
  • Teacher, Champlain College, Programming for AR/VR (2020) – introductory college-level CS class.
  • Guest Lecturer, NEUR 603, Computational Neuroscience (2010) – graduate-level class
    Class Instructor: Dr. Christopher Pack
    Lecture title: Generalized Linear and Additive Models in neuroscience.

Distinctions

  • Postdoc grant, Fonds de recherche Québec, Nature et Technologies (FRQNT), 2015 – refused.
  • Top 5% Kaggle submission, AXA driver telematics, March 2015.
  • PhD scholarship, Fond de recherche Québec, Nature et Technologies (FRQNT), 2011-2013.
  • Selected for Cold Spring Harbour Lab 2012 Computational Vision class (< 20% accepted).
  • Graduated with joint honours in Physics and Mathematics, McGill University, 2007

About this blog

I have been writing about neuroscience, programming, data science on this blog since 2008.

Licensing

All of the code snippets on this blog are authored by me – unless otherwise indicated – and are licensed under an MIT license, which means you can use them without asking me about it. All the text is CC-BY-2.0, which means you can include it in a textbook, a paper, or repost it elsewhere and potentially modify without asking permission as long as you maintain an attribution line.

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