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Hi there 👋

Name's Pierre Jacquier (as French as it gets), I am currently a Technical Lead at Algolux, a Montreal-based company focused on robust perception in all conditions for autonomous vehicles and beyond. With degrees in Mechanical Engineering from France and Canada plus a huge interest in software and cool tech, I'm working hard to reach a point of cross-functional understanding of products, with the ultimate goal of launching some. Computationally curious, and it’s not up for debate.

Drop me a line: meet at pierre dot coffee

Or DM at 🐦

Back to the standard homepage: 🏠

Some coding stuff @pierremtb

YouTube videos @pierrejacquierfr

Medium blog @pierremtb

Journal Publication

Applying AI tools to Computational Mechanics was at the core of my Master's thesis.

Journal of Computational Physics"Non-intrusive reduced-order modeling using uncertainty-aware Deep Neural Networks and Proper Orthogonal Decomposition: Application to flood modeling", Pierre Jacquier, Azzedine Abdedou, Vincent Delmas, Azzeddine Soulaimani. Project page 🔗

Reading List

All-time favs: Shoe Dog, Talking to Strangers, Food Fix.

More in Books 📕.


  1. easy-fit Public

    Parse your .FIT files easily, directly from JS

    JavaScript 60 39

  2. PINNs-TF2.0 Public

    TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).

    Mathematica 152 73

  3. 🚀 More than a simple cross-platform Material Design calculator

    QML 22 6

  4. POD-UQNN Public

    Uncertainty Quantification in the POD-NN framework

    Python 15 10

  5. CastMenu Public

    A macOS Menu Bar applet to control playback on Chromecast-enabled devices

    Python 1

  6. TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks (UQPINNs).

    Python 13 21

142 contributions in the last year

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Contribution activity

August 2022

pierremtb has no activity yet for this period.

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