Poincaré Podcast

Poincaré Trajectories

Research podcast. Infinitely differentiable. Podcast cover art: Order-7 triangular tiling.

  1. 07/26/2022

    Poincaré Podcast #26 - Logan Kilpatrick

    In this episode, we interview Logan Kilpatrick. Logan currently splits his time between a number of professional commitments he is passionate about. He is a full-time Senior Technology Advocate at PathAI, the Developer Community Advocate for the Julia Programming Language, and a Teaching Fellow for Harvard University's Extension School course CSCI E-33A. Logan was previously an Applied Machine Learning Engineer and Software Engineer at Apple as well as the Community Manager for the Julia Programming Language. Additionally, Logan is on the Board of Directors at NumFOCUS and DEFNA. We started talking about the whole Julia Ecosystem with a particular focus on their pandemic response, touching a bit on the hot theme of the Metaverse. We spoke about why someone should use Julia with respect to other programming languages, mentioning some specific packages. We then switch to decentralisation/open source topics analyzing them ideologically, applicationally and financially. We then talked about Julia's future and the amazing interactions among Julia users. Given the background of Logan, we finally spoke about open science with NASA and the application of Julia in the aerospace sector, speaking also about PathAI, Logan's full-time company job. LINKS: https://julialang.org https://github.com/logankilpatrick https://twitter.com/OfficialLoganK https://scholar.harvard.edu/logankilpatrick RESOURCES: Anchor: https://anchor.fm/poincare-podcast Youtube: https://www.youtube.com/watch.v RSS: https://anchor.fm/s/84561ce0/podcast/rss Linktree: https://linktr.ee/poincaretrajectories Company: https://www.linkedin.com/company/poincaretrajectories/

    1h 10m
  2. Poincaré Podcast #24 - Jean-Marc Mercier

    07/12/2022

    Poincaré Podcast #24 - Jean-Marc Mercier

    The guest of this episode is Jean-Marc Mercier. Dr. Jean-Marc studies machine learning, both kernel methods and deep learning, in the context of mathematical finance. We start talking about the differences between kernel methods and deep learning and some history of machine learning, then about the relations between orthogonal polynomials, and deep learning and kernel methods, touching on the application of kernel principal component analysis in aerospace and optimal transport. Dealing with finance, we talk about his vision in AI algorithmic trading and in general more financial applications where AI can be useful. Then we move on modelling approach and assumptions of the observable that brought us to economic bubble formation. We reserve quite a lot of time to talk about "codpy" an open-source python library for machine learning, mathematical finance and statistics of which Jean-Marc is one of the authors. We end up speaking about "codpy" more in detail such as function representation, mesh free methods which bring us to its applicability in fluid dynamics and we conclude with the future expansions of this library. LINKS: https://www.researchgate.net/profile/Jean-Marc-Mercier https://pypi.org/project/codpy/ RESOURCES: Anchor: https://anchor.fm/poincare-podcast Youtube: https://www.youtube.com/watch.v RSS: https://anchor.fm/s/84561ce0/podcast/rss Linktree: https://linktr.ee/poincaretrajectories Company: https://www.linkedin.com/company/poincaretrajectories/

    43 min

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Research podcast. Infinitely differentiable. Podcast cover art: Order-7 triangular tiling.