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://julialang.org)
https://github.com/logankilpatrick (https://github.com/logankilpatrick)
https://twitter.com/OfficialLoganK (https://twitter.com/OfficialLoganK)
https://scholar.harvard.edu/logankilpatrick (https://scholar.harvard.edu/logankilpatrick)
RESOURCES:
Anchor: https://anchor.fm/poincare-podcast (https://anchor.fm/poincare-podcast)
Youtube: https://www.youtube.com/watch.v (https://www.youtube.com/watch?v=l2I45GqREgE&list=PLQIbxjHCZG-BUTCC5tdGUjtx23EQGebAw&index=13)
RSS: https://anchor.fm/s/84561ce0/podcast/rss (https://anchor.fm/s/84561ce0/podcast/rss)
Linktree: https://linktr.ee/poincaretrajectories (https://linktr.ee/poincaretrajectories)
Company: https://www.linkedin.com/company/poincaretrajectories/ (https://www.linkedin.com/company/poincaretrajectories/)
21 Jul 2022
Poincaré Podcast #25 - Erik Van Winkle
Here is a conversation with Erik Van Winkle, Operations Lead at DeSci Labs, besides having been part of the core team at Constitution DAO.
DeSci is a pioneering movement dedicated to exploring the capabilities of web3 technologies applied to the scientific ecosystem. We started talking about how DeSci works and the potential of sharing information while maintaining copyright. Then we jumped into the differences between decentralized science and open source, touching on the system design and business models. We then spoke about the story of DeSci and its relevant implementation choices. We finally discussed the new paradigm brought by DeSci, focusing on the ethics of interacting with traditional scientific media.
LINKS:
https://www.linkedin.com/in/erik-van-winkle/ (https://www.linkedin.com/in/erik-van-winkle/)
https://desci.com/ (https://desci.com/)
https://discord.gg/TnTsAUUu (https://discord.gg/TnTsAUUu)
https://t.me/BlockchainForScience (https://t.me/BlockchainForScience)
RESOURCES:
Anchor: https://anchor.fm/poincare-podcast (https://anchor.fm/poincare-podcast)
Youtube: https://www.youtube.com/watch.v (https://www.youtube.com/watch?v=l2I45GqREgE&list=PLQIbxjHCZG-BUTCC5tdGUjtx23EQGebAw&index=13)
RSS: https://anchor.fm/s/84561ce0/podcast/rss (https://anchor.fm/s/84561ce0/podcast/rss)
Linktree: https://linktr.ee/poincaretrajectories (https://linktr.ee/poincaretrajectories)
Company: https://www.linkedin.com/company/poincaretrajectories/ (https://www.linkedin.com/company/poincaretrajectories/)
12 Jul 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://www.researchgate.net/profile/Jean-Marc-Mercier)
https://pypi.org/project/codpy/ (https://pypi.org/project/codpy/)
RESOURCES:
Anchor: https://anchor.fm/poincare-podcast (https://anchor.fm/poincare-podcast)
Youtube: https://www.youtube.com/watch.v (https://www.youtube.com/watch?v=l2I45GqREgE&list=PLQIbxjHCZG-BUTCC5tdGUjtx23EQGebAw&index=13)
RSS: https://anchor.fm/s/84561ce0/podcast/rss (https://anchor.fm/s/84561ce0/podcast/rss)
Linktree: https://linktr.ee/poincaretrajectories (https://linktr.ee/poincaretrajectories)
Company: https://www.linkedin.com/company/poincaretrajectories/ (https://www.linkedin.com/company/poincaretrajectories/)
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