Poincaré Podcast Poincaré Trajectories

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/

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://desci.com/
https://discord.gg/TnTsAUUu
https://t.me/BlockchainForScience

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/

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/

Joel Rosenfeld is Assistant Professor at the University of South Florida and his research covers machine learning, kernel methods, approximation theory, function analysis and many more.
After a brief introduction about career strategies and issues caused by the pandemic, we talked about kernel functions with many digressions, such as the statistical description of dynamical systems and relationships between different spaces. We discuss the occupation of kernel functions and occupation measures (a branch of control theory) then we go on approximation of bounded operator using the densely defined operator and alternative approaches when operators are discontinuous in the domain. In the end, you'll find that sometimes one tries to make links between those subjects and dynamical systems and chaos, not all of them are congruent but is really interesting to hear the reasons.

LINKS:
https://scholar.google.com/citations?user=pqsepdcAAAAJ&hl=en
https://www.thelearningdock.org
https://youtube.com/c/ThatMathThing

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/

Gary Froyland is a professor at the School of Mathematics and Statistics at the University of New South Wales, and his research includes dynamical systems and optimization.
We started talking about research life and the struggle with our limits to pursue it, and then we went technical arguing about applied mathematics. In particular, we touched on the chaotic nature of climate and ocean science and the determination of some geometric structures (coherent) in it. We moved on to linear operators in functional analysis for time-varying dynamical systems and transfer operators both in discrete and continuous time. We discussed the software he developed (GAIO) and its applicability to the aerospace sector concluding by discussing the applicability of chaotic maps (e.g. Anosov), the periodicity of a system and the usage of the operators in multiscale systems (e.g. deterministic chaos and turbulence, fractals structures).

LINKS:
https://scholar.google.com/citations?user=uAIy_MMAAAAJ&hl=en
https://web.maths.unsw.edu.au/~froyland/
https://research.unsw.edu.au/people/professor-gary-froyland

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/

Miles Cranmer is a Ph.D. candidate at the University of Princeton. He is working on the interplay between astrophysics and AI.
We talk about Symbolic Regression, Genetic Programming, and the peculiarities of Julia, the programming language, comparing it with C++ and Python.
We then talk about his approach in studying new programming language features and about how to balance exploration versus exploitation. We largely discuss his work at Deepmind, outlining graph- and Lagrangian-Neural Networks, particularly in relation to the ability to investigate chaotic motion in dynamical systems.

LINKS:
https://astroautomata.com
https://web.astro.princeton.edu/people/miles-cranmer
https://github.com/MilesCranmer

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/