web01.fireside.fm Sat, 25 Apr 2026 19:22:32 -0500 Fireside (https://fireside.fm) Vanishing Gradients - Episodes Tagged with “Python” https://vanishinggradients.fireside.fm/tags/python Thu, 25 May 2023 08:00:00 +1000 A podcast about all things data, brought to you by data scientist Hugo Bowne-Anderson. It's time for more critical conversations about the challenges in our industry in order to build better compasses for the solution space! To this end, this podcast will consist of long-format conversations between Hugo and other people who work broadly in the data science, machine learning, and AI spaces. We'll dive deep into all the moving parts of the data world, so if you're new to the space, you'll have an opportunity to learn from the experts. And if you've been around for a while, you'll find out what's happening in many other parts of the data world. en-us episodic a data podcast with hugo bowne-anderson Hugo Bowne-Anderson A podcast about all things data, brought to you by data scientist Hugo Bowne-Anderson. It's time for more critical conversations about the challenges in our industry in order to build better compasses for the solution space! To this end, this podcast will consist of long-format conversations between Hugo and other people who work broadly in the data science, machine learning, and AI spaces. We'll dive deep into all the moving parts of the data world, so if you're new to the space, you'll have an opportunity to learn from the experts. And if you've been around for a while, you'll find out what's happening in many other parts of the data world. no data science, machine learning, AI Hugo Bowne-Anderson hugobowne@hey.com Episode 18: Research Data Science in Biotech https://vanishinggradients.fireside.fm/18 83afeb64-21ec-4828-bf96-75a08c710391 Thu, 25 May 2023 08:00:00 +1000 Hugo Bowne-Anderson full 1 Hugo Bowne-Anderson Machine learning, deep learning, Bayesian inference for drug discovery, OSS, and accelerating discovery science to the speed of thought! 1:12:42 no Hugo speaks with Eric Ma about Research Data Science in Biotech. Eric leads the Research team in the Data Science and Artificial Intelligence group at Moderna Therapeutics. Prior to that, he was part of a special ops data science team at the Novartis Institutes for Biomedical Research's Informatics department. In this episode, Hugo and Eric talk about What tools and techniques they use for drug discovery (such as mRNA vaccines and medicines); The importance of machine learning, deep learning, and Bayesian inference; How to think more generally about such high-dimensional, multi-objective optimization problems; The importance of open-source software and Python; Institutional and cultural questions, including hiring and the trade-offs between being an individual contributor and a manager; How they’re approaching accelerating discovery science to the speed of thought using computation, data science, statistics, and ML. And as always, much, much more! LINKS Eric's website (https://ericmjl.github.io/) Eric on twitter (https://twitter.com/ericmjl) Vanishing Gradients on YouTube (https://www.youtube.com/channel/UC_NafIo-Ku2loOLrzm45ABA) Cell Biology by the Numbers by Ron Milo and Rob Phillips (http://book.bionumbers.org/) Eric's JAX tutorials at PyCon (https://youtu.be/ztthQJQFe20) and SciPy (https://youtu.be/DmR36wtel4Y) Eric's blog post on Hiring data scientists at Moderna! (https://ericmjl.github.io/blog/2021/8/26/hiring-data-scientists-at-moderna-2021/) machine learning, AI, data science, open source, python, biotech Hugo speaks with Eric Ma about Research Data Science in Biotech. Eric leads the Research team in the Data Science and Artificial Intelligence group at Moderna Therapeutics. Prior to that, he was part of a special ops data science team at the Novartis Institutes for Biomedical Research's Informatics department.

In this episode, Hugo and Eric talk about

  • What tools and techniques they use for drug discovery (such as mRNA vaccines and medicines);
  • The importance of machine learning, deep learning, and Bayesian inference;
  • How to think more generally about such high-dimensional, multi-objective optimization problems;
  • The importance of open-source software and Python;
  • Institutional and cultural questions, including hiring and the trade-offs between being an individual contributor and a manager;
  • How they’re approaching accelerating discovery science to the speed of thought using computation, data science, statistics, and ML.

And as always, much, much more!

LINKS

]]> Hugo speaks with Eric Ma about Research Data Science in Biotech. Eric leads the Research team in the Data Science and Artificial Intelligence group at Moderna Therapeutics. Prior to that, he was part of a special ops data science team at the Novartis Institutes for Biomedical Research's Informatics department.

In this episode, Hugo and Eric talk about

  • What tools and techniques they use for drug discovery (such as mRNA vaccines and medicines);
  • The importance of machine learning, deep learning, and Bayesian inference;
  • How to think more generally about such high-dimensional, multi-objective optimization problems;
  • The importance of open-source software and Python;
  • Institutional and cultural questions, including hiring and the trade-offs between being an individual contributor and a manager;
  • How they’re approaching accelerating discovery science to the speed of thought using computation, data science, statistics, and ML.

And as always, much, much more!

LINKS

]]> Episode 7: The Evolution of Python for Data Science https://vanishinggradients.fireside.fm/7 da4fab18-c5fa-460d-9ddf-0c8f1e60f3f8 Mon, 02 May 2022 06:00:00 +1000 Hugo Bowne-Anderson full 1 Hugo Bowne-Anderson Hugo speaks with Peter Wang, CEO of Anaconda, about how Python became so big in data science, machine learning, and AI. They jump into many of the technical and sociological beginnings of Python being used for data science, a history of PyData, the conda distribution, and NUMFOCUS. 1:02:31 yes Hugo speaks with Peter Wang, CEO of Anaconda, about how Python became so big in data science, machine learning, and AI. They jump into many of the technical and sociological beginnings of Python being used for data science, a history of PyData, the conda distribution, and NUMFOCUS. They also talk about the emergence of online collaborative environments, particularly with respect to open source, and attempt to figure out the movings parts of PyData and why it has had the impact it has, including the fact that many core developers were not computer scientists or software engineers, but rather scientists and researchers building tools that they needed on an as-needed basis They also discuss the challenges in getting adoption for Python and the things that the PyData stack solves, those that it doesn’t and what progress is being made there. People who have listened to Hugo podcast for some time may have recognized that he's interested in the sociology of the data science space and he really considered speaking with Peter a fascinating opportunity to delve into how the Pythonic data science space evolved, particularly with respect to tooling, not only because Peter had a front row seat for much of it, but that he was one of several key actors at various different points. On top of this, Hugo wanted to allow Peter’s inner sociologist room to breathe and evolve in this conversation. What happens then is slightly experimental – Peter is a deep, broad, and occasionally hallucinatory thinker and Hugo wanted to explore new spaces with him so we hope you enjoy the experiments they play as they begin to discuss open-source software in the broader context of finite and infinite games and how OSS is a paradigm of humanity’s ability to create generative, nourishing and anti-rivlarous systems where, by anti-rivalrous, we mean things that become more valuable for everyone the more people use them! But we need to be mindful of finite-game dynamics (for example, those driven by corporate incentives) co-opting and parasitizing the generative systems that we build. These are all considerations they delve far deeper into in Part 2 of this interview, which will be the next episode of VG, where we also dive into the relationship between OSS, tools, and venture capital, amonh many others things. LInks Peter on twitter (https://twitter.com/pwang) Anaconda Nucleus (https://anaconda.cloud/) Calling out SciPy on diversity (even though it hurts) (https://ilovesymposia.com/2015/04/03/calling-out-scipy-on-diversity/) by Juan Nunez-Iglesias Here Comes Everybody: The Power of Organizing Without Organizations (https://en.wikipedia.org/wiki/Here_Comes_Everybody_(book)) by Clay Shirky Finite and Infinite Games (https://en.wikipedia.org/wiki/Finite_and_Infinite_Games) by James Carse Governing the Commons: The Evolution of Institutions for Collective Action (https://www.cambridge.org/core/books/governing-the-commons/7AB7AE11BADA84409C34815CC288CD79) by Elinor Olstrom Elinor Ostrom's 8 Principles for Managing A Commmons (https://www.onthecommons.org/magazine/elinor-ostroms-8-principles-managing-commmons) oss, data science, machine learning, python Hugo speaks with Peter Wang, CEO of Anaconda, about how Python became so big in data science, machine learning, and AI. They jump into many of the technical and sociological beginnings of Python being used for data science, a history of PyData, the conda distribution, and NUMFOCUS.

They also talk about the emergence of online collaborative environments, particularly with respect to open source, and attempt to figure out the movings parts of PyData and why it has had the impact it has, including the fact that many core developers were not computer scientists or software engineers, but rather scientists and researchers building tools that they needed on an as-needed basis

They also discuss the challenges in getting adoption for Python and the things that the PyData stack solves, those that it doesn’t and what progress is being made there.

People who have listened to Hugo podcast for some time may have recognized that he's interested in the sociology of the data science space and he really considered speaking with Peter a fascinating opportunity to delve into how the Pythonic data science space evolved, particularly with respect to tooling, not only because Peter had a front row seat for much of it, but that he was one of several key actors at various different points. On top of this, Hugo wanted to allow Peter’s inner sociologist room to breathe and evolve in this conversation.

What happens then is slightly experimental – Peter is a deep, broad, and occasionally hallucinatory thinker and Hugo wanted to explore new spaces with him so we hope you enjoy the experiments they play as they begin to discuss open-source software in the broader context of finite and infinite games and how OSS is a paradigm of humanity’s ability to create generative, nourishing and anti-rivlarous systems where, by anti-rivalrous, we mean things that become more valuable for everyone the more people use them! But we need to be mindful of finite-game dynamics (for example, those driven by corporate incentives) co-opting and parasitizing the generative systems that we build.

These are all considerations they delve far deeper into in Part 2 of this interview, which will be the next episode of VG, where we also dive into the relationship between OSS, tools, and venture capital, amonh many others things.

LInks

]]> Hugo speaks with Peter Wang, CEO of Anaconda, about how Python became so big in data science, machine learning, and AI. They jump into many of the technical and sociological beginnings of Python being used for data science, a history of PyData, the conda distribution, and NUMFOCUS.

They also talk about the emergence of online collaborative environments, particularly with respect to open source, and attempt to figure out the movings parts of PyData and why it has had the impact it has, including the fact that many core developers were not computer scientists or software engineers, but rather scientists and researchers building tools that they needed on an as-needed basis

They also discuss the challenges in getting adoption for Python and the things that the PyData stack solves, those that it doesn’t and what progress is being made there.

People who have listened to Hugo podcast for some time may have recognized that he's interested in the sociology of the data science space and he really considered speaking with Peter a fascinating opportunity to delve into how the Pythonic data science space evolved, particularly with respect to tooling, not only because Peter had a front row seat for much of it, but that he was one of several key actors at various different points. On top of this, Hugo wanted to allow Peter’s inner sociologist room to breathe and evolve in this conversation.

What happens then is slightly experimental – Peter is a deep, broad, and occasionally hallucinatory thinker and Hugo wanted to explore new spaces with him so we hope you enjoy the experiments they play as they begin to discuss open-source software in the broader context of finite and infinite games and how OSS is a paradigm of humanity’s ability to create generative, nourishing and anti-rivlarous systems where, by anti-rivalrous, we mean things that become more valuable for everyone the more people use them! But we need to be mindful of finite-game dynamics (for example, those driven by corporate incentives) co-opting and parasitizing the generative systems that we build.

These are all considerations they delve far deeper into in Part 2 of this interview, which will be the next episode of VG, where we also dive into the relationship between OSS, tools, and venture capital, amonh many others things.

LInks

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