Episode 18: Research Data Science in Biotech
Machine learning, deep learning, Bayesian inference for drug discovery, OSS, and accelerating discovery science to the speed of thought!
May 24, 20231h 12m
Audio is streamed directly from the publisher (api.substack.com) as published in their RSS feed. Play Podcasts does not host this file. Rights-holders can request removal through the copyright & takedown page.
Show Notes
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/)
Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe
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/)
Get full access to Vanishing Gradients at hugobowne.substack.com/subscribe