Podknife - Charles Yang: Machine Learning for Scientific Research by Machine Learning Engineered

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Charles Yang: Machine Learning for Scientific Research

Podcast |: Machine Learning Engineered
Publisher |: Charlie You
Media Type |: audio
Podknife tags |: Artificial Intelligence,; Business,; Careers,; Data Science,; Interview,; Machine Learning,; Technology
Categories Via RSS |: Business,; Careers,; Science,; Technology
Publication Date |: Sep 15, 2020
Episode Duration |: 01:26:11

Charles Yang is an EECS masters student at UC Berkeley focusing on AI and dynamical systems. He writes the excellent Machine Learning For Science newsletter where he showcases a wide range of use cases for machine learning in scientific research and engineering. Learn more about Charles:

Website: https://charlesxjyang.github.io/

Google Scholar: https://scholar.google.com/citations?user=BYOREdwAAAAJ&hl=en

ML4Sci Newsletter (Highly Recommended!): https://ml4sci.substack.com/

Want to level-up your skills in machine learning and software engineering? Subscribe to our newsletter: https://mlengineered.ck.page/943aa3fd46

Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Subscribe to ML Engineered: https://www.mlengineered.com/listen

Follow Charlie on Twitter: https://twitter.com/CharlieYouAI

Timestamps:

(02:08) Getting started in material science and machine learning

(08:58) "ImageNet moment" for ML in science

(13:20) Model explainability and transparency

(17:06) Charles' Current Research

(18:40) Embedding existing knowledge into ML models

(22:26) "Bilingual Scientists"

(24:46) Learning ML as a traditional scientist

(28:22) Private vs Public ML Research

(32:42) Rise of open-access research

(35:22) "SOTA chasing" in ML research

(38:10) Scientific ML research processes

(44:34) Applying ML knowledge to a scientific problem

(48:00) Biggest opportunities for ML in science

(51:18) Diversity in the research community

(54:24) Writing the ML4Sci newsletter

(56:20) Keeping up with new research

(01:05:30) Rapid Fire Questions

Links:

Charles' ML4Sci newsletter

Charles' article on AI-powered Science as a Service

Charles' article on Deep Learning in Science

Charles' article on Scientific Gatekeeping

Charles' article on Open Access Research

Google Weather Forecasting paper

neural-weather-model-for-eight-hour.html?m=1">Google 2nd Weather Forecasting paper

DeepMind Protein Folding paper

Charles discusses the breakthrough results ML has produced in scientific research, how both traditional scientists and ML researchers can get involved, and gives an unexpected answer to a rapid fire question.