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/

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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

Google 2nd Weather Forecasting paper

DeepMind Protein Folding paper

SalesForce Protein Folding paper

ML speeding up simulations by 9+ orders of magnitude (!)

Oak Ridge AI for Science Report

Nature paper using word2vec on MatSci papers

Paper using Graph NNs to find dark matter concentrations

Robert Caro - The Power Broker

Conor Dougherty - Golden Gates