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' 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
