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=enML4Sci 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/943aa3fd46Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/Subscribe to ML Engineered: https://www.mlengineered.com/listenFollow Charlie on Twitter: https://twitter.com/CharlieYouAITimestamps:(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 QuestionsLinks:Charles' ML4Sci newsletterCharles' article on AI-powered Science as a ServiceCharles' article on Deep Learning in ScienceCharles' article on Scientific GatekeepingCharles' article on Open Access ResearchGoogle Weather Forecasting paperGoogle 2nd Weather Forecasting paper DeepMind Protein Folding paper

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