Matthew Davis — Bringing Genetic Insights to Everyone

Matthew explains how combining machine learning and computational biology can provide mainstream medicine with better diagnostics and insights. --- Matthew Davis is Head of AI at Invitae, the largest and fastest growing genetic testing company in the world. His research includes bioinformatics, computational biology, NLP, reinforcement learning, and information retrieval. Matthew was previously at IBM Research AI, where he led a research team focused on improving AI systems. Connect with Matthew: 📍 Personal website: https://www.linkedin.com/in/matthew-davis-51233386/ 📍 Twitter: https://twitter.com/deadsmiths --- ⏳ Timestamps: 0:00 Sneak peek, intro 1:02 What is Invitae? 2:58 Why genetic testing can help everyone 7:51 How Invitae uses ML techniques 14:02 Modeling molecules and deciding which genes to look at 22:22 NLP applications in bioinformatics 27:10 Team structure at Invitae 36:50 Why reasoning is an underrated topic in ML 40:25 Why having a clear buy-in is important 🌟 Transcript: http://wandb.me/gd-matthew-davis 🌟 Links: 📍 Invitae: https://www.invitae.com/en 📍 Careers at Invitae: https://www.invitae.com/en/careers/ --- Get our podcast on these platforms: 👉 Apple Podcasts: http://wandb.me/apple-podcasts​​ 👉 Spotify: http://wandb.me/spotify​ 👉 Google Podcasts: http://wandb.me/google-podcasts​​ 👉 YouTube: http://wandb.me/youtube​​ 👉 Soundcloud: http://wandb.me/soundcloud​ Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack​​ Check out Fully Connected, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/fully-connected

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Gradient Dissent is a machine learning podcast from Weights & Biases with hosts Lukas Biewald, Lavanya Shukla and Caryn Marooney. It takes you behind-the-scenes to learn how industry leaders are putting deep learning models in production at NVIDIA, Meta, Google, Lyft, OpenAI, and more.