Today we are joined by Anubhav Jain, Staff Scientist & Chemist at Lawrence Berkeley National Lab.
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Anubhav leads the Hacker Materials Research Group, where his research focuses on applying computing to accelerate the process of finding new materials for functional applications. The paper we are discussing in this episode is ‘Unsupervised word embeddings capture latent knowledge from materials science literature’. With the immense amount of published scientific research out there, it can be difficult to understand how that information can be applied to future studies, let alone find a way to read it all. Anubhav breaks down the design of a system that takes the literature and uses natural language processing to analyze, synthesize and then conceptualize complex material science concepts. Additionally, he discusses how the method is shown to recommend materials for functional applications in the future – scientific literature mining at its best.
From the Interview
Many of you are aware that we’ve been hosting a couple of paper-reading meetups in conjunction with the podcast. I’m excited to share that Matt Kenney, Duke staff researcher and long-time listener and friend of the show, has stepped up to help take this group to the next level. The paper reading meetup will now be meeting every other Sunday at 1 PM Eastern Time to dissect the latest and greatest academic research papers in ML and AI. If you want to take your understanding of the field to the next level, check twimlai.com/meetup for more upcoming community events.
We’ve also got a couple of study groups currently running, one working through the fast.ai Deep Learning from the Foundations course, another on fast.ai Natural Language Processing, and another working through the Stanford cs224n Deep Learning for Natural Language Processing course. These study groups will be working on these courses through October and November, so it’s not too late to join. Sign up on the meetup page at twimlai.com/meetup.
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“More On That Later” by Lee Rosevere licensed under CC By 4.0