Biology is moving faster than ever, and we’re making rapid progress in modeling useful biological systems in silico.
ML is poised to have a huge impact on the entire field in the next decade — and this semester Machine Learning at Berkeley is running a seminar series with researchers and leaders leveraging ML to stay at the cutting edge of biology: they’re working to change healthcare through vaccine development, antibody design, single cell analysis, and all sorts of protein engineering. Come join us to see how you can make GPUs go brrr in the service of designing life-saving therapeutics instead of just making them paint for you.
The series kicks off with Peyton Greenside, co-founder of BigHat Biosciences!
A pioneer of deep learning applied to life science problems, Peyton has developed computational, statistical, and AI/ML techniques to model, understand, and optimize biological sequences in academia and industry. Peyton was an inaugural Schmidt Science Fellow, a computational biologist at the Broad Institute, a scientific founder of Valis, and holds a PhD from Stanford University (Accel Innovation Scholar), an MPhil in Computational Biology from Cambridge University (Herchel Smith Scholar), and a BA in Applied Math from Harvard.
She founded BigHat Biosciences in 2019, enabling drug developers to create antibodies and complex therapeutics much faster than usually possible.
Soham Sankaran is the founder of PopVax, an Indian biotechnology company based in Hyderabad building a novel thermostable, low-cost, and safe second-generation mRNA platform for rationally-designed broadly-protective multi-epitope vaccines. Soham dropped out of the PhD program in the Computer Science department at Cornell, where his research was focused on robotics and distributed systems. He previously did sociology research at the Human Nature Lab, part of the Yale Institute for Network Science.