Anselm Levskaya
I studied physics at Cornell, biophysics at UCSF, and neuroscience at Stanford. At UCSF I was involved in early work in optogenetics with Chris Voigt and Wendell Lim, engineering light-sensitive proteins for the direct control of intracellular signaling using patterned light controlled with computational microscopy. At Stanford I worked on light-field microscopy with the labs of Karl Deisseroth and Marc Levoy, developing this technique for optogenetic experiments in zebrafish and mice.
In industry, I founded a startup that developed a high-throughput DNA synthesis pipeline involving high-throughput single molecule cloning, next-gen sequence verification and physical selection by lasers for radically reducing error rates. I was one of the first employees at Cell Design Labs (acquired by Gilead), which developed next-generation engineered T-cell reagents for fighting blood cancers. There I developed novel synthetic notch receptors for direct contact cell-cell antigen sensing. Additionally, I’ve worked with startups applying deep learning to medical diagnostics and phenotypic screening.
I'm broadly interested in machine learning systems for assisting in the analysis and engineering of organisms, cells and and biological circuits, especially in moving beyond the data-poor, intuition-driven “artisanal” engineering approaches typical of existing biomedical projects. I believe we can leverage rich new biological data sources (high throughput imaging, sequencing, high-dimensional cytometry, etc.) via deep-learning approaches to one day accelerate the development cycle of therapeutics and diagnostics. I’m additionally interested in low level software infrastructure for deep learning and more academic aspects of representation learning and generative models over images and sequences.
Authored Publications
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PaLM: Scaling Language Modeling with Pathways
Aakanksha Chowdhery
Sharan Narang
Jacob Devlin
Maarten Bosma
Hyung Won Chung
Sebastian Gehrmann
Parker Schuh
Sasha Tsvyashchenko
Abhishek Rao
Yi Tay
Noam Shazeer
Nan Du
Reiner Pope
James Bradbury
Guy Gur-Ari
Toju Duke
Henryk Michalewski
Xavier Garcia
Liam Fedus
David Luan
Barret Zoph
Ryan Sepassi
David Dohan
Shivani Agrawal
Mark Omernick
Marie Pellat
Aitor Lewkowycz
Erica Moreira
Rewon Child
Oleksandr Polozov
Zongwei Zhou
Brennan Saeta
Michele Catasta
Jason Wei
Kathy Meier-Hellstern
arxiv:2204.02311 (2022)
Studying Stand-Alone Self-Attention in Vision Models
Prajit Ramachandran
Niki Parmar
Ashish Vaswani
Irwan Bello
Jon Shlens
Neurips (2019) (to appear)
