Since Jan. 2018 I am a Professor in UC Berkeley's
Center for Computational Biology,
a member of the steering committee for the
Berkeley AI Research (BAIR) Lab,
Chan Zuckerberg investigator.
From 2007 to 2017 I was at Microsoft Research, through
Cambridge, MA, Los Angeles and
Redmond, WA. Before that I did my PhD in the machine learning group at the
University of Toronto.
(Feel free to use this
short bio and picture for announcements.)
My expertise is in machine learning, applied statistics and computational biology. I'm interested in both methods development as well as application of methods to enable new insight into basic biology and medicine. A recent print interview focused on my CRISPR work can be found here. If you're interested more generally in how machine learning and biology go together, check out this Talking Machines interview with me instead. Finally, if you want to hear about my random walk in education & career space, take a look at this Berkeley Science Review profile.
Current areas of interest include: computational methods for protein design/engineering for properties such as expression, flurorescence, binding, stability, etc.; similar methods applied to molecule design; drug repositioning and discovery; machine learning methods development, and in particular at the intersection of graphical models, neural networks and variational inference, as well as inverting black box probablistic functions to perform input optimization of probabilistic functions; genetic association studies with complex, high-dimensional traits such as image volumes over time.
Previous focus areas include: machine learning methods for time series alignment and normalization; LC-MS proteomics; statistical genetics methods to correct for confounding factors in GWAS, epigenome-WAS and eQTL studies; problems in immunoinformatics such as HLA class I epitope prediction and HLA allele imputation. If you're interested in my statistical genetics work (FaST-LMM or EWASher), please go to this landing page.