Jennifer Listgarten

Research Description

Since Jan. 2018, Jennifer Listgarten has been a Professor in UC Berkeley's EECS department and Center for Computational Biology, a member of the steering committee for the Berkeley AI Research (BAIR) Lab. From 2007 to 2017 she was at Microsoft Research, through Cambridge, MA, Los Angeles and Redmond, WA. Before that she did her PhD in the machine learning group at the University of Toronto

Her expertise is in machine learning, applied statistics, computational biology. She is interested in both methods development as well as application of methods to enable new insight into basic biology and medicine. Recently she has also started to work on computational chemistry. A recent print interview focused on her 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 her 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/optimization/engineering for properties such as expression, flurorescence, binding, stability, etc.; related methods for molecule design and chemical reactions; 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 optimizing black box probablistic functions with pathological regions that can't be trusted; genetic association studies with complex, high-dimensional traits such as image volumes over time.

In the News

Four Berkeley engineers receive awards for COVID-19 research

The newly formed research consortium Digital Transformation Institute has made awards to 26 research projects led by top scientists and engineers to mitigate the COVID-19 pandemic. Four of the recipients have faculty appointments at UC Berkeley’s College of Engineering.
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