Jennifer Listgarten

Timeline

1. By 2007

   Physics and computer science degrees

   She earns undergraduate degrees in physics and computer science at Queen’s University and an MSc in computer science and computational vision at the University of Toronto. In 2007, she completes a PhD in the Department of Computer Science’s machine learning group at Toronto.

2. 2007–2017

   Research at Microsoft

   From 2007 to 2017, she works at Microsoft Research. Her posts take her through Redmond in 2007–2008, Los Angeles in 2008–2014 and Cambridge, Massachusetts, in 2014–2017.

3. 2011

   FaST linear mixed models

   In 2011, she coauthors the Nature Methods paper ‘FaST Linear Mixed Models for Genome-Wide Association Studies’.

4. 2016–2018

   Azimuth and Elevation for CRISPR

   In 2016, she coauthors the Nature Biotechnology paper behind Azimuth, a machine learning model that predicts CRISPR guide activity at intended targets. In January 2018, she coauthors the Nature Biomedical Engineering paper on Elevation, which predicts activity at unintended targets.

5. January 2018

   Joining Berkeley EECS

   She joins UC Berkeley’s Department of Electrical Engineering and Computer Sciences in January 2018, bringing her work on machine learning and applied statistics for computational biology.

6. 2019

   Conditioning by adaptive sampling

   In 2019, she coauthors ‘Conditioning by adaptive sampling for robust design’ with David Brookes and Hahnbeom Park at ICML. The method uses predictive models and adaptive sampling to design inputs with desired properties, such as protein sequences with greater fluorescence.

7. 2024

   A Bakar Spark Award

   In 2024, she receives a Bakar Fellows Spark Award. With Andrew Yang at UCSF, she works on ‘Revealing the Molecular Code for Crossing the Blood-Brain Barrier’, a project that studies proteins and receptors to help deliver therapies to the brain.

8. 2026

   An ISCB fellowship

   In 2026, she is elected a Fellow of the International Society for Computational Biology.

9. July 2026

   ProteinGuide for protein sequences

   In July 2026, she coauthors ‘Property guidance for protein sequence generative models with ProteinGuide’ in Nature Biotechnology. She and David F. Savage serve as corresponding authors.

10. As of October 2026

    Professor and computational biomedicine chair

    As of October 2026, she is a professor in UC Berkeley’s Department of Electrical Engineering and Computer Sciences, Center for Computational Biology and Bioengineering program, and holds the Jeffrey Huber and Angel Vossough Chancellor’s Chair in Computational Biomedicine. She serves on the Berkeley AI Research Lab’s steering committee and studies how machine learning can advance protein engineering.