I am an Assistant Professor of Computer Science at Princeton University. I am interested in problems at the
intersection of AI and biology. My research develops core machine learning techniques for computational and
structural biology problems, with a particular focus on protein structure determination with cryo-electron
microscopy (cryo-EM). For more details on research from my group, see our lab website here.
I received my Ph.D. from MIT, where I was co-advised by Bonnie Berger and Joey Davis. During my Ph.D., I created
cryoDRGN, a neural method for 3D reconstruction of dynamic protein structures from cryo-EM images. In the summer of
2021, I interned with John Jumper and the AlphaFold Team at DeepMind. Prior to MIT, I was a scientific programmer at
D. E. Shaw Research where I developed algorithms and infrastructure for predicting protein-small molecule binding
free energies from molecular dynamics simulations for drug discovery.
I am actively recruiting graduate students and postdocs to become the founding members of my group. Please see here
for more details.
Amortized inference for heterogeneous reconstruction in cryo-EM.
Axel Levy, Gordon Wetzstein, Julien Martel, Frederic Poitevin, Ellen D. Zhong.
Neural Information Processing Systems (NeurIPS), 2022.
CryoDRGN2: Ab Initio Neural Reconstruction of 3D Protein Structures From Real Cryo-EM Images
Ellen D. Zhong, Adam Lerer, Joey Davis, and Bonnie Berger.
International Conference on Computer Vision (ICCV), 2021.
CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks
Ellen D. Zhong, Tristan Bepler, Bonnie Berger, and Joey Davis.
Nature Methods, February 2021.
Learning the language of viral evolution and escape
Brian Hie, Ellen D. Zhong, Bonnie Berger, and Bryan Bryson.
Science, January 2021.
Learning mutational semantics
Brian Hie, Ellen D. Zhong, Bryan Bryson, and Bonnie Berger.
Neural Information Processing Systems (NeurIPS), December 2020.
Reconstructing continuous distributions of 3D protein structure from cryo-EM images
Ellen D. Zhong, Tristan Bepler, Joey Davis, and Bonnie Berger.
International Conference on Learning Representations (ICLR), May 2020.
Spotlight Presentation at ICLR.
Oral Presentation and Best Paper award at MLCB satellite conference at NeurIPS 2019.
Machine Learning for Reconstructing Dynamic Protein Structures from Cryo-EM Images
Ellen D. Zhong
Ph.D. dissertation. May, 2022.
We co-founded and organize the Machine Learning for Structural Biology Workshop, held at NeurIPS in
Selected Talks and Travel
- May 2023: University of Pennsylvania Structural Biology Symposium, Philadelphia, PA
- Mar 2023: Centre International de Rencontres Mathmatiques, Marseille, France
- Mar 2023: University of Washington Institute of Protein Design, Seattle, WA
- Mar 2023: Columbia Physiology and Cellular Biophysics, New York, NY
- Feb 2023: Biophysical Society Annual Meeting, San Diego, CA
- Jan 2023: Brigham Young University Department of Chemistry and Biochemistry, Provo, UT
- Dec 2022: Guest lecture, MIT 6.S980 Machine Learning for Inverse Graphics, Cambridge, MA
- Nov 2022: Institute of Pure and Applied Mathematics (IPAM) workshop, Los Angeles, CA
- Nov 2022: Cold Springs Harbor Laboratory, Course on Cryo-EM, Long Island, NY
- Nov 2022: Chan Zuckerberg Imaging Institute, Frontiers in Cryo-Electron Tomography, San Francisco, CA
- Oct 2022: Yale Department of Statistics and Data Science, New Haven, CT
- Oct 2022: Keynote, MIT Molecule Machine Learning Conference, Cambridge, MA
- Oct 2022: Purdue Department of Computer Science, Virtual
- Oct 2022: Rutgers Institute for Quantitative Biomedicine and RCSB Protein Data Bank, Virtual
- Sept 2022: Nature Conferences, Frontiers in Electron Microscopy for Physical and Life Sciences, Princeton, NJ
- Sept 2022: Van Andel Institute, Virtual
- Aug 2022: Microscopy & Microanalysis, Portland, OR
- Jun 2022: CVPR, Neural Fields in Computer Vision Tutorial, New Orleans, LA
- Apr 2022: ICLR Deep Generative Models for Highly Structured Data Workshop, Virtual
- Apr 2022: VIB-VUB Center of Structural Biology, Virtual
- Apr 2022: CCP-EM/CCPBioSim Cryo-EM Dynamics Discussion Meeting, Virtual
- Mar 2022: Vienna Biocenter IMBA/IMP Young Investigator Symposium, Virtual
- Mar 2022:Society for Industrial and Applied Mathematics (SIAM) Conference on Imaging Science, Cryo-EM Mini-symposium, Virtual
- Mar 2022: SLAC/Stanford University, Palo Alto, CA
- Mar 2022: John Hopkins University Cryo-EM Seminar Series, Virtual
- Mar 2022: Brookhaven National Lab Applied Mathematics Seminar Series, Virtual
- Mar 2022: International Conference on Image Analysis in Three-dimensional Cryo-EM, Lake Tahoe, CA
- Mar 2022: OpenEye CUP Conference, Santa Fe, NM
- Feb 2022: Princeton Department of Computer Science, Princeton, NJ
- Feb 2022: Columbia Department of Computer Science, Virtual
2020 and earlier:
- Nov 2021: MRC Laboratory of Molecular Biology, Cambridge, UK
- Nov 2021: The Francis Crick Institute, London, UK
- Oct 2021: Introductory remarks and discussion leader: Gordon Research Conference, Visualizing Biological
Complexity Across Scales, Waterville Valley, NH
Cryo-EM and AlphaFold in translational research
- Nov 2021: Microsoft Research New England, Virtual
- Sept 2021: Scientific Computing in Structural Biology Workshop, Stanford SLAC Users Meeting, Virtual
- Aug 2021: RosettaCon, Virtual
- Aug 2021: American Crystallographic Association Annual Meeting, Virtual
- Apr 2021: CCP-EM Spring Symposium, Virtual
- Apr 2021: GlaxoSmithKline (GSK), Virtual
- Feb 2021: Princeton University Applied Mathematics IDeAS Seminar, Virtual
- Feb 2021: UIUC Coordinated Science Laboratory Student Conference (CSLSC), Virtual
- Nov 2020: Vienna BioCenter, Research Institute of Molecular Pathology (IMP) Seminar Series, Virtual
- Sept 2020: SciLifeLab Advanced Cryo-EM Seminar Series, Virtual
- Aug 2020: Microscopy & Microanalysis, Virtual
- May 2020: SBGrid Annual Symposium, Virtual
- Feb 2020: Relay therapeutics, Cambridge, MA
- Dec 2019: Machine learning in Computational Biology (MLCB) meeting, Vancouver, BC
- Dec 2019: Poster: NeurIPS Learning Meaningful Representations of Life (LMRL) workshop, Vancouver, BC
- Dec 2019: Harvard Cryo-EM Club, Cambridge, MA
- Nov 2019: Poster: Janelia Women in Computational Biology Meeting, Ashburn, VA
- Oct 2019: New England CryoEM symposium, Worchester, MA
- Aug 2019: Poster: Flatiron Institute Computational Cryo-EM Workshop, New York, NY
- Nov 2015: Out in STEM National Conference, Pittsburgh, PA
From silicon to medicine: Core challenges of using molecular dynamics for early-stage drug discovery.
- Oct 2015: Grace Hopper Annual Conference, Houston, TX
Optimizing molecular visualization for drug discovery.
- Nov 2013: AIChE Annual Meeting, San Francisco, CA
Efficient simulation of protein stability on surfaces using a Hamiltonian Monte Carlo approach.
This website is a work in progress. Last updated: September, 2022.