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Ellen Zhong is an Assistant Professor of Computer Science at Princeton University. She is interested in problems at the intersection of AI and biology. Her research develops machine learning methods for computational and structural biology problems with a focus on protein structure determination with cryo-electron microscopy (cryo-EM). She received her Ph.D. from MIT in 2022, advised by Bonnie Berger and Joey Davis, where she developed deep learning algorithms for 3D reconstruction of dynamic protein structures from cryo-EM images. She has interned with John Jumper and the AlphaFold Team at DeepMind and previously worked on molecular dynamics algorithms and infrastructure for drug discovery at D. E. Shaw Research. She obtained her B.S. from the University of Virginia where she worked with Michael Shirts on computational methods for studying protein folding.

For more information about her research and group, please visit her lab website: https://ezlab.princeton.edu/.

Research Positions

• Assistant Professor
  Department of Computer Science, Princeton University
  Principal Investigator, E.Z. Lab
  Co-affiliated faculty: Quantitative and Computational Biology Graduate Program, Omenn-Darling Bioengineering Institute, Center for Statistics and Machine Learning
  July 2022 - Present
• Research Scientist Intern
  AlphaFold team, DeepMind
  Host: John Jumper
  Machine learning for protein structure prediction
  May - Sept 2021
• Graduate Student Researcher
  MIT Computer Science and Artificial Intelligence Lab, Computational and Systems Biology Ph.D. Program
  Advisors: Bonnie Berger (Math, EECS), Joey Davis (Biology)
  Thesis: Machine learning for reconstructing dynamic protein structures from cryo-EM images
  Sept 2017 - May 2022
• Scientific Programmer
  D. E. Shaw Research
  Methods for estimating protein-ligand binding free energies
  July 2014 - Sept 2017
• Undergraduate Student Researcher
  University of Virginia, Chemical Engineering
  Advisor: Michael Shirts (ChemE)
  Thesis: Thermodynamics of protein folding from Hamiltonian Monte Carlo simulations
  Fall 2011 - Spring 2014

For more details, see Ellen's CV.

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Selected Publications

See here for a full list of publications.

2022

• Latent space diffusion models of cryo-EM structures.
  Karsten Kreis*, Tim Dockhorn*, Zihao Li, Ellen D. Zhong. (*Equal contribution)
  Machine Learning for Structural Biology Workshop at NeurIPS, 2022. (Oral)
  [paper] [talk]
• 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.
  [paper] [project page]
• Deep Generative Modeling for Volume Reconstruction in Cryo-EM
  Claire Donnat, Axel Levy, Fred Poitevin, Ellen D. Zhong, Nina Miolane
  Journal of Structural Biology, 2022.
  [paper]
• Uncovering structural ensembles from single-particle cryo-EM data using cryoDRGN
  Kinman LF*, Powell BM*, Zhong ED*#, Berger B#, Davis JH# (*co-first, #co-corresponding)
  Nature Protocols, 2022.
  [paper]
• Machine Learning for Reconstructing Dynamic Protein Structures from Cryo-EM Images
  Ellen D. Zhong
  Ph.D. dissertation, 2022.
  [pdf]

2021

• 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.
  [paper] [code]
• CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks
  Ellen D. Zhong, Tristan Bepler, Bonnie Berger, and Joey Davis.
  Nature Methods, February 2021.
  [paper] [pdf] [project page] [code]
• Learning the language of viral evolution and escape
  Brian Hie, Ellen D. Zhong, Bonnie Berger, and Bryan Bryson.
  Science, January 2021.
  [paper] [pdf]

2020 and earlier

• Exploring generative atomic models in cryo-EM reconstruction
  Ellen D. Zhong, Adam Lerer, Joey Davis, and Bonnie Berger
  Machine Learning in Structural Biology Workshop at NeurIPS, December 2020.
  [paper]
• Learning mutational semantics
  Brian Hie, Ellen D. Zhong, Bryan Bryson, and Bonnie Berger.
  Neural Information Processing Systems (NeurIPS), December 2020.
  [paper]
• Structures of radial spokes and associated complexes important for ciliary motility
  Miao Gui, Meisheng Ma, Erica Sze-Tu, Xiangli Wang, Fujiet Koh, Ellen D. Zhong, Bonnie Berger, Joseph H. Davis, Susan K. Dutcher, Rui Zhang, and Alan Brown.
  Nature Structural and Molecular Biology, December 2020.
  [paper] [pdf]
• RNA timestamps identify the age of single molecules in RNA sequencing
  Sam Rodriques, Linlin Chen, Sonya Liu, Ellen D. Zhong, Joe Scherrer, Ed Boyden, and Fei Chen.
  Nature Biotechnology, October 2020.
  [paper] [pdf]
• 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.
  [paper] [project page] [code]

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Code

CryoDRGN (Deep Reconstructing Generative Networks) is a state-of-the-art machine learning system for 3D reconstruction of dynamic protein structures from cryo-EM data, developed and maintained by Ellen, her collaborators and research group. CryoDRGN is released as an open-source tool on Github:

• Main software repository: https://github.com/zhonge/cryodrgn

For more information, see her group's software page.

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Teaching

• Fall 2022: COS 597N Machine Learning for Structural Biology

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Workshops

Ellen is a co-organizer for the Machine Learning for Structural Biology Workshop, held at NeurIPS. The MLSB workshop website can be found here: [2020] [2021] [2022].

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Talks

Upcoming:

• July 2024: Protein Society Annual Meeting, Vancouver, Canada
• Jun 2024: Keynote, 3DEM Gordon Research Seminar, Castelldefels, Spain
• Mar 2024: EMBO|EMBL Symposium on AI & Biology, Heidelberg, Germany
• Dec 2023: NeurIPS Workshop on Deep Learning and Inverse Problems
• Dec 2023: UC Berkeley CCB Seminar Series, Berkeley, CA
• Nov 2023: Toronto Vision Seminar, Toronto, Canada
• Oct 2023: Keynote, CryoNet, Stockholm, Sweden
• Oct 2023: NYU Langone, New York, NY
• Sept 2023: MIT Bioinformatics Seminar, Cambridge, MA

2023:

• Aug 2023: NNF Center for Protein Research at Copenhagen University
• July 2023: IEEE International Conference on Computational Photography, Madison, WI
• July 2023: American Crystallography Association Annual Meeting, Baltimore, MD
• July 2023: Symposium on Geometry Processing, Genoa, Italy
• June 2023: EMBL Heidelberg, Heidelberg, Germany
• June 2023: Google Research, Virtual
• June 2023: Flatiron Institute workshop on Cryo-EM methods, New York, NY
• May 2023: University of Pennsylvania Structural Biology Symposium, Philadelphia, PA
• May 2023: Princeton Catalysis Intiative, Princeton, NJ
• May 2023: University of Michigan Cryo-EM Data Processing Workshop, Ann Arbor, MI
• May 2023: ICLR Neural Fields Across Fields Workshop, Kigali, Rwanda
• Apr 2023: UC Davis Department of Molecular and Cellular Biology, Davis, CA
• Apr 2023: Stanford SCIEN Seminar Series, Palo Alto, CA
• Apr 2023: Caltech AI4Science Seminar Series, Pasadena, CA
• Mar 2023: University of Washington Institute of Protein Design, Seattle, WA
• Mar 2023: North Atlantic Microscopy Society Annual Meeting, Princeton, NJ
• 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

2022:

• Dec 2022: MIT 6.S980 Machine Learning for Inverse Graphics guest lecture, 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: Flatiron Institute Workshop on Sampling, Diffusion, and Transport, New York, 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

2021:

• 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

2020 and earlier:

• 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.

Last updated June 2023.