Aug 2021: I will be giving a talk at RosettaCon on "Neural reconstruction of protein dynamics from cryo-EM images".
Aug 2021: I presented our work on heterogeneous cryo-EM reconstruction at the American Crystallographic Association Annual Meeting.
July 2021: Our workshop on Machine Learning for Structural Biology is accepted at NeurIPS 2021!
June 2021: A seminar and discussion on machine learning in cryo-EM at the Flatiron Institute in NYC.
May 2021: Started an internship with the Science team at DeepMind!
Apr 2021: I gave an invited talk, "Advances in Heterogeneous Reconstruction with cryoDRGN", at the CPP-EM Annual Symposium to hundreds of attendees from the cryo-EM community! Youtube link.
Apr 2021: I gave a semiar at Glasko-Smith-Kline's Protein, Cellular, and Structural Science and AI group. Excited to see the future impact of cryo-EM and AI advances on the development of human therapeutics.
Apr 2021: A profile of the interdepartmental collaboration responsible for cryoDRGN!
Mar 2021: Seminar, "Neural reconstruction of dynamic protein structure from cryo-EM images" at Silicon Therapeutics.
Feb 2021: I gave a seminar to Princeton's Applied and Computational Mathematics Department.
Feb 2021: Our paper describing cryoDRGN for heterogeneous cryo-EM reconstruction is out at Nature Methods!
Jan 2021: Our paper proposing a NLP framework for predicting viral esacpe mutations is published in Science!
My research applies core machine learning techniques to computational and structural biology problems, with a particular focus on protein structure determination with cryo-electron microscopy (cryo-EM). In my PhD, I created cryoDRGN, a neural method for reconstructing dynamic protein structures from cryo-EM images.
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.
This summer I am interning with John Jumper on the Science Team at DeepMind.
CryoDRGN2: Ab initio reconstruction of heterogeneous cryo-EM structures using neural networks
Ellen D. Zhong, Adam Lerer, Joey Davis, and Bonnie Berger.
To appear at 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.
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.
Learning mutational semantics
Brian Hie, Ellen D. Zhong, Bryan Bryson, and Bonnie Berger.
NeurIPS, December 2020.
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.
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.
Reconstructing continuous distributions of 3D protein structure from cryo-EM images.
Ellen D. Zhong, Tristan Bepler, Joey Davis, and Bonnie Berger.
ICLR, May 2020.
Oral Presentation and Best Paper award at MLCB satellite conference at NeurIPS 2019.