Ellen D. Zhong is an Assistant Professor of Computer Science at Princeton University. Her research interests lie at the intersection of AI and biology, with a particular focus on protein structure determination with cryo-electron microscopy (cryo-EM).
Zhong obtained her B.S. from the University of Virginia, where she worked with Michael Shirts on computational methods for studying protein folding. She then went on to complete her Ph.D. at MIT in 2022, advised by Bonnie Berger and Joey Davis. During her time at MIT, Zhong developed deep learning algorithms for 3D reconstruction of dynamic protein structures from cryo-EM images. She also interned at DeepMind with the AlphaFold team and worked on molecular dynamics algorithms and infrastructure for drug discovery at D. E. Shaw Research.
Zhong's notable publications include:
Zhong has received several awards, including:
Ellen D. Zhong is a researcher in the fields of machine learning, computational biology, and structural biology. She is currently an Assistant Professor of Computer Science at Princeton University, where she is also the Principal Investigator of the E.Z. Lab for Molecular Machine Learning. Zhong obtained her Ph.D. from MIT in 2022, where she developed deep learning algorithms for 3D reconstruction of dynamic protein structures from cryo-EM images.
Zhong received her B.S. from the University of Virginia, where she worked with Michael Shirts on computational methods for studying protein folding. She then went on to complete her Ph.D. at MIT, advised by Bonnie Berger and Joey Davis. During her time at MIT, Zhong also interned with the AlphaFold team at DeepMind and worked on molecular dynamics algorithms for drug discovery at D. E. Shaw Research.
Zhong's research interests lie at the intersection of AI and biology, with a particular focus on protein structure determination using cryo-electron microscopy (cryo-EM). Her notable publications include:
Zhong has received several awards, including:
Ellen D. Zhong is a researcher in the fields of machine learning, computational biology, and structural biology. She is currently an Assistant Professor of Computer Science at Princeton University, where she also acts as the Principal Investigator of the E.Z. Lab for Molecular Machine Learning. Zhong obtained her Ph.D. from MIT in 2022, where she focused on developing deep learning methods for 3D reconstruction of dynamic protein structures. She has held internships at DeepMind and previously worked at D. E. Shaw Research on drug discovery.
Zhong received her Ph.D. from the Massachusetts Institute of Technology (MIT) in 2022, under the supervision of Bonnie Berger and Joey Davis. During her doctoral studies, she developed deep learning algorithms for 3D reconstruction of dynamic protein structures from cryo-EM images. Prior to her Ph.D., Zhong worked as a scientific programmer at D. E. Shaw Research, where she contributed to drug discovery efforts by developing algorithms and infrastructure for predicting protein-small molecule binding free energies from molecular dynamics simulations. She also holds a B.S. from the University of Virginia, where she worked with Michael Shirts on computational methods for studying protein folding.
In July 2022, Zhong joined Princeton University as an Assistant Professor of Computer Science. She is also the Principal Investigator of the E.Z. Lab for Molecular Machine Learning at Princeton, focusing on problems at the intersection of AI, structural biology, and computational biology.
Zhong's research interests lie at the intersection of AI and biology, particularly in developing machine learning techniques for computational and structural biology problems. Her work has been published in renowned journals and conferences, including Nature Methods, Science, Neural Information Processing Systems (NeurIPS), and International Conference on Learning Representations (ICLR).
One of Zhong's notable contributions is the development of cryoDRGN, a neural method for 3D reconstruction of dynamic protein structures from cryo-EM images. This algorithm leverages deep neural networks to reconstruct continuous distributions of 3D density maps, enabling the study of protein complexes with intrinsic structural or conformational heterogeneity.
Another significant work by Zhong is her research on "Learning the language of viral evolution and escape," published in Science in 2021. In this study, she applied machine learning algorithms originally developed for human natural language to model viral escape, where viruses mutate to evade the immune system. This approach accurately predicted structural escape patterns using only sequence data.