Ph.D. Student, Yale University
chen.liu.cl2482 at yale.edu
New Haven, CT & Mountain View, CA.
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Acknowledgements Many thanks to Zhuang Liu for kindly providing this website template, which was adapted from Zhe Cao's website.
Chen Liu
I am looking for a research internship (Summer 2025). Please let me know if you have opportunities in spatial-temporal modeling, multimodal learning, self-supervised learning, manifold learning, AI in healthcare, or related fields.
I am a Ph.D. student in computer science at Yale University (2022~) advised by Prof. Smita Krishnaswamy. I received my M.S. from Columbia University (2018~2020), and I did my undergraduate studies at a liberal arts college, Bucknell University (2014~2018).
Research Areas I primarily work in the field of manifold learning, a subfield of deep learning that studies how to best organize the representations in the latent space of neural networks.
Most of my research involve direct applications to healthcare or medicine, via analysis of medical images and omics (genomics, transcriptomics, proteomics) data. I also work on fusing multiple modalities, modeling time-varying dynamics, and learning from limited or no labels.
Experience Prior to my Ph.D., I first served as a full-time research assistant at Columbia University Medical Center (2020) in a medical imaging lab. The next year, I went to the industry and joined a startup company Matic (2021) and developed SLAM algorithms for housekeeping robots. Following that, I worked as a Senior Research Scientist at GE Healthcare (2021~2022), on deep learning in medical imaging applications, where I co-invented 2 patents.
News
[11/2024] I was recognized as a Top Reviewer at NeurIPS 2024 (top 9%) .
[07/2024] I wrote a tool to generate your Google Scholar Citation World Map. [PDF] [Code]
[06/2024] My first project during my Ph.D. was accepted to MICCAI 2024. [Paper] [PDF] [Code] [MICCAI] [Poster]
[08/2022] I started my Ph.D. journey at Krishnaswamy Lab, Yale University.
[06/2022] I was recognized as an Outstanding Reviewer at ICML 2022 (top 10%) .
Selected Recent Publications (* equal contribution)
✂️ CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation
Chen Liu*, Matthew Amodio*, Liangbo L. Shen, Feng Gao, Arman Avesta, Sanjay Aneja, Jay C. Wang, Lucian V. Del Priore, Smita Krishnaswamy
MICCAI 2024
[Paper] [PDF] [Code] [MICCAI] [Poster]
⏳ ImageFlowNet: Forecasting Multiscale Image-Level Trajectories of Disease Progression with Irregularly-Sampled Longitudinal Medical Images
Chen Liu*, Ke Xu*, Liangbo L. Shen, Guillaume Huguet, Zilong Wang, Alexander Tong, Danilo Bzdok, Jay Stewart, Jay C. Wang, Lucian V. Del Priore, Smita Krishnaswamy
arXiv 2024
Conference Papers (* equal contribution)
✂️ CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation
Chen Liu*, Matthew Amodio*, Liangbo L. Shen, Feng Gao, Arman Avesta, Sanjay Aneja, Jay C. Wang, Lucian V. Del Priore, Smita Krishnaswamy
MICCAI 2024
🎲 Assessing Neural Network Representations During Training Using Noise-Resilient Diffusion Spectral Entropy
Danqi Liao*, Chen Liu*, Benjamin W. Christensen, Alexander Tong, Guillaume Huguet, Guy Wolf, Maximilian Nickel, Ian Adelstein, Smita Krishnaswamy
ICML 2023 TAG-ML Workshop & IEEE CISS 2024
🌐 Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
Xingzhi Sun*, Danqi Liao*, Kincaid MacDonald*, Yanlei Zhang, Chen Liu, Guillaume Huguet, Guy Wolf, Ian Adelstein, Tim GJ Rudner, Smita Krishnaswamy
ICML 2024 GRaM Workshop & under review at a conference
Substituting Gadolinium in Brain MRI Using DeepContrast
Haoran Sun, Xueqing Liu, Xinyang Feng, Chen Liu, Nanyan Zhu, Sabrina J Gjerswold-Selleck, Hong-Jian Wei, Pavan S Upadhyayula, Angeliki Mela, Cheng-Chia Wu, Peter D Canoll, Andrew F Laine, J Thomas Vaughan, Scott A Small, Jia Guo
IEEE ISBI 2020