Chao Chen
Assistant Professor
Stony Brook, NY 117948322
Contact
 Office: Computer Science Building, 2313C
 Tel: +16316322593
 Email: chao.chen.1 (@) stonybrook.edu
Research Interests
I explore geometric and topological properties of data. These global and robust information can provide
insight for modern data and image analytics. My research draws from the following different domains.
Biomedical imaging informatics: topologyinformed image segmentation, generation and analysis.
Machine learning: robustness of deep neural networks, label noise, graph neural networks.
Topological data analysis: persistent homology, computation and learning with topological features.
Past Experience
Recent Services
Associate Editor, Pattern Recognition
Area Chair, CVPR 2022
Area Chair, NeurIPS 2021, 2022
Area Chair, MICCAI 2021, 2020
Senior PC, AAAI 2022, 2021
News and Annoucement

New!!
One paper accepted by ICML 2022!
We develop a cyclecentric graph neural network which learns representations of cycles instead of nodes. The method is applied to knowledge graph completion task.

New!!
One paper accepted by NAACL 2022!
We provide an insightful analysis of backdoorattacked BERT models focusing on the attention focus drifting phenomenon. This provides an explanation of how backdoors are injected and inspires better backdoor detection algorithms.

New!!
One paper accepted by CVPR 2022!
We improve predictions on snapshot medical images using temporal sequences. We design a model that use temporal representation to recalibrate snapshot representation.

New!!
Just won the NSF CAREER award.
Project: TopologyDriven Learning for Biomedical Imaging Informatics
This 5 year project will support us in developing topologyinformed image analysis methods for different tasks such as segmentation, generation and analysis. The key is to combine theory from topological data analysis with deep learning techniques to develop trasparent, trustworthy and powerful tools. These tools will be validated in various biomedical problems.

New!!
One paper accepted by SoCG 2022!
We develop a GPU algorithm to speed up the computation Euler Characteristics Curve for large scale imaging data.

New!!
One paper accepted by ICLR 2022!
We use a topological prior to better reconstruct triggers of the input. This helps us identify backdoor attacked neural network models.

New!!
One paper accepted by AISTATS 2022!
We investigate adversarial robustness from a manifold's perspective.

New!!
One paper accepted by NeurIPS 2021!
We use the tool of persistent homology to investigate the behavior of neural networks under backdoor attacks.

New!!
One paper accepted by ICCV 2021!
We model spatial context for cell detection and classification in histopathology images. We use the kfunction in spatial statistics and incorporate into the feature representation on our deep neural network.

New!!
One paper accepted by ICML 2021!
We use topological features for link prediction with graph neural networks! Another contribution of the paper is a quadratic algorithm for the computation of extended persistent homology feature with graphs. It can be applied to any graph learning methods.

New!!
One paper accepted by IPMI 2021!
We learn topological biomarkers for MRI images of breast cancer patients.

New!!
Two papers accepted by ICLR 2021 as spotlight presentations!
We use discrete Morse theory to improve the topologypreserving segmentation problem.
We also introduce a novel noise model and a theoreticallyguaranteed algorithm to train robust model against such noise.

New!!
One paper accepted by AAAI 2021!
We use topological information for crowd counting.

New!!
Two papers accepted by NeurIPS 2020!
We train robust models by filtering out label noise using topological information. We also proposed a new variational formulation for the instance segmentation task.
