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<title>Chao Chen</title>
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    <h1>Chao Chen</h1> <!-- <p><i><a href="https://www.howtopronounce.com/quanquan">pronunciation</a></i></p> -->
 <br>
<div id="subtitle">Assistant Professor</div>
<div id="subtitle">Department of <a href="https://bmi.stonybrookmedicine.edu/"><u>Biomedical Informatics</u></a></div>
<div id="subtitle">Also affiliated with <a href="https://www.cs.stonybrook.edu/"><u>Computer Science</u></a></div>
<div id="subtitle">and <a href="https://www.stonybrook.edu/commcms/ams/"><u>Applied Mathematics & Statistics</u></a></div>
<div id="subtitle"><a href="https://www.stonybrook.edu/"><u>Stony Brook University</u></a></div>
<div id="subtitle"><a href="http://www.ucla.edu">Stony Brook, NY 11794-8322</a></div>
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<p>
 If you are a PhD or Master student at Stony Brook, please contact me for research opportunities.<br><br>
 <b>Research Interests</b><br>
  I explore geometric and topological properties of data. These global and robust information can provide
  insight for modern data analytics. My research draws from the following different domains.
  <ul>
  <li><b>Machine learning</b>: robustness of deep neural networks, label noise, graph neural networks.</li>
  <li><b>Topological data analysis</b>: persistent homology, computation of topological features.</li>
  <li><b>Biomedical image analysis</b>: segmentation, digital pathology.</li>
  </ul>
  <b>Past Experience</b><br>
  <ul>
  <li>Assistant Professor of Computer Science, City University of New York (CUNY) <a href="http://www.gc.cuny.edu/Page-Elements/Academics-Research-Centers-Initiatives/Doctoral-Programs/Computer-Science"><u>Graduate Center</u></a> and <a href="http://www.cs.qc.cuny.edu/"> <u>Queens College</u></a>, United States </li>
  <li>Postdoc at <a href="http://www.cs.rutgers.edu/"><u>Rutgers University</u></a>, United States (advised by <a href="http://www.cs.rutgers.edu/~dnm/"><u>Prof. Dimitris Metaxas</u></a>)</li>
  <li>Postdoc at <a href="http://www.ist.ac.at/"><u>Institute of Science and Technology</u></a>, Austria (advised by <a href="http://www.ist.ac.at/~edels/"><u>Prof. Herbert Edelsbrunner</u></a>)</li>
  <li>Ph.D. at <a href="http://www.cs.rpi.edu/"><u>Rensselaer Polytechnic Institute</u></a>, United States (advised by <a href="https://www.linkedin.com/in/daniel-freedman-a5157247/"><u>Prof. Daniel Freedman</u></a>)</li>
  <li>B.S. at <a href="http://eecs.pku.edu.cn/EN/"><u>Peking University</u></a>, China</li>
  </ul>
</p>
<!--  <p>Here is my latest <a href="./pdf/CV.pdf">CV</a>.</p> -->
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<h2>Contact</h2>
<ul>
<li><p>Office: Computer Science Building, 2313C</p>
</li>
<li><p>Tel: +1-631-632-2593</p>
</li>
<li><p><img width="280" src="images/email_v3.jpg"></img></p>
</li>
</ul>
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<h2>News and Annoucement</h2>
<ul>

<li>
<font color="red">New!!</font>
One paper accepted by NeurIPS 2021!
<p> We use the tool of persistent homology to investigate the behavior of neural networks under backdoor attacks. </p>
</li>

<li>
<font color="red">New!!</font>
One paper accepted by ICCV 2021!
<p> We model spatial context for cell detection and classification in histopathology images. We use the k-function in spatial statistics and incorporate into the feature representation on our deep neural network. </p>
</li>

<li>
<font color="red">New!!</font>
One paper accepted by ICML 2021!
<p> 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. </p>
</li>

<li>
<font color="red">New!!</font>
One paper accepted by IPMI 2021!
<p> We learn topological biomarkers for MRI images of breast cancer patients.</p>
</li>

<li>
<font color="red">New!!</font>
Two papers accepted by ICLR 2021 as spotlight presentations!
<p> We use discrete Morse theory to improve the topology-preserving segmentation problem.</p> 
<p> We also introduce a novel noise model and a theoretically-guaranteed algorithm to train robust model against such noise.</p>
</li>

<li>
<font color="red">New!!</font>
One paper accepted by AAAI 2021!
<p> We use topological information for crowd counting.</p>
</li>

<li>
<font color="red">New!!</font>
Two papers accepted by NeurIPS 2020!
<p> We train robust models by filtering out label noise using topological information. We also proposed a new variational formulation for the instance segmentation task.</p>
</li>

</ul>
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<h2>Recent Services</h2>    
  <ul>
  <li><p> Area Chair, CVPR 2022</p></li>
  <li><p> Area Chair, NeurIPS 2021</p></li>
  <li><p> Area Chair, MICCAI 2021</p></li>
  <li><p> Senior PC, AAAI 2021</p></li>
  <li><p> Area Chair, MICCAI 2020</p></li>
  </ul>
       
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<h2>Awards</h2>    
<ul> 
    <li> <p>AWS Machine Learning Research Award, 2020</p>
    <li> <p>Simons Berkeley Research Fellowship, 2019</p>
    <li> <p>Adobe Data Science Research Award, 2018</p>
    <li> <p>Salesforce Deep Learning Research Award, 2018</p>
    <li> <p>NSF CAREER Award, 2017</p>    
    <li> <p>Yahoo! Academic Career Enhancement Award, 2015</p>
</ul>
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