Yiqun T. Chen
I am Yiqun Chen (pronunciation here), currently an Assistant Professor in Biostatistics and Computer Science (affiliated) at Johns Hopkins University. Previously, I was a Data Science Postdoctoral Fellow at Stanford University, working with Professor James Zou on applying generative AI techniques to biomedical data. I completed my PhD in Biostatistics under the guidance of Professor Daniela Witten at the University of Washington, where I developed methods to test data-driven hypotheses. My application interest spans different fields of data science: from dermatology, social networks and epidemiology, as well as human-computer interaction and microbiome science.
Before graduate school, I earned undergraduate degrees in Statistics, Computer Science, and Chemical Biology with high distinction from the University of California, Berkeley. I’ve also gained industry experience in data science with Amazon Search and Waymo LLC.
I am always excited to explore collaboration opportunities, participate in reviews, and deliver talks. Please feel free to reach out to me via email at yiqun.t.chen[at]gmail[dot]com or schedule a meeting with me.
recent news
- If you want to collaborate or look for research opportunities, shoot me an email at yiqun.t.chen[at]gmail[dot]com.
- GenePT, our work on applying ChatGPT to analyze single-cell genes and cells data, have been published at Nature Biomedical Engineering.
- Our work on testing for a difference in means for a single feature after clustering has been accepted to Biostatistics (paper; software)!
🎉 celebrate every (small) win
I have been fortunate to have received several awards and honors, including the Cornell Young Researchers Workshop in 2023, the Rising Stars in Data Science in 2022, the Best Paper Honorable Mention at CHI 2023, the Thomas R. Fleming Excellence in Biostatistics Award, the Student Research Paper Award at NESS 2022, the Best Award at WNAR 2021, the Young Investigator Award at CROI 2020, and the Outstanding Teaching Assistant Award from the School of Public Health at the University of Washington.
selected publications
- Why, when, and from whom: considerations for collecting and reporting race and ethnicity data in HCIIn Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, 2023
- GenePT: A Simple But Hard-to-beat Foundation Model For Genes and Cells Built From ChatGPTbiorxiv.org/content/10.1101/2023.10.16.562533v1, 2023
- Selective inference for k-means clusteringJournal of Machine Learning Research, 2023