I am Yiqun Chen (pronunciation here), currently a Data Science Postdoctoral Fellow at Stanford University, working with Professor James Zou on applying advanced AI techniques to biomedical data. Previously, 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 HCI 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.
- Excited to travel to MLCB 2023 to present our work GenePT and to moderate the industry discussion panel!
- TWIGMA, our work (preprint) looking at AI-generative art, has been accepted to NeurIPS 2023.
- I proposed a test for a difference in means for a single feature after clustering (preprint and 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.
- 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