Nan Huang

Data Science PhD Student @HDSIUCSD | B.S. in Data Science and Math&Econ @UCSanDiego

[Nan Huang @NanHuang99]

@kel_ruael @mragsac @XiaoyuGui1119 @TAmariuta Thanks! Next step is to validate the framework across more independent cohorts and extend it to additional traits to test how well the findings generalize.

[Nan Huang @NanHuang99]

⚡ Presented our childhood-onset asthma genomics work today at the Advances in Polygenic Precision Genomics (APPG 2026) Lightning Talks! 🧬🫁

We integrate large-scale GWAS, bulk-tissue transcriptomics, and single-cell regulatory data to prioritize disease-relevant genes and cellular contexts, and improve cross-cohort polygenic risk prediction.

📄 https://t.co/dchBm7EKM8

📍 Catch us at #ASHG2026 next month! ✈️

Joint work with @mragsac , @XiaoyuGui1119 & Kelan G. Tantisira, with guidance from @TAmariuta 🙌

#Genomics #GWAS #SingleCell #PolygenicScores #Asthma #MultiOmics

[Jay (Xinkai) Zou @Jayzou3773]

COLM 2026 Main Accepted🎉!

Is AI good at predicting how groups will behave in the future?

We know AI can make such predictions. But how well can it actually do? And more importantly, how can we make it better?

In our new work Simulating Organized Group Behavior: New Framework, Benchmark, and Analysis we study this problem systematically. Paper link: https://t.co/2k87ulCkx7

If you're interested in AI for future prediction—especially predicting the behavior of companies, organizations, governments, and other organized groups—check out our work:
🔗 https://t.co/zFFXgJkKms

Huge thanks to my amazing collaborators @yeeelow233 , @JasonWuzh @NanHuang99 and other collaborators, and to my advisors @LetianPeng and @shangjingbo for their invaluable guidance