Qijia Jiang [Chee-ja Jee-ang]

I live somewhere in the convex hull of an Applied Mathematician / Statistician / EECS Researcher.

I got my PhD in Electrical Engineering from Stanford under Emmanuel Candès in 2021, and spent the last 5 years in academia (first as a postdoc and later as an assistant professor), working on various problems in optimization, statistics, signal processing and machine learning. Mirror Langevin and Parallel Complexity of Convex Optimization are some of the representative works here.

Research

I'm interested in two things at the moment:

1. AI for Science (here is a recent work: Generative Model for Molecular Dynamics)
2. AI alignment (including interpretability and scalable oversight)

I believe (1) is almost unequivocally good for the world if AI can be used to cure diseases and solve climate change.
(2), in my opinion, might be the only problem that will matter asymptotically as the technology develops. I hope to report more results on this soon.

Contact & Links

• Email: qjang AT ucdavis DOT edu
• Link to Google Scholar
• Link to GitHub
• I don't really use social media … prefer Podcast interviews / books / op-ed pieces to help me stay informed and make sense of the world.

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