Bio
I work on optimization and ML for commercial robotaxi at Uber (e.g. news1 news2). Previously, I was a postdoctoral research scientist at Columbia IEOR and Data Science Institute, hosted by Agostino Capponi. In 2024, I received PhD in MS&E with PhD Minor in Statistics from Stanford. I was fortunate to be advised by Markus Pelger as a member of the Advanced Financial Technologies Lab.
Doctoral dissertation committee: Markus Pelger, Kay Giesecke, Itai Ashlagi, Han Hong, Jann Spiess.
Contact: jiachengzou [at] alumni.stanford.edu
Research brief
I develop methods for large spatial temporal panel data. My goal is to design useful tools that invite Transformer-based ML to more classical inference settings. See more in my research tab.
Updates
[Aug 2026] Accepted at Management Science (MS) for our panel inference paper.
[Dec 2025] New paper the Nonstationarity-Complexity Tradeoff in Return Prediction is now posted. Our method ATOMS leads to better performance during non-stationarity e.g. recessions:
[Dec 2025] R&R at Journal of Financial Economics (JFE) for our graph learning for supply chain paper.
[Apr 2025] R&R at Management Science (MS) for our panel inference paper.
[Aug 2024] Present panel inference in Frontiers of Economics and AI+ML Meeting at Cornell.