Jiaqi Zhang announced she will join the Computer Science Department at Columbia University as an Assistant Professor in January 2027, focusing her lab on causal inference, artificial intelligence, experimental design, and biomedical applications, and will begin recruiting PhD students and postdocs.

She obtained her PhD from MIT EECS under the supervision of Caroline Uhler and completed her undergraduate studies in mathematics at Peking University. She has interned at ByteDance, Microsoft Research, and Apple. Her research focuses on identifying causal relationships from data, extrapolating intervention effects, and selecting the most informative interventions, serving cell biology and biomedical discoveries. Relevant work includes causal decoupling identifiability, active learning for intervention design, and single-cell perturbation modeling, and she has been involved in organizing the CLeaR conference and the NeurIPS causal science workshop.

The recruitment window is aligned with this fall's PhD applications in computer science. The lab will be located in New York, integrating statistical causality with wet lab design into the same group. Columbia's Computer Science Department aims to incorporate causal and biomedical computation into its faculty roster, connecting with the perturbation prediction networks around MIT and the Broad Institute.

In market mechanisms, this represents the academic establishment's pricing of talent in causal and perturbation prediction. Beneficiaries include departments looking to establish cross-disciplinary labs and biomedical labs needing intervention design tools; the pressure point is private labs in similar fields still competing for the same pool of PhD students. Funding comes from faculty startup packages and subsequent federal and foundation grants, without going through product releases.

She will start in January 2027. Publications and conference organization are existing outputs, while the lab size will depend on the first cohort of admissions.

Source: Public information