Leying Guan is an Associate Professor of Biostatistics at Yale Univ. Her team develops statistical and machine-learning methods for reliable scientific discovery from complex, high-dimensional data, along three interconnected directions: (1) models that capture structure in heterogeneous, high-dimensional data — latent-factor and multi-view models, structured/tensor estimation, high-dimensional regression, and integrative modeling; (2) inference that stays valid after complex or data-adaptive analysis — conformal prediction, robust inference, adaptive signal detection with error-rate control, and post-selection assessment; and (3) computational biology — building tools with improved statistical rigor for large-scale immune, single-cell, and multi-omics data, in collaboration with immunologists and consortium-scale data resources. Looking ahead, she is currently extending all three directions further toward new model structures and data types.
Depending on background and interest, projects may span new computational models for single-cell data targeting unresolved challenges; software and computational platform development with large-scale application to consortium data resources; theoretical and methodological development in robust uncertainty quantification or high-dimensional structured modeling. Candidates also have good flexibility to propose and develop their own research topics.
You don't need to fit all of these, but should be strong on at least one axis:
Methodologists: rigorous training in statistics, biostatistics, ML theory, comfortable enough with code and real data to implement and validate methods at scale.
Computational biologists / strong engineers moving into methodology: excellent software and data engineering skills (scalable computing, single-cell or genomic data pipelines).
ML-trained researchers curious about biology: strong deep learning background who are interested in working on problems with real statistical stakes in a biological domain and are motivated to learn the biology as they go.
Across all profiles, what matters most: genuine intellectual motivation, comfort working hands-on with real and large-scale data, and interest in building methods that are both theoretically sound and practically useful.
Compensation
Salary: Compensation will follow Yale University’s postdoctoral compensation guidelines and will be commensurate with experience.
Preferred Start Date: Preferred Early 2027, but negotiable. Applications will be considered until the position is filled.
International applicants are welcome.
PhD (or near completion) in Statistics, Computer Science, Biostatistics, Computational Biology, or a related quantitative field, with a track record in either rigorous methodology, computational software development, or applying/extending computational methods to real, large-scale single cell/omics data.
Send CV, one- or two-page research statement, two or three representative works (preprints are acceptable), and contact information for 2–3 references to leying.guan@yale.edu.