My name’s Jianle Sun (孙健乐). I am a PhD student in Logic, Computation, and Methodology at Carnegie Mellon University. Before this, I obtained my M.S. and B.S. degree from the Department of Bioinformatics and Biostatistics, Shanghai Jiao Tong University. I am working on the intersection of statistics, machine learning, and computational biology (like genetics, genomics, and epidemiology).

Reseach interests

• Causal inference and learning. I am dedicated to developing methods for learning causal relationships from data and performing personalized counterfactual inference, which bridges many active areas in statistics and machine learning, such as semiparametric estimation, conditional copulas, disentangled representation learning, generative model and optimal transport.
• Computational biology: Statistical genetics (GWAS, eQTL, TWAS, MR, etc.) and genomics (single-cell and spatial omics, etc.). I am particularly interested in the integration and causal analysis of different genetic signals (GWAS, eQTLs, etc.) and omics data (transcriptomics, chromatin accessibility, methylation, etc.). It will facilitate a deeper understanding of the functional effects of variants and the mechanisms of gene expression regulation, but is also confronted by numerous challenges such as cross-population and cross-tissue heterogeneity, measurement noise, disparities in batch, resolution, and modality, and high dimensionality.
• Foundation models in biology: Genomics language model, single-cell foundation model, etc.

You can find the list of my publications and read the papers here.

I am happy with collaboration on theory (asymptotic properties, uncertainty quantification), methodology (representation learning, effect estimation, counterfactual generation), and application (genetics, genomics, epidemiology).