I am a Ph.D. Candidate in Statistics at UBC working on Interpretable latent variable methods for High-dimensional biology 🧬. I’m currently working towards my Ph.D. dissertation and am open to job opportunities starting in 2024.

My research on Interpretable latent variable models (LVM) has involved several techniques.

To name a few: topic modelling, variational autoencoder, matrix factorization and Bayesian networks. Check out the following projects for details.

• Unraveling dynamically-encoded latent transcriptomic patterns in pancreatic cancer cells by topic modelling, Zhang et al, Cell Genomics, 2023.
• DeepPLIER: A deep learning approach to pathway-level representation of gene expression data, Zhang et al, MLCSB, ISMB, 2020.
• Scaled Matrix Completion.

Prior to my Ph.D. study, I finished my Master's in Biostatistics at the University of Washington, working on matrix completion and cell deconvolution problems.

• I was a Biostatistician Intern at NanoString Technologies, Inc., working on cell deconvolution problems.

• I was an Associate Researcher Intern at Huawei AI Lab working on video recognition. Check out here: Dynamic Cross-scale Aggregation and Multi-Head Interactions for Temporal Action Localization.

• I have been working as a Senior Statistical Consultant in Applied Statistics and Data Science Group.