• Helped developing a machine learning method PerturbVI that discovered gene regulatory networks with CRISPR perturbation data and single-cell RNA-seq data using Variational Inference and Jax in a team of three
• Simulated model misspecification of latent variables using Python and improved 6.5% sensitivity compared to existing methods
• Enabled ultra-fast inference speed with an average convergence time of 70x faster on the largest scale perturbation matrix (310,385 x 8563) than the existing method

• Developed a machine learning method SCFM that identifies gene-to-disease associations on the largest-scale single-cell RNA-seq data (4.1GB), utilizing coordinate ascent variational inference
• Achieved an average of 32% improvement in sensitivity and discovered an average of 15% more genetic variants when benchmarking against the existing method through extensive simulations
• Built a new Python package implementing SCFM framework with Jax to achieve ultra-fast computing speed with an average inference time 15x faster than the existing method (1.3s vs 20s)
• Enabled robustness on calibration and model misspecification over 4000+ simulation scenarios and benchmarked the method against baseline and other published models
• Accepted as the first-author abstract to a top-tier conference American Society of Human Genetics

Responsibilities include:

• Built a statistical analysis pipeline using Python and R and conducted the experiments for accessing genotype imputation quality over 123 populations

• Discovered that the imputation quality fell short 6.5%–42% in imputation R square among minority populations compared to European controls

• Raised minority awareness by presenting research results during the undergraduate poster session and awarded Provost Research Fellowship twice (in fall 2020 and fall 2021)

• Findings were published in the top tier journal (AJHG, IF=12.6)