Camellia (Xinyue) Rui

PhD Candidate in Biostatistics · University of Southern California

I'm a research engineer building and evaluating AI agents for drug target discovery — grounded in human genetics and a statistical-genetics background. My dissertation focuses on probabilistically prioritizing causal variants underlying complex diseases. Currently AI Resident at Xaira Therapeutics. Previously ML intern at Genentech. Advised by Prof. Nick Mancuso and Prof. Steven Gazal. Final-year PhD candidate in Biostatistics at USC.

Skills

Core Competencies

Deep Generative Modeling Representation Learning Variational Autoencoder LLM Agents Machine Learning Deep Learning Transformer Statistical Inference NLP RAG

Languages

Python R SQL Bash LaTeX

Libraries & Frameworks

JAX PyTorch Scikit-learn NumPy Pandas SciPy Keras TensorFlow Spark Dask LangChain Chroma OpenAI SDK Claude Code SDK MCP

Experience

ML Research Intern — AI for Biology

Genentech, Inc. · May 2025 – Aug 2025

  • Conducted research and developed a deep learning Variational Autoencoder (VAE) model to model gene regulatory networks in a team of four using PyTorch
  • Engineered a model prototype from scratch using JAX and identified identifiability and misparametrization issues in the existing codebase
  • Reduced overall model loss from 6.7 × 10⁻² to 1 × 10⁻⁷ and improved inference accuracy by 19.7%
  • Successfully implemented the knockout procedure within the VAE model to denoise real biological signals while controlling the false discovery rate (FDR) under 10%
  • Managed reproducible code through GitLab using Merge Request-based Model Context Protocol (MCP), integrating open collaboration and clear communication across the team

Research Assistant — SCFM

Prof. Nicholas Mancuso & Prof. Steven Gazal · Mar 2024 – Present

  • 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, leveraging big data technologies and HPC clusters 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, demonstrating strong communication and publication skills

Research Assistant — PerturbVI

Prof. Nicholas Mancuso · Mar 2024 – Present

  • Developed 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 × 8,563) than the existing method
  • Optimized core algorithms, improving statistical inference by reducing computation time of false signal rate by 4x and significantly accelerating large-scale genetic analysis
  • Collaborated with team members to enhance model initialization, decreasing compiling time from 3.5 minutes to 1 minute and improving overall productivity

Accomplishments

Keck School of Medicine/Graduate School Fellowship

Aug 2022

University of Southern California

Jennifer Battat Scholarship

Jun 2020

University of Southern California

Provost's Research Fellowship

Sep 2019

University of Southern California

Publications

scFM: an efficient statistical fine-mapping approach for eQTLs using large-scale single-cell data

Rui X, et al. (1st author) · ASHG 2024 Abstract, 2024

perturbVI: A Scalable Latent Factor Model to Infer Genetic Regulatory Modules through CRISPR Perturbation Data

2nd author · In preparation, 2025

Estimating heritability explained by local ancestry and evaluating stratification bias in admixture mapping from summary statistics

Contributing author · American Journal of Human Genetics, 2024

A global view of disparity in imputation resources for conducting genetic studies in diverse populations

Rui X, et al. (2nd author) · American Journal of Human Genetics, 2022