Rui (Rachel) Yan

Ph.D. Candidate

Institute for Computational and Mathematical Engineering

Stanford University
ruiyan [at] stanford [dot] edu
 LinkedIn /  GitHub /  Google Scholar

Hello! I am a final-year Ph.D. candidate in the Institute for Computational and Mathematical Engineering (ICME) at Stanford University, advised by Prof. Lei Xing.

My research lies at the intersection of AI/ML and biomedicine, with a focus on developing and evaluating computational methods to learn representations from high-dimensional data. My interest spans representation learning, computer vision, and graph learning.

I obtained my B.S. in Applied Math and Computer Science from UCLA in 2019. I have also spent time at Meta, Uber AI, and Microsoft Research.


Selected Publications

Deep Representation Learning of Protein-Protein Interaction Networks
Rui Yan, Md Tauhidul Islam, Lei Xing
Science Advances (2024) [Paper] [PDF] [Code]

Interpretable Discovery of Patterns in Tabular Data via Spatially Semantic Topographic Maps
Rui Yan, Md Tauhidul Islam, Lei Xing
Nature Biomedical Engineering (2024) [Paper] [PDF] [Code]

Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging
Rui Yan, Liangqiong Qu, Qingyue Wei, Mars Huang, Liyue Shen, Daniel Rubin, Lei Xing, Yuyin Zhou
IEEE Transactions on Medical Imaging (2023) [Paper] [arXiv] [Code]

Correlative Image Learning of Chemo-mechanics in Phase-transforming Solids
Haitao Deng, Hongbo Zhao,..., Rui Yan, ..., Andrew M. Minor, William C. Chueh
Nature Materials (2022) [Paper] [Code]

Stochastic Models Accounting for Hidden Cell-drug Interactions
Song Yi Bae, Ning Guan, Rui Yan, Katrina Warner, Scott D. Taylor, Aaron S. Meyer
Cell Death & Disease (2020) [Paper] [Code]

Course Projects

Evaluation Metrics for Split and Rephrase
Stanford CS224n Natural Language Processing with Deep Learning
[Report] [Code]

Prediction of Gene Expression from Histopathology Images via Deep Learning in Gastric Cancer
Stanford CS271 Artificial Intelligence in Healthcare
[Report]

CNN Image Recognition Architecture Simplification
Stanford CS229 Machine Learning
[Poster] [Report] [Code]

Microscopy Cell Classification with Image Processing and SVM classifier
UCLA NSF-REU project advised by Prof. Marcus Roper
[Report]

Investment Portfolio Analysis -- Statistical Models in Finance
STATSC283 project: analyzed porfolio of 30 stocks from 5 industries
[Report]

Experience

 Internships
  • Software Engineer (Machine Learning) Intern @ Meta, Menlo Park, CA Summer 2024
  • Research Intern @ Microsoft Research, Redmond, WA Summer 2022
  • Research Engineer Intern @ Uber AI, San Francisco, CA Summer 2021
  • Data Scientist Intern, Infra @ Facebook (now Meta), Menlo Park, CA Summer 2020

 Teaching Assistant
  • ICME Data Science Summer Workshop @ Stanford Summer 2021 & 2022
  • CS224N: Natural Language Processing with Deep Learning @ Stanford Winter 2021
  • EE364A: Convex Optimization @ Stanford Summer 2020
  • PIC20A: Java Programming @ UCLA Winter & Spring 2019
  • PIC16: Python Programming @ UCLA Fall 2018

Awards