Curriculum Vitae

Education

Ph.D. in Statistics, University of California, Irvine, 2027 (expected)

• Advised by Dr. Stephan Mandt and Dr. Babak Shahbaba
• Expertise: Multimodal Representation Learning, Generative AI, Computer Vision, AI for Science, Machine Learning

M.A. in Statistics, Columbia University, 2020

• Advised by Dr. Tian Zheng
• The 2019 Chair’s List Award

B.S. in Statistics, Zhejiang University, 2018

• Chu Kochen Honors College
• Dual Degree: B.A. in English Language and Literature

Work experience

Applied Scientist Intern @ Amazon, 06/2025 - Current

GenAI for Computer Vision

• Visual Scene Control via Stable Diffusion
• Text-to-Image Generation

AI Researcher @ Chan-Zuckerberg Initiative, 11/2024 - Current

Multimodal Learning & AI4Science

• AI for Cell Biology
• In-context Learning

Senior Data Analyst @ Varo Bank, 11/2021 - 08/2022

Machine Learning & Experimental Design

• Modeling & Data Visualization
• Experimental Design & A/B Testing

Data Analyst @ YipitData, 09/2020 - 10/2021

Data Science & Business Analytics

• Database Management
• Web Scraping & ETL Pipeline Design

Technical Skills

Coding

• Languages: Python
• AI Development: PyTorch, Pandas, Numpy, Pyro, Optuna, scikit-learn, WandB, Transformers (Hugging Face)
• AI Development: PyTorch, Pandas, Numpy, Pyro, Optuna, scikit-learn, WandB, Transformers (Hugging Face)
• Data Infrastructure: Databases (PostgreSQL, MySQL), AWS (Redshift, S3, Athena)
• Software: Tableau, AirFlow, R (dplyr, ggplot2, shiny)

Algorithms

• Generative Models: Flow Matching, Diffusion Models, VAE, GAN, Normalizing-Flows
• Artificial intelligence: Multimodal Learning, Representation Learning, RNN, CNN, Contrastive Learning
• AI for Science: Neuroscience, Climate Analysis, Public Health
• Statistical Learning: Random Forest, Decision Trees, Regression, Boosting, PCA, SVM, Clustering, MCMC

Publications and Preprints

Unity by Diversity: Improved Representation Learning in Multimodal VAEs

Sutter T. M., Meng Y., Agostini A., Chopard D., Fortin N., Vogt J. E., Shahbaba B., Mandt S. (2024). "Unity by Diversity: Improved Representation Learning in Multimodal VAEs" arXiv Preprint arXiv: 2403.05300, 2024.

Weakly-Supervised Multimodal Learning on MIMIC-CXR

Agostini A., Chopard D., Meng Y., Fortin N., Shahbaba B., Mandt S., Sutter T. M., Vogt J. E. (2024). "Weakly-Supervised Multimodal Learning on MIMIC-CXR" arXiv Preprint arXiv: 2411.10356, 2024.

Stroke recurrence prediction using machine learning and segmented neural network risk factor aggregation

Ding, X., Meng, Y., Xiang, L. et al. Stroke recurrence prediction using machine learning and segmented neural network risk factor aggregation. Discov Public Health 21, 119 (2024). https://doi.org/10.1186/s12982-024-00199-6

Scaling Up Bayesian Neural Networks with Neural Networks

Moslemi Z., Meng Y., Lan S., Shahbaba B. (2023). "Scaling Up Bayesian Neural Networks with Neural Networks." arXiv preprint: arXiv: 2312.11799, 2023

Log-rank-type tests for equality of distributions in high-dimensional spaces

Liu L., Meng Y., Wu X., Ying Z., Zheng T. (2022). "Log-rank-type tests for equality of distributions in high-dimensional spaces." Journal of Computational and Graphical Statistics 31 (4), 1384-1396.

Teaching

Univerity of California, Irvine

• STATS 212 – Statistical Methods III: Methods for Correlated Data, TA
• STATS 210C - Statistical Methods III: Longitudinal Data, TA
• STATS 67 – Introduction to Probability and Statistics for Computer Science, TA
• STATS 7 – Basic Statistics, TA
• STATS 210P – Statistical Methods I, TA
• STATS 120A – Introduction to Probability and Statistics I, Grader

Columbia University

• GU4234/ GR5234 – Sample Survey, Grader
• GU4222/ GR5222 - Nonparametric Statistics, Grader