Yang Meng is currently pursuing a Ph.D. in Statistics at the University of California, Irvine, expected to complete by 2026, co-advised by Dr. Stephan Mandt and Dr. Babak Shahbaba. With three years of full-time professional experience in Data Science, he brings a wealth of practical knowledge to his academic pursuits. Prior to his doctoral studies, Yang earned his M.A. in Statistics from Columbia University in 2020, advised by Dr. Tian Zheng, following his B.S. in Statistics from Zhejiang University in 2018.

Research Interests

His research focuses on artificial intelligence, supported by Chan-Zuckerberg Initiative.

Multimodal Learning: latent representation analysis, multimodal variational autoencoders, interpretable machine learning

Generative Models: particularly flow matching models, diffusion-VAE hybrids, normalizing flows, and protein language generation models

AI for Science: applying AI to single-cell biology, neuroscience, and public health

Uncertainty Quantification: employing Bayesian neural networks and modern statistical inference to address uncertainties in AI models.