Develop and implement computational statistical methods for analyzing large‑scale, heterogeneous, and multi‑type datasets (e.g., continuous, categorical, functional, spatial, temporal, genomic, medical images).
Design Bayesian models and inference algorithms, including hierarchical models, latent variable frameworks, and Bayesian computation (MCMC, variational inference, sequential Monte Carlo).
Integrate multi‑modal data sources using advanced statistical fusion techniques, joint modeling, and representation learning to extract coherent signals across disparate data types.
Build and evaluate machine learning models—supervised, unsupervised, and semi‑supervised—tailored to scientific or engineering applications requiring statistical rigor and interpretability.
Develop scalable algorithms for high‑performance computing environments, including parallelization, GPU‑based co...