معرفی
Qiang Cheng is an Associate Professor at the University of Kentucky with joint appointments in the Division of Biomedical Informatics and the Stanley and Karen Pigman College of Engineering. His research bridges computational methods with biomedical applications, focusing on machine learning, data mining, and AI-driven solutions for precision medicine.
Research interests center on developing novel algorithms for biomedical data analysis, including tabular data processing, gene expression modeling, drug response prediction, and temporal pattern recognition in healthcare contexts. His work frequently integrates deep learning architectures with domain-specific challenges in omics and clinical informatics.
Recent publications demonstrate strong focus on generative models (diffusion networks, autoregressive architectures), efficient learning methods (sparse attention, lightweight networks), and biological applications (circadian rhythm prediction, molecular generation). Methodological innovations consistently target high-dimensional biomedical data challenges through multi-view learning, tensor decomposition, and causal inference frameworks.
