معرفی
Archer Yang is an Associate Professor in the Department of Mathematics and Statistics at McGill University, with additional affiliations as an Associate Academic Member of Mila - Quebec AI Institute, Associate Member of the School of Computer Science, and Member of the Quantitative Life Science Program. His academic journey began with a PhD from the University of Minnesota under the supervision of Hui Zou, establishing his foundation in statistical methodology and machine learning.
Dr. Yang's research spans three interconnected themes: statistical machine learning, applications in drug discovery, and computational genomics and healthcare. In statistical machine learning, he focuses on developing dimensionality reduction, probabilistic models, and causality-inspired methods to address complex high-dimensional data challenges. His work in drug discovery involves creating machine learning models to accelerate drug candidate identification and enhance understanding of drug efficacy and safety. In computational genomics and healthcare, he develops techniques to analyze genomic data, identify biomarkers, and explore the genetic basis of diseases, with the goal of improving precision medicine and predicting patient outcomes. His overarching objective is to bridge advanced data-driven methodologies with impactful applications in pharmacology, genomics, and healthcare.
His recent publications reveal a strong trend toward applying machine learning to healthcare challenges, particularly in congenital heart disease analysis, mortality prediction, and drug discovery. His work demonstrates expertise in developing interpretable models that can handle complex, high-dimensional biomedical data while maintaining statistical rigor. The integration of causal inference methods with machine learning appears to be a growing focus in his research trajectory.
- ICML Spotlight Paper (top 2.6%, 313/12,107)
Dr. Yang actively supervises a large research group including multiple postdoctoral fellows, PhD students, and Master's students. His lab has developed several notable software tools, including ml-mr for machine learning in Mendelian randomization. His supervision extends across statistics, computer science, and biomedical applications, reflecting the interdisciplinary nature of his work. He appears to maintain strong collaborative relationships with researchers in healthcare and genomics fields.
His laboratory, the Archer Yang Lab, maintains active GitHub repositories focused on machine learning applications in healthcare and drug discovery, with particular emphasis on interpretable models and statistical methodology development. The lab appears to work at the intersection of theoretical statistics and practical biomedical applications, with projects spanning from algorithm development to clinical implementation.
