Prof. Jirong Long
Vanderbilt University, USA
Dr. Jirong Long is a Professor of Medicine at Vanderbilt University School of Medicine. Her research centers on identifying novel genetic, epigenetic, and microbiota biomarkers for cancer risk, deepening insights into cancer development mechanisms. She currently leads five major NIH-funded studies: three focused on breast cancer—a transcriptome-wide association study (TWAS, R01CA235553), a proteome-wide association study (PWAS, R01CA293996), and a methylation-wide association study (MeWAS, R01CA247987)—and two targeting lung cancer: a combined MeWAS/TWAS study (R01CA249863) and a social epigenomics study (R01MD015396). Dr. Long has authored over 300 papers, many published in high-impact journals. She is a frequent invited speaker at national and international academic conferences. She has mentored more than 30 trainees and faculty members, significantly shaping their careers. Dr. Long plays a pivotal role in the molecular and genetic epidemiology programs at the Vanderbilt Epidemiology Center and the Vanderbilt-Ingram Cancer Center.
Prof. Jiang Gui
Dartmouth College, USA
My research program sits at the intersection of biostatistics, computational biology, and machine learning. I am interested in understanding the role of genomic variation and environmental context in disease susceptibility. Success in this important public health endeavor will depend critically on the amount of non- linearity in the mapping of genotype to phenotype and our ability to address it. I have developed several novel non-parametric machine learning algorithms to detect and characterize gene-gene and gene- environment interactions in the absence of statistically significant main effects. Those methods have been applied to several large population based genetic association studies and identified a few gene-gene interaction models that would be missed by regular regression methods.
Assoc. Prof. Hamed S. Najafabadi
Human Genetics at McGill University, Canada
Dr. Najafabadi is an Associate Professor of Human Genetics at McGill University, with expertise spanning genomics, computational biology, and machine learning. His research focuses on developing statistical and machine learning methods for modelling genomes and transcriptomes, gene regulatory programs, and complex multi-omic data. His group combines these methods with patient omics data to uncover the basis for development and progression of cancer, and to develop new omics-based diagnostic and prognostic tools. His work has been recognized with several national and international honours, including a Sloan Research Fellowship in Computational and Evolutionary Molecular Biology and a Canada Research Chair in Systems Biology of Gene Regulation.
Dr. Michail Smyrnakis
Science and Technology Facilities Council (STFC), UK
Dr. Smyrnakis is leading the AI group of Hartree Centre The Hartree Centre is a UK research laboratory that focuses on translating cutting-edge research into practical solutions for industry, academia, and the public sector. It accelerates innovation through high-performance computing (HPC), data analytics, and artificial intelligence technologies. He has a special interest in the applications of machine learning to the life sciences, particularly in approaches inspired by network biology. His work focuses on implementing a wide range of graph-based methods including graph neural networks (GNNs), graph embeddings, and on developing novel algorithms that integrate multi-modal omics data such as genomics, transcriptomics, proteomics, and metabolomics. Their work also involves creating methodologies grounded in statistical significance and robustness, designed to handle datasets of varying scales, from a few samples to thousands, to address diverse industry-related challenges.