Invited Speakers
Jean (Jiayu) Wen is a computational biologist with expertise in computational and statistical method development, machine learning, high-throughput genomic data analysis, and molecular biology experiments to model gene regulatory interaction. Wen is an Associate Professor at The John Curtin School of Medical Research at Australian National University (ANU) where she holds an Australian Research Council (ARC) Future Fellowship (2017-22), an ARC Discovery Project (2022-25), and an ANU Future Scheme Fellowship (2019-23). She has extensive experience developing computational methods and machine learning models of diverse modes of gene regulatory networks. Her work has made original contributions to both computational methodological advances and novel biological discoveries, evidenced by her consistently highly cited publications in the top journals of the field, with over 50% of her papers having appeared in the top 1-2% of journals in the field of computational biology and genomics. Her research is in close collaboration with investigators from diverse disciplines, and she has established many strong national (6 current) and international (10 current) collaborations with world-leading computational and experimental biologists. The current goal of her research group is to make use of machine learning, computational and statistical method development, and the power of deep sequencing approaches combined with experimental validation to explore diverse modes of gene regulatory networks. Her skills and expertise in integrative computational models of gene regulation networks, founded on her research training in these areas in world-leading gene regulation laboratories (Memorial Sloan-Kettering Institute and Copenhagen University), are of particular importance for the data-driven modelling approaches.
Ben brings a longstanding research interest in AI, genomics, and predictive modelling. He completed his PhD at the University of Melbourne, focusing on novel computational approaches to large-scale genomic data analysis. From 2013 to 2021, he was a Research Scientist at IBM Research, then rejoined the University of Melbourne as a research fellow in Computing and Information Systems and later at the Florey Center for Neuroscience. His work spans research, consulting, and development across multiple areas of life sciences, from predicting cochlear implantation success to assessing genetic risk for human diseases. Ben has extensive experience in AI and statistical analysis of large genomic and life-science datasets.