Brief Bio and Research Contribution:
Seid Miad Zandavi received his Ph.D. degree in various Machine Learning and AI applications from the School of Computer Science, the University of Sydney, Sydney, Australia, in Oct 2020. He is currently a postdoctoral research fellow at Harvard Medical School, and the Division of Genetics and Genomics at Boston Children’s Hospital, and Broad Institute of MIT and Harvard. Prior to join Harvard University, Miad was a Postodctoral Research Associate in computational biology at AI-Empowered Biomedicine Laboratory (VafaeeLab). He is also an Adjunct Lecturer with the Faculty of Science, the University of New South Wales, Sydney. Miad’s current research interests are to develop a model estimating a target gene effect perturbation inferred by GWAS, and model gene networks from phenotype associations to elucidate a disease description as a collection of dysfunctional biological pathways.
I developed an AI-model in the diagnosis of one of the world’s most pressing health problems, liver cancer in a highly collaborative environment conducted among scientists in cancer informatics and integrative computational biology as well as in close collaboration with clinicians and experimentalists. As a member of AI-enhanced Biomedicine Laboratory, I am focusig to develope advanced machine-learning methods and deep-learning models that leverage large omics data to find hidden structures within them, account for complex interactions among the measurements, integrate heterogeneous data and make accurate predictions in different biomedical applications ranging from multi-omics biomarker discovery to single-cell sequencing analytics.
Miad has a strong track record of multidisciplinary research. His research has attracted >$4M through several projects: Medical Research Future Fund (MRFF, 2020-2021) and Cellular Genomics Future Institute (2019). He has co-authored over 30 publications (70%first/corresponding author) in prestigious venues—e.g., Nucleic Acid Research, IEEE Transaction on Cybernetics, etc., —demonstrating his substantive contribution to methodological changes.