Yves A. Lussier is a leader in translational bioinformatics, actively involved with the development of the field and its conferences (PSB co-session chair x 3, co-founder and chair of the AMIA Summit on Translational Bioinformatics and of the Translational Bioinformatics Conference). Recently, his research group computationally predicted antagony between noncoding DNA (disease risk polymorphisms) located in distinct chromosomes that paradoxically interact to reduce the risk of Alzheimer’s Disease and of Bladder Cancer and were confirmed in GWAS by Jason Moore’s group.
Maricel G. Kann is a leader in translational computational biology and has been actively involved in organizing PSB scientific sessions for over a decade (2006, 2007, 2008, 2013, 2016, 2017). Her research interest lies in computationally determining the mechanisms of complex biological processes that underpin healthy vs diseased organisms. Dr. Kann has developed a unique expertise in accurately predicting protein domain interactions and consequently drug development.
Jason H. Moore is the Edward Rose Professor of Informatics and Director of the Penn Institute for Biomedical Informatics. He also serves as Senior Associate Dean for Informatics and Chief of the Division of Informatics in the Department of Biostatistics, Epidemiology, and Informatics. He came to Penn in 2015 from Dartmouth where he was Director of the Institute for Quantitative Biomedical Sciences. Prior to Dartmouth he served as Director of the Advanced Computing Center for Research and Education at Vanderbilt University where he launched their first high-performance computer. He has a Ph.D. in Human Genetics and an M.S. in Applied Statistics from the University of Michigan. He leads an active NIH-funded research program focused on the development of artificial intelligence and machine learning algorithms for the analysis of complex biomedical data. He is an elected fellow of the American Association for the Advancement of Science (AAAS), an elected fellow of the American College of Medical Informatics (ACMI), an elected fellow of the American Statistical Association (ASA), and was selected as a Kavli fellow of the National Academy of Sciences. He serves as Editor-in-Chief of the journal BioData Mining.
Martha L. Bulyk is a Professor in the Division of Genetics in the Department of Medicine, and also a Professor of Pathology, at Brigham & Women's Hospital and Harvard Medical School. She is also an Associate Member of the Broad Institute of MIT and Harvard, and an Associate Member of the Dana Farber Cancer Institute’s Center for Cancer Systems Biology. She holds undergraduate degrees in Biology and Mathematics from MIT, and a PhD in Biophysics from Harvard University. In 2005, Dr. Bulyk was named one of the TR35, MIT Technology Review’s annual competition to select the top 35 young innovators under the age of 35, and in 2007 she was named in Genome Technology’s annual selection of “Tomorrow’s PIs”. She serves on numerous journal editorial boards and is a Member of Faculty of 1000. Dr. Bulyk has published over 100 scientific articles and book chapters, and has presented over 190 invited seminars. Her group is focused on studies of transcription factors, DNA regulatory elements, gene regulatory networks, and the effects of genetic variation, using a variety of experimental and computational approaches including new technologies they have developed.
Joanne Berghout has been research faculty at The University of Arizona since 2016, working in the group directed by Yves Lussier. Her research is centered on using ontologies to discover patterns and new insights from genetic data. Recently, this led to the discovery of convergent regulatory mechanisms for intergenic SNPs associated with complex disease, revealed by 2-locus analysis of GWAS candidate SNPs, eQTL data, and enriched ontology annotations. Dr. Berghout holds a PhD in Biochemistry (molecular genetics) from McGill University, and worked previously as the Outreach Coordinator for the Mouse Genome Informatics database.
Francesca Vitali has been research faculty at The University of Arizona since 2016, working with Joanne Berghout in the group directed by Yves Lussier. Her research focuses on machine learning, network and systems approaches to pharmacogenomics, and drug repositioning. Her PhD and post-doctoral studies, conducted under Dr. Riccardo Bellazzi, led her to extend the flexibility of graph-theoretic approaches for integration of biological knowledge and data, which were applied to software tools she developed. In collaboration with Drs. Lussier and Berghout, she is investigating drug repositioning in eQTL networks anchored on noncoding disease-associated polymorphisms
Manolis Kellis is a Professor of Computer Science at MIT, an Institute Member of the Broad Institute of MIT and Harvard, a member of the Computer Science and Artificial Intelligence Lab at MIT, and head of the MIT Computational Biology Group (compbio.mit.edu). His research spans an unusually broad spectrum of areas, including disease genetics, epigenomics, gene circuitry, noncoding RNAs, comparative genomics, and phylogenetics. He has helped direct several large-scale genomics projects, including the Roadmap Epigenomics project, the ENCODE project, the Roadmap Epigenomics Project, the Genotype Tissue-Expression (GTEx) project, and comparative genomics projects in mammals, flies, and yeast. He received the US Presidential Early Career Award in Science and Engineering (PECASE) by US President Barack Obama, the NSF CAREER award, the Alfred P. Sloan Fellowship, the Technology Review TR35 recognition, the AIT Niki Award, and the Sprowls award for the best Ph.D. thesis in computer science at MIT. He has authored more than 150 journal publications, which have been cited more than 47,000 times. He lived in Greece and France before moving to the US, and he studied and conducted research at MIT, the Xerox Palo Alto Research Center, and the Cold Spring Harbor Lab.
For more information, please visit: http://compbio.mit.edu
Michael Hoffman creates predictive computational models to understand interactions between genome, epigenome, and phenotype in human cancers. He implemented the genome annotation method Segway, which simplifies interpretation of large multivariate genomic datasets, and was a linchpin of the NIH ENCODE Project analysis. He is a principal investigator at the Princess Margaret Cancer Centre and Assistant Professor in the Departments of Medical Biophysics and Computer Science, University of Toronto. He was named a CIHR New Investigator and has received several awards for his academic work, including the NIH K99/R00 Pathway to Independence Award, and the Ontario Early Researcher Award.