Center for Causal Discovery
Distinguished Lecture in Causal Discovery
University of Pittsburgh, Carnegie Mellon University,
Pittsburgh Supercomputing Center and Yale University
Jonas Almeida, PhD, Professor and Chief Technology Officer, Department of Biomedical Informatics, Stony Brook University (SUNY), “Data Science for Biomedical Informatics in the Planet of the Apps” at 11:00 am on Thursday, November 30, 2017, in Rooms 407A/B BAUM, 5607 Baum Blvd., The Offices at Baum.
Abstract: As in all new fields of academic inquiry, Data Science starts with an identity crisis. So, what’s new about the way Data Science derives from Computer Science, Biostatistics and Genomic Atlases? Does the deployment of interoperable Data Spaces from Genomic Data Commons to HL7 FHIR, the commoditization of Cloud Computing, or the optimized classification with Machine Learning, fundamentally contribute to answering important questions? What about Precision Medicine, does Data Science even play a role in that translation beyond being a toolbox? This discussion will be illustrated with examples* of how Data Science already contributes to some of these endeavors, and how it could for many others, as it matures into a quantitative framework that is both pervasive and participated. It will be argued, and illustrated with published work, that Data Science opens a number of novel avenues in quantitative research that go beyond its immediate applications to the delivery of HealthCare. Bring your laptop if you want to try the examples as they are presented.
Biography: In January 2015, Dr. Almeida accepted the new position of Professor and Chief Technology Officer at the Biomedical Informatics Department of Stony Brook University (State University of NY, Long Island). This follows 4 years as the inaugural director of a new Division in Informatics in the Department of Pathology of the University of Alabama at Birmingham (UAB), and 5 years as Professor of Bioinformatics in the Division of Applied Mathematics of the University of Texas MDAnderson Cancer Center (2005-2010).
His current research interests are at the intersection of Semantic Web abstractions and distributed Cloud Computing approaches to Bioinformatics application development in the pervasive Web Platform. The use of computational statistics at the intersection of those two fields now gets a fancy new name, Big Data Science, which is also the focus of his educational and service activities. This research pulls together threads from past, and ongoing, work on mathematical modeling and machine learning for Medical Genomics, at a time when these fields are challenged by the increasingly data driven nature of modern Biomedical research. In his own work this has often focused on The Cancer Genome Atlas (TCGA), a Biomedical Big Data resource that enables, and requires, this new synthesis for the development of Personalized Medicine applications. As Population Health data becomes available in real-time (see for example http://bit.ly/pqiSuffolk), the opportunities for pursuing Machine Learning as a pervasive Web Computing exercise are emerging, with a new avenues for research in Artificial Intelligence applications embedded in the increasingly patient-facing Health-Care enterprise.
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