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To leverage and navigate the full potential of genomics data and its secondary use we need to address various challenges, such as funding the generation of the data in the first place, the impact of any data-associated exclusivity claims, the global changing landscape in data privacy, and the potential consequences of patient led initiatives to be in control of their own data for use in research and development. In this session, the panelists will discuss these various aspects.
Jennifer Cubino brings many years of experience as a clinical research industry executive, most recently at IQVIA / Quintiles. She has a diverse background including creating and leading strategic partnerships, clinical trial operations, business strategy, precision medicine, technology development, and oncology. Her focus is developing high performing and engaged teams capable of tackling rapidly changing markets. With seven years of experience as a former IRB member at Tufts Medical Center, Jennifer has a deep commitment to patient well-being. Jennifer has numerous posters and publications on the topic of integrating NGS into clinical research and has presented at international conferences on enhancing Executive Leadership skills.
Dr. Hassanpour’s research is focused on developing intelligent computational methods to capture and organize unstructured biomedical information in a structured format to advance translational research and clinical practice. Dr. Hassanpour’s research laboratory works on building novel machine learning, natural language processing, and image analysis methods to extract clinical and health-related insights from medical records and images to improve diagnosis, prognosis, and targeted therapies. Before joining Dartmouth, he worked as a Research Engineer at Microsoft on high-throughput semantic text analysis for Web search queries for more than two years. Dr. Hassanpour received his Ph.D. in Electrical Engineering with a minor in Biomedical Informatics from Stanford University and a Master of Math in Computer Science from the University of Waterloo in Canada.
Deep Learning For Histology Image Analysis
With the recent expansions of whole-slide digital scanning, archiving, and high-throughput tissue banks, the field of digital pathology is primed to benefit significantly from deep learning technology. This talk will cover new deep-learning-based methods for pathology slide analysis which have shown promising results for histologic characterization of the slides.
Tõnu Esko’s research focuses on developing and implementing genomics-guided, personalized healthcare models at the national scale by using comprehensive electronic health records available for special populations as a means of understanding the role of DNA mutations. Among his many honors and affiliations, Esko has been a semifinalist for the American Society of Human Genetics Charles J. Epstein Trainee Award for Excellence in Human Genetics Research; he received the Estonian Academy of Sciences Young Investigator Scholarship; he was awarded first prize in the category of biology and environmental sciences for the Estonian national student research competition (Ph.D. level) in 2012; and he was a nominated participant at the 61st Meeting of Nobel Laureates and Young Researchers in Lindau, Germany. Esko is a current eLife Science editor and a member of both the Estonian and American Societies of Human Genetics. He also acts as scientific advisor to the Social Science Genetic Association Consortium and chairs the scientific program committee for the International Geneforum conference.