Dexter Hadley, MD, PhD
|Stanford University||Residency||Clinical Pathology||2012|
|Hospital of the University of Pennsylvania||Internship||General Surgery||2010|
|University of Pennsylvania||M.D.||School of Medicine||2009|
|University of Pennsylvania||Ph.D.||Genomics & Computational Biology||2007|
|University of Pennsylvania||M.S.E.||Systems Engineering||2003|
|New College of FL (Honors)||B.A.||Natural Science||1999|
||2018||Inaugural Marcus Award for Precision Medicine Innovation|
||2018||Big Data 2 Knowledge (BD2K) Crowdsourcing Award|
||2016||RAP Award for Digital Health|
||2015||Faculty Enrichment Fund|
||2015||AMIA Design Challenge Competition Winner|
||2013||LRP award in pediatrics|
||2009||Penn Biotech Group Entrepreneurial Competition Winner|
||2007||Computational Biology Training Grant|
||2000||Center of Excellence Fellowship|
||2000||Medical Student Fellowship|
Dr. Hadley's expertise is in translating big data into precision medicine and digital health. His work has resulted in an ongoing precision medicine clinical trial for ADHD (ClinicalTrials.gov Identifier: NCT02286817) for a first-in-class, non-stimulant neuromodulator to be targeted across the neuropsychiatric disease spectrum. His laboratory was recently funded by the NIH Big Data to Knowledge initiative to integrate multiple large-scale open databases to allow cross platform computational analyzes powerful enough to discover the functional genes and their related biological pathways that are defective in disease. He received the inaugural UCSF Marcus Award for Precision Medicine to develop a digital health initiative to use smartphones to screen for skin cancer and reduce the mortality of melanoma. In general, the end point of his work is rapid proofs of concept clinical trials in humans that translate into better patient outcomes and reduced morbidity and mortality across the spectrum of disease.
big data, precision medicine, digital health, crowdsourcing, open data, learning health systems, medical devices, genomics, biomarkers, drug discovery, deep learning
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