Dexter Hadley, MD, PhD
|Stanford University||Residency||2012||Clinical Pathology|
|Hospital of the University of Pennsylvania||Internship||2010||General Surgery|
|University of Pennsylvania||M.D.||2009||School of Medicine|
|University of Pennsylvania||Ph.D.||2007||Genomics & Computational Biology|
|University of Pennsylvania||M.S.E.||2003||Systems Engineering|
|New College of FL (Honors)||B.A.||1999||Natural Science|
||2018||Pilot Award in Precision Imaging of Cancer and Therapy|
||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 background is in genomics and computational biology and he has training in clinical pathology. His research generates, annotates, and ultimately reasons over large multi-modal data stores to identify novel biomarkers and potential therapeutics for disease. His early work resulted in a successful 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. More recently, his laboratory was funded by the NIH Big Data to Knowledge initiative to develop the stargeo.org online portal to crowd-source annotations of open genomics big data that allows users to discover the functional genes and biological pathways that are defective in disease. In addition to his genomics work, he develops state-of-the-art data driven models of clinical intelligence that drive clinical applications to more precisely screen, diagnose, and manage disease. Towards this end, he has been repeatedly recognized by UCSF with various awards including the inaugural UCSF Marcus Award for Precision Medicine to develop a digital learning health system to use smartphones to screen for skin cancer as well as a pilot award in precision imaging to better screen mammograms for invasive breast cancer. 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.
, precision medicine
, digital health
, open data
, learning health systems
, medical devices
, drug discovery
, deep learning
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