Team
Our team comprises of proven innovators with exceptional academic, industry, and clinical experience. DeepHealth’s clinical and technical teams consist of experts in radiology, computer vision, and machine learning, with PhDs and technical degrees from Harvard, MIT, and other respected institutions. We are led by Greg Sorensen, Radiologist and former Harvard professor and CEO of Siemens Healthcare North America.
Bill received a PhD in Biophysics from Harvard studying deep learning and computational neuroscience and has extensive experience developing machine learning solutions in domains ranging from sports analytics to algorithmic trading.
Greg is a board certified radiologist with extensive experience as a medical practitioner, physician scientist, and business executive including positions as Professor at Harvard Medical School and CEO of Siemens Healthcare North America.
Bret received his MBA from Harvard Business School and has extensive experience in developing and commercializing new medical technology and services.
Yun received her Master’s degree of Engineering and Bachelor’s degree in Computer Science and Molecular Biology from MIT. She was previously an DeepHealth intern.
Giorgia received a PhD from the Harvard-MIT HST program, focusing on the validation of imaging algorithms and developing an expertise at the intersection of medicine, engineering and neuroscience.
Bryan received his PhD in computer science from MIT and has experience in technology transfer from academia to industry.
Jiye received a PhD in Cognitive Neuroscience and completed two postdoctoral fellowships at Princeton and Harvard. She has extensive experience working with diverse clinical populations and imaging data.
Myeongchan Kim, MD is a Machine Learning Engineer with experience in both medicine and computer science. He has previously worked as a Postdoctoral Research Fellow at MGH/Harvard Medical School.
Hyunkwang received his PhD in Electrical Engineering from Harvard and has extensive experience in developing generalizable and explainable deep learning algorithms for medical imaging applications.