Monday, May 7th at 3:00 p.m., Dr. Giorgio Quer, Sr. Research Scientist at the Scripps Research Institute and Director of Artificial Intelligence at the Scripps Translational Science Institute will be presenting a distinguished lecture: “Machine Learning in Digital Medicine”.
Day & Time: Monday, May 7, 2018
3:00 p.m. ‐ 4:00 p.m.
Speaker: Dr. Giorgio Quer
Sr. Research Scientist, Scripps Research Institute, San Diego, California
Director of Artificial Intelligence, Scripps Translational Science Institute
Senior Member of the IEEE, Distinguished Lecturer for the IEEE Communications society
Location: Room BA1210
Bahen Centre for Information Technology
40 St George St., Toronto, ON M5S 2E4
Contact: Eman Hammad
Organizer: Communications Society
Abstract: Digitalize human beings using biosensors to track our complex physiologic system, process the large amount of data generated with artificial intelligence (AI) and change clinical practice towards individualized medicine: these are the goals of digital medicine. At Scripps, we promote a strong collaboration between computer scientist, engineers, and clinical researchers, as well as a direct partnership with health industry leaders. We propose new solutions to analyze large longitudinal data using statistical learning and deep convolutional neural networks to address different cardiovascular health issues. Among them, one of the greatest contributors to premature morbidity and mortality worldwide is hypertension. It is known that lowering blood pressure (BP) by just a few mmHg can bring substantial clinical benefits, but the assessment of the “true” BP for an individual is non-trivial, as the individual BP can fluctuate significantly. We analyze a large dataset of more than 16 million BP measurements taken at home with commercial BP monitoring devices, in order to unveil the BP patterns and provide insights on the clinical relevance of these changes. Another prevalent health issue we investigated is atrial fibrillation (AFib), one of the most common sustained cardiac arrhythmia, which is associated with stroke, hospitalization, heart failure and coronary artery disease. AFib detection from single-lead electrocardiography (ECG) recordings is still an open problem, as AFib events may be episodic and the signal noisy. We conduct a thoughtful analysis of recent deep network architectures developed in the computer vision field, redesigned to be suitable for a one-dimensional signal, and we evaluate their performance for the AFib detection problem using 200 thousand seconds of ECG recording, highlighting the potential of this technology. Looking to the future, we are investigating new applications of existing wearable devices, requiring advanced processing and clinical validation, and we are participating to the All of Us research program, an unprecedented research effort to gather data from one million people in the USA to accelerate the advent of precision medicine.
Biography: Dr. Giorgio Quer is a Sr. Research Scientist at the Scripps Research Institute in San Diego, California, and he is the Director of Artificial Intelligence at the Scripps Translational Science Institute.
He received the B.Sc. degree, the M.Sc. degree (with honors) in Telecommunications Engineering and the Ph.D. degree (2011) in Information Engineering from University of Padova, Italy. In 2007, he was a visiting researcher at the Centre for Wireless Communication at the University of Oulu, Finland. During his Ph.D., he proposed a solution for the distributed compression of wireless sensor networks signals, based on the joint exploitation of Compressive Sensing and Principal Component Analysis. From 2010 to 2017, he was a visiting scholar at the California Institute for Telecommunications and Information Technology and then a postdoc at the Qualcomm Institute, University of California San Diego (UCSD), working on cognitive networks protocols and implementation.
He is a Senior Member of the IEEE, a member of the American Heart Association (AHA), and a Distinguished Lecturer for the IEEE Communications society. His research interests include wireless sensor networks, network optimization, compressive sensing, probabilistic models, deep convolutional networks, wearable sensors, physiological signal processing, and digital medicine.