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Distinguished Lecturer - Giorgio Quer

Giorgio Quer

Giorgio Quer, Ph.D., joined the faculty at STSI in January 2017. His expertise is on artificial intelligence and probabilistic modeling applied to heterogeneous data signals, in order to extract key information and make predictions on future occurrences based on past data. His contributions include new methods exploiting compressive sensing to collect and process wireless sensor network data, data link layers protocols for cognitive networks, and new ways to extract information from heart rate variability in order to study group dynamics.

His research interests are focused on the interpretation and representation of big data for human health, in order to build models for prediction of future occurrences, and improve patient outcomes. His multi-disciplinary interests include theoretical models, such as compressive sensing, Bayesian analysis, wavelet coherence and Markov decision processes; and analysis of noisy time-series from wearables, in particular physiological signals such as blood pressure, heart rate variability, and  photoplethysmography.

At STSI, he works on the data analytic side of the All of Us Research Program, adopting probabilistic models and predictive analytics to extract information from large health datasets available through the program, as well as from other industrial collaborations. His goal is to extract and present this information in a useful way to clinicians and other users.

Giorgio received a B.Sc. (2005), an M.Sc. (2007) with honors in Telecommunications Engineering, and a Ph.D. (2011) in Information Engineering from the University of Padova, Italy. During his doctoral studies, he was a visiting researcher at the Centre for Wireless Communication at the University of Oulu, Finland, and at the California Institute for Telecommunications and Information Technology at the University of California San Diego. Prior to joining STSI, he was a postdoctoral researcher at the Qualcomm Institute, University of California San Diego. He currently serves as a reviewer for several IEEE and ACM journals, and he was the co-chair for the CQRM symposium at IEEE GLOBECOM 2015.

LECTURE TOPICS: 
  • Machine Learning in Digital Medicine
  • Big Health Data with Wearables: Sensing, Processing and Outcomes
  • Device-to-Device Communication in 5G
  • Cellular and Device-to-Device Networks: towards Non-orthogonal Coexistence
DL Country: 
USA
DL Term: 
Term effective through 31 December 2019