Gari D. Clifford, DPhil

Share Last Updated: September 12, 2018

Interim Chair, Associate Professor, Biomedical Informatics (Emory University)

Associate Professor, Biomedical Engineering (Georgia Institute of Technology)  

Adjunct Faculty, Morehouse School of Medicine

Honorary Professor, The Sleep & Circadian Neuroscience Institute, University of Oxford

Distinguished Guest Professor, Tsinghua University, Beijing, China

Deputy Editor, Physiological Measurement, Institute of Physics and Engineering

[Clifford Lab]  [Google Scholar]

Dr. Clifford was trained at a graduate level in Theoretical Physics and then later in Neural Networks and Biomedical Engineering from the University of Oxford, United Kingdom. He has an international reputation in mHealth informatics, critical care data analysis and the application of signal processing and machine learning to medicine.  Dr. Clifford received postdoctoral research training from the Massachusetts Institute of Technology when he later became a Principal Research Scientist, managing the development of the world's largest open access critical care database (MIMIC II).  From 2009 to 2014, he has served as Associate Professor and Fellow of Kellogg College at the University of Oxford in the Department of Engineering Science, the Director of the Centre for Doctoral Training in Healthcare Innovation at the Institute of Biomedical Engineering (IBME), and the Founding Director of the Centre for Affordable Healthcare Technology at Kellogg College.   Dr Clifford also helped found the Sleep, Circadian Rhythm and Neuroscience Institute (SCNi) at the University of Oxford, where he is an Honorary Professor and led one of its five themes. Since moving to Atlanta in 2014, he has established BMI as a leading centre for mHealth informatics, underpinning the mobile data analytics for several research projects including the Emory Healthy Aging Study

Research Interests: scalable and affordable healthcare: finding ways to use state-of-the-art signal processing, machine learning and physiological modeling to reduce costs, increase accuracy and improve access in healthcare using enormous data streams through data fusion, prediction and developing confidence intervals and trust metrics. 

Main application areas include:

  • Computational Psychaitry
  • Critical Care
  • Perinatal Monitoring
  • mHealth, particularly in Resource-Constrained Environments (such as developing countries) and Disparity Populations
  • Sleep & Circadian Rhythms


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