Biography & Academic Profile

Dr. Gari D. Clifford is a professor in the Department of Anesthesiology and Critical Care Medicine at the Johns Hopkins University School of Medicine and a professor of biomedical engineering in the Whiting School of Engineering at Johns Hopkins University. He is an internationally recognized researcher in biomedical engineering, informatics, physiological signal processing, and medical artificial intelligence. His primary expertise focuses on machine learning in healthcare, wearable and ambient sensors, physiological monitoring, critical care informatics, and affordable digital health technologies designed for resource-constrained environments.

Dr. Clifford received his Bachelor of Science (Honours) in Physics from the University of Exeter and his Master of Science in Mathematical and Theoretical Physics from the University of Southampton. He earned his DPhil in Neural Networks and Biomedical Engineering from the University of Oxford under the supervision of Professor Lord Lionel Tarassenko. Following his doctoral work, Dr. Clifford undertook postdoctoral fellowships at the University of Oxford and in the Harvard–MIT Division of Health Sciences and Technology (HST) under Professor Roger G. Mark.

Prior to joining Johns Hopkins University, Dr. Clifford was a Professor of Biomedical Engineering and Biomedical Informatics at the Georgia Institute of Technology and Emory University, where he served as the Chair of the Department of Biomedical Informatics and Director of Informatics for the Georgia CTSA. Prior to that, he was an Associate Professor of Biomedical Engineering at the University of Oxford, where he served as Director of the Centre for Doctoral Training in Healthcare Innovation. He has also served as Principal Research Scientist at MIT and Lecturer in Medicine at Harvard University. Since 2014, Dr. Clifford has served as the Director of the George B. Moody PhysioNet Challenges.

Research Overview & Interests

Dr. Clifford leads a comprehensive research enterprise centered on developing novel approaches to signal processing, edge artificial intelligence, machine learning, and mathematical modeling of physiological time series. His group focuses on translating complex, high-frequency multimodal datasets — from continuous ICU bedside telemetry to low-power wearable, hearable, and nearable devices — into low-resource real-time clinical decision support systems to enhance access to care.

Critical Care Informatics & AI

Dr. Clifford’s work addresses fundamental limitations in high-dimensional, noisy patient data streams to improve decision support and early warning systems. Through support from the NIH and foundational leadership within PhysioNet, his laboratory develops open-access datasets and algorithms for a range of clinical problems, including arrhythmia detection, prediction of deterioration in critical care, and continuous artifact suppression in cardiac, respiratory, and hemodynamic waveform monitors. He led the creation of the MIMIC II database, the first and most comprehensive open-access critical care database, and trained the engineers who developed the subsequent MIMIC generations. His leadership of the PhysioNet Challenges catalyzes hundreds of researchers each year to answer key healthcare engineering problems.

Digital Neuropsychiatry & Cognitive Monitoring

His team pioneers non-invasive, objective behavioral tracking for neurological and psychiatric conditions. Leveraging edge computing, multimodal cameras, and audio and wearable sensors, the lab constructs predictive models for post-traumatic stress disorder, traumatic brain injury, major depression, schizophrenia, and sleep disorders. As part of the multi-center AURORA initiative and the Emory Healthy Brain Study, his research identifies fine-grained digital biomarkers across circadian rhythms, sleep micro-architecture, eye-tracking metrics, and autonomic reactivity.

Scalable Point-of-Care Technologies & Global Health

Dr. Clifford directs initiatives creating rugged, low-cost diagnostic systems designed specifically for low- and middle-income countries. Significant deployments include the safe+natal mobile health ecosystem, which equips traditional indigenous birth attendants in rural Guatemala and sub-Saharan Africa with AI-augmented Doppler ultrasound and automated computer vision tools for early identification of fetal growth restriction, preeclampsia, and perinatal complications.

Selected Publications

  • McSharry PE, Clifford GD, Tarassenko L, Smith L. A dynamical model for generating synthetic electrocardiogram signals. IEEE Transactions on Biomedical Engineering 50(3):289–294, 2003.
  • GD Clifford, F Azuaje, PE McSharry, Advanced Methods and Tools for ECG Data Analysis, Artech House, Cambridge, Massachusetts, 384, 2006.
  • Saeed M, Villarroel M, Reisner AT, Clifford GD, Lehman L, Moody GB, Heldt T, Kyaw TH, Moody B, Mark RG. Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II): A public-access intensive care unit database. Critical Care Medicine 39(5):952–960, 2011.
  • Johnson AEW, Ghassemi MM, Nemati S, Niehaus KE, Clifton DA, Clifford GD. Machine Learning and Decision Support in Critical Care. Proc IEEE Inst Electr Electron Eng. 104(2):444–466, 2016.
  • GD Clifford et al. AF classification from a short single lead ECG recording: The PhysioNet/Computing in Cardiology Challenge 2017. Computing in Cardiology 45:1–4; IEEE Computer Society Press, September 2017.
  • Nemati S, Holder A, Razmi F, Stanley MD, Clifford GD, Buchman TG. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU. Critical Care Medicine 46(4):547–553, 2018.
  • McLean SA, Ressler K, Koenen KC, Neylan T, Germine L, Jovanovic T, Clifford GD, et al. The AURORA Study: A Longitudinal, Multimodal Library of Brain Biology and Function after Traumatic Stress Exposure. Molecular Psychiatry 25(2):283–296, 2020.
  • Li J, Aguirre AD, Junior VM, Jin J, Liu C, Zhong L, Sun C, Clifford GD, Westover MB, Hong S. An Electrocardiogram Foundation Model Built on over 10 Million Recordings. NEJM AI 2(7):AIoa2401033, 2025.

Selected Honors

  • William Patterson Timmie Professor in Biomedical Informatics, 2026
  • Elected Fellow, National Academy of Inventors, 2025
  • Elected Fellow, American Institute for Medical and Biological Engineering, 2025
  • Dean’s Eminent Investigator and Distinguished Professor, 2025–2029
  • Elected Fellow, Institute of Electrical and Electronics Engineers, 2023
  • Elected Fellow, Asia-Pacific Artificial Intelligence Association, 2023
  • Fulbright Scholar & Specialist Roster, 2017–2023, 2023
  • Max Harry Weil Memorial Award, Society of Critical Care Medicine, 2020
  • NIH DataWorks! Distinguished Achievement Award for Data Reuse, NIH Office of Data Science Strategy, 2023
  • Martin Black Prize, Institute of Physics, 2009