Paydarfar’s clinical research program seeks to develop novel biosensors, signal-processing algorithms, and user interfaces that will enable clinicians and researchers to track and predict the health of individual patients as well as entire populations. This approach will extend beyond current reactive alarm systems, enabling us to forecast — and avert — adverse disease trajectories and to test the impact of such a strategy on health outcomes. This engineering and informatics platform should provide unprecedented opportunities to conduct fieldwork on human physiology and pathophysiology. Paydarfar’s basic research program seeks to understand mechanisms underlying disease states associated with abnormal behavior of neural oscillators, such as apnea, circadian dysrhythmias, and epilepsy, as well as the co-ordination of pacemakers with other physiological and behavioral functions. His research is funded by the NIH, NSF, and the Clayton Foundation for Research.
Data Collection and Processing of Physiological Signals in the Neonatal Intensive Care Unit (NICU)
Predicting Life-threatening Events and Outcomes in Preterm Infants
Effects of Gentle Vibration on Swallowing and Hemodynamics
Developing Algorithms that can Optimize Stimulus Waveforms for Electroceutical Devices (for more information, click here)
Using Mathematical Models to Optimize Stroke Response
Promoting Fairness in Machine Learning Predictions with Medical Data
Developing Explainable Machine Learning Models for Stroke MRI data