Linda J. and Mark C. Smith Chair in Bioscience and Bioengineering
Associate Professor of Electrical and Computer Engineering
Adjunct Associate Professor of Biomedical Engineering
Program Faculty Bioengineering
Education
Ph.D., Electrical Engineering, Stanford University, Stanford, CA, 2009
M.Sc., Electrical Engineering, Stanford University, Stanford, CA, 2005
B.Sc., Electrical Engineering, Stanford University, Stanford, CA, 2004
Major Awards
Outstanding Doctoral Thesis Advisor Award, Georgia Tech, 2021
Academy Award for Technical Achievement, American Academy of Motion Picture Arts and Sciences (Oscars), 2021
Office of Naval Research Young Investigator Award (ONR YIP), 2018
National Science Foundation CAREER Award, 2018
IEEE Sensors Early Career (Young Professional) Award, 2018
Sigma Xi Young Faculty Award, Georgia Tech, 2017
Lockheed Dean’s Excellence in Teaching Award, Georgia Tech, 2016
G. J. Lieberman Fellowship, Stanford University, 2009
My research interests are to use embedded systems and machine learning to develop miniaturized sensors that can be used in and outside the clinic. My main focus is to develop a miniaturized bioimpedance sensor to detect IV infiltration and evaluate joint health.
My main research interests are embedded systems and assistive robotics.
My research interest focus on biomedical sensing system for home monitoring. This includes developing novel wearable devices, measuring bio-signals, and analyzing those through signal processing algorithms to get clinically useful data.
My research interests focus on non-invasive wearables that leverage novel signal processing techniques for physiological monitoring.
My research interests include machine learning and bio-signal processing for applications in wearable health monitoring, particularly for detecting and monitoring trauma and assisting in triage.
My research interests focus on deep learning with less supervised environments, including weakly supervised, semi-supervised, and transfer learning, particularly for human activity recognition and physiological signal analysis.
My research interests include designing circuits and systems for sensing biological signals, and finding biomarkers for quantifying the biomedical condition through signal processing techniques. Designing such systems and miniaturizing them to a wearable size helps with health monitoring for both clinical and at-home settings.
My research interests lie in the hardware development of non-invasive wearable devices and the application of signal processing techniques to get clinically useful data from them.
My research is focused on the quantification and modulation of joint load, specifically how joint acoustic emissions could be used to estimate load and in turn provide physiological feedback to exoskeletons.
My research interests include noninvasive neuromodulation and electrophysiological monitoring as applied to clinically relevant conditions. I am especially interested in the monitoring and modulation of psychiatric disorders.
My research interests center around the development of non-invasive wearable biomedical devices for improving diagnosis and remote monitoring of patients. I am particularly interested in developing embedded systems for monitoring cardiopulmonary health.
My research interests include hardware and embedded systems development for miniaturized wireless non-invasive wearables, remote detection via image sensing technologies, and the application of machine learning techniques in conjunction with the above to effectively monitor health and detect disease. I also enjoy designing GUIs for the systems that I develop.
My research interests focus on solving common biomedical problems such as scarcity of data, domain discrepancies, and data fusion using deep learning. I am also interested in incorporating prior knowledge of physiological signals to neural network architecture by leveraging its expressiveness.
My research interests are based in the development of wearable physiological sensing devices by integrating embedded systems and pairing them with signal processing techniques, with a specific interest in quantifying cardiopulmonary health.
My research interests revolve around using non-invasive bio-signal sensing and measurement techniques to help improve patient outcomes. I am especially passionate about acoustics, signal processing, and device design that empowers patients to take ownership of their health both inside and outside of the clinic.
My research interests focus on wearable device hardware design and signal processing techniques to improve health and accelerate performance recovery.
My research interests lie at the interface of engineering and public health. I aim to understand how vagal nerve stimulation can decrease opioid dependency in individuals suffering with opioid use disorder. In the future, I hope to use my work to promote healthcare equity for marginalized populations.
My research focuses on applications of signal processing, machine learning, and deep learning in time series signals for the domain of wearables.
Currently transitioning to assist with the development of a miniaturized bioimpedance sensor to detect IV infiltration and evaluate joint health.
My research interests are focused biomedical sensing systems for investigating stress and mood disorders such as PTSD. This includes assessing and developing non-invasive technology as treatment options and investigating signal processing methods to uncover more about the underlying processes involved in mental health.
