Assistant Professor, Department of Biomedical Engineering
Institute for Computational Medicine
3400 N. Charles Street
Hackerman Hall, Room 315
Baltimore, MD 21218
Phone: (410) 516-4381
Fax: (410) 516-5294
E-mail: sree | at | jhu.edu
Sridevi V. Sarma (M’04) received the B.S. degree in electrical engineering from Cornell University, Ithaca NY, in 1994; and an M.S. and Ph.D. degrees in Electrical Engineering and Computer Science from Massachusetts Institute of Technology in, Cambridge MA, in 1997 and 2006, respectively. She was a Postdoctoral Fellow in the Brain and Cognitive Sciences Department at the Massachusetts Institute of Technology, Cambridge, from 2006-2009. She is now an assistant professor in the Institute for Computational Medicine, Department of Biomedical Engineering, at Johns Hopkins University, Baltimore MD. Her research interests include modeling, estimation and control of neural systems. She is a recipient of the GE faculty for the future scholarship, a National Science Foundation graduate research fellow, a L’Oreal For Women in Science fellow, and a recipient of the Burroughs Wellcome Fund Careers at the Scientific Interface Award.
- Developing control-oriented input-output models of basal ganglia nuclei (BG) and motor thalamus that describe how deep brain stimulation waveforms (input) influence neuronal activity (output) from cells in BG nuclei and motor thalamus for Parkinson’s disease (PD) patients; developing models of how neuronal activity in BG and motor thalamus influence PD patient behavior such as tremor frequency, movement velocity, and reaction time.
- Designing feedback control algorithms that measure neuronal activity from one or more BG nuclei in a PD patient with DBS, and in turn select the appropriate DBS inputs to one or more BG nuclei to generate normal-like neural activity and behavior. These control strategies will be based on the input-output models developed above.
- Developing point process models of single neurons in the sub-thalamic nucleus (STN) of the BG in PD patients that describe spiking characteristics across space and time. This information can be used to determine whether the neurophysiology in different regions of the STN varies, and whether some regions are more pathological than others-which may ultimately guide optimal DBS electrode placement.
- Automated seizure detection
- Automated seizure foci localization
- Brain Machine Interactive Control of Fast and Loaded Movements
- BME/ME/ECE: 580.616/530.616/520.601 Introduction to Linear Dynamical Systems (3 cr)
- BME/ME/ECE: 580.222 Signals, Systems and Control (3 cr)
- Closed-Loop Deep Brain Stimulation (DBS)
- Optimizing DBS Electrode Placement
- Automatic DBS Programming