About me

I am a Ph.D. student in the School of Electrical and Computer Enginnering at Georgia Institute of Technology. I am advised by Prof. James Rehg. I am broadly interested in the field of machine learning for healthcare and mobile health.

My research involves developing methods for improving the effectiveness of mobile health interventions using machine learning and signal processing tools. My past projects involve (1) Detecting the clinical state of heart failure from Ballistocardiography signals collected in a home environment. (2) Detecting sleep stages in mice using novel electric field sensors placed on the walls of the home-cage. (3) Prediction of glaucoma progression using spatiotemporal data modeled using a Continuous-time Hidden Markov Model (CT-HMM).

I am currently working on the problem of analyzing and modeling user engagement in mobile health. I am specifically working on promt-level ecological momentary assessment (EMA) compliance in mobile health studies, and developing predictive tools for this problem.

Outside of research, I enjoy being outdoors - hiking, biking, and running. I enjoy cooking, theatre performances, and am proficient in Indian classical music.


Supriya Nagesh

snagesh7@gatech.edu

Ph.D. Student
School of Electrical and Computer Engineering
Georgia Institute of Technology

Google Scholar
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Publications

Journal papers
  • H. Kloefkorn, L. M. Aiani, A Lakhani,S. Nagesh , A. Moss, W. Goolsby, J. M. Rehg, N. P.Pedersen, S. Hochman , ‘Noninvasive Three-State Sleep-Wake Staging in Mice using Electric Field Sensors ’ Journal of Neuroscience Methods, 2020
  • V. B. Aydemir, S. Nagesh, M. Shandi, J. Fan, L. Klein, M. Etemadi, J. A. Heller, O. Inan, J.M. Rehg, ‘Classification of Decompensated Heart Failure from Clinical and Home Ballistocardiography. ’ IEEE Transactions on Biomedical Engineering, 2019
  • C. Yarra,S. Nagesh, O. D. Deshmukh, P. K. Ghosh, ‘Noise robust speech rate estimation using signal-to-noise ratio dependent sub-band selection and peak detection strategy. ’ The Journal of the Acoustical Society of America, 2019
Conference papers
  • S. Nagesh , A. Moreno, H. Ishikawa, G. Wollstein, J. S. Schuman, J. M. Rehg, ‘A Spatiotemporal Approach to Predicting Glaucoma Progression Using a CT-HMM.’ Machine Learning for Healthcare Conference (MLHC), 2019
  • P. B. Suresha,S. Nagesh, P. S. Roshan, A. P. Gaonkar, G. N. Meenakshi, P. K. Ghosh, ‘A high resolution ENF based multi-stage classifier for location forensics of media recordings. ’ Proceedings of IEEE National Conference on Communications (NCC), 2017
  • S. Nagesh, C. Yarra, O. D. Deshmukh, P. K. Ghosh, ‘A robust speech rate estimation based onthe activation profile from the selected acoustic unit dictionary. ’ Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2016
Workshop papers
  • S. Nagesh , A. Moreno, H. Ishikawa, G. Wollstein, J. S. Schuman, J. M. Rehg, ‘Modeling glaucomaprogression in a population from spatiotemporal measurements.’ Machine Learning for Health workshop at NeurIPS 2019