Aarti Sathyanarayana
Assistant Professor
Research interests
- Digital health
- Wearable devices
- Digital phenotyping
- Digital biomarker discovery
Education
- PhD in Computer Science, University of Minnesota
- MS in Computer Science, University of Minnesota
- BS in Mathematics, University of Minnesota
Biography
Aarti Sathyanarayana is an assistant professor in the Khoury College of Computer Sciences and the Bouvé College of Health Sciences at Northeastern University, based in Boston.
Sathyanarayana’s research strives to improve human health and performance through digital phenotyping and biomarker discovery. She aims to translate enigmatic digital health data collected from smartphones, wearables, and biomedical devices into actionable insights for clinical care and personal wellness. Her work has developed new signal processing and machine learning algorithms for time variant health data analysis. Sathyanarayana explores these and other goals through Northeastern's SATH Lab, which she directs.
In addition to her role at Northeastern, Sathyanarayana also holds appointments in the Department of Biostatistics at Harvard University's T.H. Chan School of Public Health, as well as the Clinical Data Animation Center at Massachusetts General Hospital and Harvard Medical School.
Sathyanarayana received her PhD in computer science from the University of Minnesota, where her dissertation was selected for the university’s Doctoral Dissertation Award. Since then, her work has earned junior investigator awards from the National Center of Women and Information Technology, the American Medical Informatics Association, the American Epilepsy Society, and the American Clinical Neurophysiology Society. Her expertise has also led her to positions at Apple, Intel, the Mayo Clinic, and Boston Children’s Hospital.
Recent publications
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Sleep Staging from Airflow Signals Using Fourier Approximations of Persistence Curves
Citation: Shashank Manjunath, Hau-Tieng Wu, Aarti Sathyanarayana. (2024). Sleep Staging from Airflow Signals Using Fourier Approximations of Persistence Curves CoRR, abs/2411.07964. https://doi.org/10.48550/arXiv.2411.07964 -
Detection of Sleep Oxygen Desaturations from Electroencephalogram Signals
Citation: Shashank Manjunath, Aarti Sathyanarayana. (2024). Detection of Sleep Oxygen Desaturations from Electroencephalogram Signals CoRR, abs/2405.09566. https://doi.org/10.48550/arXiv.2405.09566 -
Investigating Social Interaction Patterns with Depression Severity across Different Personality Traits Using Digital Phenotyping
Citation: Ohida Binte Amin, Varun Mishra , Aarti Sathyanarayana. (2023). Investigating Social Interaction Patterns with Depression Severity across Different Personality Traits Using Digital Phenotyping ACIIW, 1-4. https://doi.org/10.1109/ACIIW59127.2023.10388164 -
Modeling Messaging Metadata to Identify Digital Disagreements among Non-incarcerated Adolescents in the Juvenile Justice System
Citation: Harshit Pandey, Christie Rizzo, Charlene Collibee, Aarti Sathyanarayana. (2023). Modeling Messaging Metadata to Identify Digital Disagreements among Non-incarcerated Adolescents in the Juvenile Justice System ACII, 1-8. https://doi.org/10.1109/ACII59096.2023.10388170 -
Topological Data Analysis of Electroencephalogram Signals for Pediatric Obstructive Sleep Apnea
Citation: Shashank Manjunath, Jose A. Perea, Aarti Sathyanarayana. (2023). Topological Data Analysis of Electroencephalogram Signals for Pediatric Obstructive Sleep Apnea CoRR, abs/2304.14853. https://doi.org/10.48550/arXiv.2304.14853 -
Topological Data Analysis of Electroencephalogram Signals for Pediatric Obstructive Sleep Apnea
Citation: Shashank Manjunath, Jose A. Perea, Aarti Sathyanarayana. (2023). Topological Data Analysis of Electroencephalogram Signals for Pediatric Obstructive Sleep Apnea CoRR, abs/2304.14853. https://doi.org/10.48550/arXiv.2304.14853 -
Measuring the Effects of Sleep on Epileptogenicity with Multiscale Entropy
Citation: Sathyanarayana, Aarti, Rima El Atrache, Michele Jackson, Kenneth Mandl, Tobias Loddenkemper, William Bosl. “Measuring the Effects of Sleep on Epileptogenicity with Multiscale Entropy.” Clinical Neurophysiology 132.9 (2021). doi:10.1016/j.clinph.2021.06.001 -
Nonlinear Analysis of Visually Normal EEGs to Differentiate Benign Childhood Epilepsy with Centrotemporal Spikes
Citation: Sathyanarayana, Aarti, Rima El Atrache, Michele Jackson, Aliza Alter, Kenneth Mandl, Tobias Loddenkemper, William Bosl. “Nonlinear Analysis of Visually Normal EEGs to Differentiate Benign Childhood Epilepsy with Centrotemporal Spikes.” Scientific Reports (2020). doi:10.1038/s41598-020-65112-y -
Benchmark on a large cohort for sleep-wake classification with machine learning techniques
Citation: João R. M. Palotti, Raghvendra Mall, Michaël Aupetit , Michael Rueschman, Meghna Singh, Aarti Sathyanarayana, Shahrad Taheri, Luis Fernández-Luque. (2019). Benchmark on a large cohort for sleep-wake classification with machine learning techniques npj Digit. Medicine, 2. https://doi.org/10.1038/s41746-019-0126-9 -
A Digital Biomarker for Detection of Benign Childhood Epilepsy with Centrotemporal Spikes
Citation: Aarti Sathyanarayana, Rima El Atrache, Michele Jackson, Ivan Sanchez Fernandez, Kenneth D. Mandl, Tobias Loddenkemper, William J. Bosl. (2019). A Digital Biomarker for Detection of Benign Childhood Epilepsy with Centrotemporal Spikes AMIA. https://knowledge.amia.org/69862-amia-1.4570936/t006-1.4574499/t006-1.4574500/3201516-1.4574576/3202070-1.4574573 -
The Science of Sweet Dreams: Predicting Sleep Efficiency from Wearable Device Data
Citation: Sathyanarayana, Aarti, Luis Fernandez-Luque, Jaideep Srivastava. “The Science of Sweet Dreams: Wearable Devices and Sleep Medicine.” IEEE Computer Magazine (March 2017). doi:10.1109/MC.2017.91 -
Sleep Quality Prediction From Wearable Data Using Deep Learning
Citation: Sathyanarayana, Aarti, Shafiq Joty, Luis Fernandez-Luque, Ferda Ofli, Jaideep Srivastava, Ahmed Elmagarmid, Teresa Arora, and Shahrad Taheri. “Sleep Quality Prediction From Wearable Data Using Deep Learning.” JMIR mHealth and uHealth 4, no. 4 (2016) doi:10.2196/mhealth.6562
