• Dr. Santosh Kumar is internationally recognized for his expertise in wearable AI and is Tennessee's first state-endowed Chair of Excellence Professor in Computer Science at the University of Memphis.

• His current research focuses on developing new AI models to infer health states, daily behaviors, privacy risks, and mitigation approaches.

• Dr. Kumar has experience leading and participating in federally-funded multidisciplinary projects worth $50+ million that have involved 30+ investigators from 20+ universities. He currently leads multiple projects including the NIH NIBIB mHealth Center for Discovery, Optimization, and Translation of Temporally-Precise Interventions (the mDOT Center)    that provides the methods, tools, and infrastructure for researchers to discover, optimize and deploy temporally-precise mHealth interventions to address growing public health problems.

• In 2010, Popular Science magazine named him one of America’s ten most brilliant scientists under the age of 38 (called “Brilliant Ten”).

• Dr. Kumar's graduate students and postdocs are in research, development, and management at IBM Research, Samsung Research, Amazon, Apple, Facebook, and Universal Creative.

• He served on the advisory boards of NSF Engineering Research Center (ASSIST), NIH PRISMS Program, NIH Center for Technology and Behavioral Health (at Dartmouth), the Department of Computer Science & Engineering at the Ohio State University, and BioTrillion.

Recent Videos

 

 

 

 

Research Focus

Dr. Kumar has been leading mobile health (mHealth) projects since 2007 when his AutoSense project for sensor-based monitoring of stress and addictive behaviors in the field environment was selected in the Genes Environment & Health Initiative (GEI) common fund program of the National Institutes of Health (NIH). He also led an NIH Center of Excellence on Mobile Sensor Data-to-Knowledge (MD2K), funded through the big data-to-knowledge (BD2K) initiative (2014-2021) that developed innovative tools and open-source software to make it easier to collect, integrate, manage, visualize, analyze, and interpret health-related data generated by mobile and wearable sensors.  He is currently the Director of the NIH NIBIB mHealth Center for Discovery, Optimization, and Translation of Temporally-Precise Interventions (the mDOT Center) that provides the methods, tools, and infrastructure for researchers to discover, optimize, and deploy temporally-precise mHealth interventions to address growing public health problems.

mDOT's software platforms are being used in fourteen studies across 12 states to investigate stress, smoking, overeating, heart failure, oral hygiene, work performance, and cocaine use. Hundreds of terabytes of sensor data have been collected by Dr. Kumar and his collaborators and used to discover novel mHealth biomarkers such as stress, conversation, smoking, craving, cocaine use, brushing, and flossing, and sensor-triggered interventions.

In addition to direct experience with leading transdisciplinary mobile sensor research projects, Dr. Kumar has led national efforts to advance the field of mHealth. In 2011, he chaired the national meeting on “mHealth Evidence” organized by NIH and NSF to establish evidence requirements for mobile health, and in 2014 he organized and chaired an NSF-NIH workshop on identifying computing grand challenges in mHealth. He mentors faculty members across the country in mHealth as part of the annual NIH mHealth Summer Institutes.

For more information about Dr. Kumar's research, view the Research at mHealth Lab page. 

Google Scholar Citations: (see Google Scholar profile for the full current list)

Selected Recent Publications

• Saleheen, N., Ullah, M.A., N., Chakraborty, S., Ones, D.S., Srivastava, M., Kumar, S.  WristPrint: Characterizing User Re-identification Risks from Wrist-worn Accelerometry Data.  In the Proceedings of the ACM SIGSAC Conference on Computer and Communications Security.  2021:2807-2823. (.pdf)
• Akther, S., Saleheen, N., Saha, M., Shetty, V., Kumar, S. mTeeth: Identifying Brushing Teeth Surfaces Using Wrist-Worn Inertial Sensors.  In the Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.  2021; 5(2):1-25. (.pdf)
• Chatterjee, S., Moreno, A., Lizotte, S.L., Akther, S., Ertin, E., Fagundes, C.P., Lam, C., Rehg, J.M., Wan, N., Wetter, D.W. and Kumar, S., 2020. SmokingOpp: Detecting the Smoking 'Opportunity' Context Using Mobile Sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 4(1), pp.1-26. to appear in ACM UbiComp 2020. (.pdf)
• Akther, S., Saleheen, N., Samiei, S.A., Shetty, V., Ertin, E. and Kumar, S., 2019. mORAL: An mHealth model for inferring Oral Hygiene Behaviors in-the-wild using wrist-worn inertial sensors. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3(1), pp.1-25. presented at ACM UbiComp 2019. (.pdf) 
• Bari, R., Adams, R.J., Rahman, M.M., Parsons, M.B., Buder, E.H. and Kumar, S., 2018. rConverse: Moment by moment conversation detection using a mobile respiration sensor. Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, 2(1), pp.1-27. presented at ACM UbiComp 2019. (.pdf)
• Hossain, S.M., Hnat, T., Saleheen, N., Nasrin, N.J., Noor, J., Ho, B.J., Condie, T., Srivastava, M. and Kumar, S., 2017, November. mCerebrum: a mobile sensing software platform for development and validation of digital biomarkers and interventions. In the Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems, pp. 1-14. (.pdf)
• Saleheen, N., Chakraborty, S., Ali, N., Rahman, M.M., Hossain, S.M., Bari, R., Buder, E., Srivastava, M. and Kumar, S., 2016, September. mSieve: differential behavioral privacy in time series of mobile sensor data. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 706-717. (.pdf)
• Chatterjee, S., Hovsepian, K., Sarker, H., Saleheen, N., al'Absi, M., Atluri, G., Ertin, E., Lam, C., Lemieux, A., Nakajima, M. and Spring, B., 2016, September. mCrave: continuous estimation of craving during smoking cessation. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 863-874. (.pdf)
• Adams, R., Saleheen, N., Thomaz, E., Parate, A., Kumar, S. and Marlin, B., 2016, June. Hierarchical span-based conditional random fields for labeling and segmenting events in wearable sensor data streams. In International conference on machine learning, pp. 334-343. (.pdf)
• Kotz, D., Gunter, C.A., Kumar, S. and Weiner, J.P., 2016. Privacy and security in mobile health: a research agenda. Computer, 49(6), pp.22-30. (.pdf)
• Sarker, H., Tyburski, M., Rahman, M.M., Hovsepian, K., Sharmin, M., Epstein, D.H., Preston, K.L., Furr-Holden, C.D., Milam, A., Nahum-Shani, I. and Al'Absi, M., 2016, May. Finding significant stress episodes in a discontinuous time series of rapidly varying mobile sensor data. In Proceedings of the 2016 CHI conference on human factors in computing systems, pp. 4489-4501. (.pdf)
• Kumar, S., Abowd, G.D., Abraham, W.T., al’Absi, M., Gayle Beck, J., Chau, D.H., Condie, T., Conroy, D.E., Ertin, E., Estrin, D. and Ganesan, D., 2015. Center of excellence for mobile sensor data-to-knowledge (MD2K). Journal of the American Medical Informatics Association, 22(6), pp.1137-1142. (.pdf)
• Saleheen, N., Ali, A.A., Hossain, S.M., Sarker, H., Chatterjee, S., Marlin, B., Ertin, E., Al'Absi, M. and Kumar, S., 2015, September. puffMarker: a multi-sensor approach for pinpointing the timing of first lapse in smoking cessation. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 999-1010. (.pdf)
• Hovsepian, K., Al'Absi, M., Ertin, E., Kamarck, T., Nakajima, M. and Kumar, S., 2015, September. cStress: towards a gold standard for continuous stress assessment in the mobile environment. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing, pp. 493-504. (.pdf)
• Sharmin, M., Raij, A., Epstien, D., Nahum-Shani, I., Beck, J.G., Vhaduri, S., Preston, K. and Kumar, S., 2015, September. Visualization of time-series sensor data to inform the design of just-in-time adaptive stress interventions. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 505-516. (.pdf)
• Sarker, H., Sharmin, M., Ali, A.A., Rahman, M.M., Bari, R., Hossain, S.M. and Kumar, S., 2014, September. Assessing the availability of users to engage in just-in-time intervention in the natural environment. In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 909-920. (.pdf)
• Hossain, S.M., Ali, A.A., Rahman, M.M., Epstein, E.E.D., Kennedy, A., Preston, K., Umbricht, A., Chen, Y. and Kumar, S., 2014, April. Identifying drug (cocaine) intake events from acute physiological response in the presence of free-living physical activity. In IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks, pp. 71-82. IEEE. (.pdf)
• Rahman, M.M., Bari, R., Ali, A.A., Sharmin, M., Raij, A., Hovsepian, K., Hossain, S.M., Ertin, E., Kennedy, A., Epstein, D.H. and Preston, K.L., 2014, September. Are we there yet? Feasibility of continuous stress assessment via wireless physiological sensors. In Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 479-488. (.pdf) 
• Vhaduri, S., Ali, A., Sharmin, M., Hovsepian, K. and Kumar, S., 2014, September. Estimating drivers' stress from GPS traces. In Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 1-8. (.pdf)
• Kumar, S., Nilsen, W., Abernethy, A., Atienza, A.A., Patrick, K., Pavel, M., Riley, W.T., Shar, A., Spring, B., Spruijit-Metz, D. and Hedeker, D., 2013. mHealth evidence workshop—exploring innovative methods to evaluate the efficacy and safety of mobile health. Am J Prev Med, 45(2), pp.228-236. (link)
• Kumar, S., Nilsen, W., Pavel, M. and Srivastava, M., 2012. Mobile health: Revolutionizing healthcare through transdisciplinary research. Computer, 46(1), pp.28-35. (link)
  
• Ali, A.A., Hossain, S.M., Hovsepian, K., Rahman, M.M., Plarre, K. and Kumar, S., 2012, April. mPuff: automated detection of cigarette smoking puffs from respiration measurements. In Proceedings of the 11th international conference on Information Processing in Sensor Networks, pp. 269-280. (.pdf, slides)
• Ertin, E., Stohs, N., Kumar, S., Raij, A., Al'Absi, M. and Shah, S., 2011, November. AutoSense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, pp. 274-287. (.pdf) 
• Rahman, M.M., Ali, A.A., Plarre, K., Al'Absi, M., Ertin, E. and Kumar, S., 2011, October. mConverse: Inferring conversation episodes from respiratory measurements collected in the field. In Proceedings of the 2nd Conference on Wireless Health, pp. 1-10. (.pdf)  Nominated for Best Paper Award
• Musthag, M., Raij, A., Ganesan, D., Kumar, S. and Shiffman, S., 2011, September. Exploring micro-incentive strategies for participant compensation in high-burden studies. In Proceedings of the 13th international conference on Ubiquitous computing, pp. 435-444. (.pdf) 
• Plarre, K., Raij, A., Hossain, S.M., Ali, A.A., Nakajima, M., Al'Absi, M., Ertin, E., Kamarck, T., Kumar, S., Scott, M. and Siewiorek, D., 2011, April. Continuous inference of psychological stress from sensory measurements collected in the natural environment. In Proceedings of the 10th ACM/IEEE international conference on information processing in sensor networks (pp. 97-108). IEEE. (.pdf, slides) Nominated for Best Paper Award
• Raij, A., Ghosh, A., Kumar, S. and Srivastava, M., 2011, May. Privacy risks emerging from the adoption of innocuous wearable sensors in the mobile environment. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 11-20. (.pdf)
• Guha, S., Plarre, K., Lissner, D., Mitra, S., Krishna, B., Dutta, P. and Kumar, S., 2012. Autowitness: locating and tracking stolen property while tolerating gps and radio outages. ACM Transactions on Sensor Networks (TOSN), 8(4), pp.1-28. (.pdf, slides) Nominated for Best Paper Award