• Dr. Santosh Kumar is Tennessee's first state-endowed Chair of Excellence Professor in Computer Science at the University of Memphis with internationally recognized expertise in mobile sensor big data for health, wellness, and workplace performance.

• His current research seeks to revolutionize healthcare research and practice via innovative machine-learning models for infering daily health and behavior from wearable sensors.

• Dr. Kumar has experience leading and participating in federally-funded multidisciplinary projects worth $45+ million that has involved 30+ investigators from 20+ universities. He currently leads multiple projects including the NIH Center of Excellence in Mobile Sensor Data-to-Knowledge (MD2K) that involves investigators in computing, engineering, behavioral science, and medicine from 12 universities.

• 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 graduated students and postdocs are in research, development, and management at IBM Research, Samsung Research, Amazon, Apple, and Universal Creative.

• He is an advisory board member of NSF Engineering Research Center (ASSIST), NIH PRISMS Program, NIH Center for Technology and Behavioral Health (at Dartmouth), 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 is currently leading an NIH Center of Excellence on Mobile Sensor Data-to-Knowledge (MD2K), funded through the big data-to-knowledge (BD2K) initiative. MD2K is a truly transdisciplinary project in which Santosh leads a team of 20+ investigators from 12 universities who come from diverse but complementary disciplines including computer science, engineering, statistics, public health, medicine, and behavioral science. MD2K has 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. The goal of these big data solutions is to reliably quantify physical, biological, behavioral, social, and environmental factors that contribute to health and disease risk.

MD2K’s software platforms are being used in fourteen studies at across 11 states to investigate stress, smoking, overeating, heart failure, oral hygiene, work performance, and cocaine use. Hundreds of terabytes of sensor data collected by MD2K has been 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

  • 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 Technologies4(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 Technologies3(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 technologies2(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 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. Computer49(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 Association22(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 Med45(2), pp.228-236. (link)
  • Kumar, S., Nilsen, W., Pavel, M. and Srivastava, M., 2012. Mobile health: Revolutionizing healthcare through transdisciplinary research. Computer46(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. (.pdfslides)
  • 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. (.pdfslidesNominated 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. (.pdfslidesNominated for Best Paper Award



Dr. Santosh Kumar

Director, NIH Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K)
Professor and Lillian & Morrie Moss Chair of Excellence | Department of Computer Science
The University of Memphis

Students at mHealth Systems Lab work with students, post-docs, and faculty from10+ universities via MD2K. See our research philosophy if interested in joining

Ph.D. Students (11)

Rummana Bari; Nazir Saleheen; Soujanya Chaterjee; Azim Ullah; Sayma Akther; Rabin Banjade; Shiplu Hawlader; Somnath Mitra; Mithun Saha; Sameer Neupane; Hosneara Ahmed

Alumni (13)

Syed Monowar Hossain (Ph.D., 2017) - MD2K

Nusrat Nasrine (M.S., 2017) - MD2K

Hillol Sarker (Ph.D., 2016) - IBM Research

Mahbubur Rahman (Ph.D., 2016) - Samsung

Amin Ahsan Ali (Ph.D., 2014) - University of Dhaka

Moushumi Sharmin (Post-doc, 2013-15) - Western Washington University

Andrew Raij (Post-doc, 2009-10) - Universal Creative

Karen Hovsepian (Post-doc, 2011-12) - Amazon

Somnath Mitra (M.S., 2012) - eBay

Animikh Ghosh (M.S., 2010) - Infosys Labs, India

Maheshbabu Satharla (M.S., 2010)

Bhagavathy Krishna (M.S., 2009) - Apple

Tim Henry (B.S., 2008) - FedEx

MD2K Staff

Dr. Timothy Hnat - Chief Software Architect

Dr. Syed 'Monowar' Hossain - Lead Software Engineer

Nusrat Nasrin - Software Engineer

Dr. Anandatirtha Nandugudi - Data Science Software Engineer

Dr. Nasir Ali - Research Assistant Professor

Joseph Biggers - Director of Administrative Operations

Lyndsey Rush - Project Coordinator

Cheryl Hayes - Business Officer

Brian Ahern - Technical Writer/Training Specialist

Shahin Samiei - Associate Director, Research & Studies