Advancing biomedical discovery and improving health through mobile sensor Big Data

MD2K’s Hillol Sarker accepts position with IBM

Posted February 03, 2017

Hillol Sarker, who earned his Ph.D. from the University of Memphis while working on MD2K research, has accepted a position with the IBM Thomas J. Watson Research Center as a Postdoctoral Researcher in Healthcare Effectiveness.

Hillol Sarker

Dr. Sarker, who received his Ph.D. in December, studied under Dr. Santosh Kumar at UofM. His work identified the precise timing for just-in-time stress interventions and developed a machine learning model to predict significant stress episodes before they occurred. His research also discovered a method to assess the availability of users to engage in just-in-time intervention in the natural environment. His doctoral dissertation was titled “From Markers to Interventions – The Case of Just-in-Time Stress Intervention

“Given the ubiquity of stress in daily life and its wide-ranging adverse impact on physical, psychological, behavioral and social health, I believe that being able to predict and prevent health-adverse behavior will improve health and quality of life for everyone,” he said.

While working with MD2K, he was lead author on two papers and co-author on two other papers that were accepted for publication at top-tier publication venues. (Citations below).

Dr. Sarker earned his master’s degree in computer science at UofM, and his bachelor’s degree in computer science and engineering at Bangladesh University of Engineering and Technology (BUET) in Dhaka, Bangladesh.

Prior to his graduate studies, Dr. Sarker was a team lead for Vantage Labs and a software engineer for Relisource Technologies Limited. He also worked as an engineer for Bangla Phone Ltd.


Soujanya Chatterjee, Karen Hovsepian, Hillol Sarker, Nazir Saleheen, Mustafa al’Absi, Gowtham Atluri, Emre Ertin, Cho Lam, Andrine Lemieux, Motohiro Nakajima, Bonnie Spring, David W. Wetter, Santosh Kumar (2016): 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, ACM, New York, NY USA, 2016, ISBN: 978-1-4503-4461-6.

Sarker, Hillol, Tyburski, Matthew, Rahman, Md Mahbubur, Hovsepian, Karen, Sharmin, Moushumi, Epstein, David H., Preston, Kenzie L., Furr-Holden, C. Debra, Milam, Adam, Nahum-Shani, Inbal, al’Absi, Mustafa, Kumar, Santosh (2016): 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, ACM, Santa Clara, California, USA, 2016, ISBN: 978-1-4503-3362-7.

Saleheen, Nazir, Ali, Amin Ahsan, Hossain, Syed Monowar, Sarker, Hillol, Chatterjee, Soujanya, Marlin, Benjamin, Ertin, Emre, al’Absi, Mustafa, Kumar, Santosh (2015): 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, ACM, Osaka, Japan, 2015, ISBN: 978-1-4503-3574-4.

H. Sarker, M. Sharmin, A.A. Ali, M.M. Rahman, R. Bari, S.M. Hossain, S. Kuma (2014): 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, ACM, Seattle, Washington, 2014, ISBN: 978-1-4503-2968-2.