Postdoctoral fellow
Statistics Department
Harvard University
Ph.D. in Information Science, Cornell University

I am interested in adaptive intervention in the mobile health space. I build computational tools that analyze human behavior from phone data (e.g., sensors, self-input) and then intervenes in real time in ways that are personalized and contextualized to users' needs, daily routine, and the environments they live in.

Keywords: Adaptive health intervention, Mobile Sensing, Context-aware computing, Reinforcement learning, User-centered design


CV Research statement | Teaching statement | Google Scholar


Short bio
I am a postdoc at Harvard University, where I am advised by Susan Murphy. I completed my PhD at Cornell University. My PhD advisor was Tanzeem Chourdhury. I also had the opportunity to work with many amazing collaborators (Andrew Campbell, Predrag Klasnja, Nic Lane, Lama Nachman, Mi Zhang). I received my bacheor degree in computer science from Bangladesh University of Enigeering and Technology (BUET).


Past and upcoming invited talks






PUBLICATIONS

Citation count: 1679 (updated Sep 20, 2019)

See Google scholar for a more up-to-date publication list

Refereed conference and journal publications


  1. 18.

    Mashfiqui Rabbi, Predrag Klasnja, Tanzeem Choudhury, Ambuj Tewari, and Susan Murphy. Optimizing mHealth Interventions with a Bandit Mobile Sensing and Digital Phenotyping: New Developments in Psychoinformatics, Springer: Berlin [preprint]
  2. 18.

    Mashfiqui Rabbi, Min SH Aung, Geri Gay, M. Cary Reid, and Tanzeem Choudhury. Feasibility and Acceptability of Mobile Phone–Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults Journal of Medical Internet Research 20, no. 10 (2018): e10147. [Impact factor 4.6] [pdf]
  3. 17.

    Mashfiqui Rabbi, Meredith Philyaw Kotov, Rebecca Cunningham, Erin E. Bonar, Inbal Nahum-Shani, Predrag Klasnja, Maureen Walton, and Susan Murphy Toward Increasing Engagement in Substance Use Data Collection: Development of the Substance Abuse Research Assistant App and Protocol for a Microrandomized Trial Using Adolescents and Emerging Adults JMIR Research Protocols 7, no. 7 (2018): e166. [pdf]
  4. 16.

    Mashfiqui Rabbi, Min Hane Aung, and Tanzeem Choudhury. Towards health recommendation systems: an approach for providing automated personalized health feedback from mobile data. In: Rehg J., Murphy S., Kumar S. (eds) Mobile Health. Springer, Cham, 2017 [pdf]
  5. 15.

    Eun Kyoung Choe, Saeed Abdullah, Mashfiqui Rabbi, Edison Thomaz, Daniel A. Epstein, Felicia Cordeiro, Matthew Kay et al. Semi-automated tracking: A balanced approach for self-monitoring applications. IEEE Pervasive Computing 16, no. 1 (2017): 74-84.[pdf]
  6. 14.

    Aung, Min S. Hane, Faisal Alquaddoomi, Cheng-Kang Hsieh, Mashfiqui Rabbi, Longqi Yang, John P. Pollak, Deborah Estrin, and Tanzeem Choudhury. Leveraging multi-modal sensing for mobile health: a case review in chronic pain. IEEE Journal of Selected Topics in Signal Processing 10, no. 5 (2016): 962-974.[ pdf]
  7. 13.

    Mashfiqui Rabbi, Jean Costa, Fabian Okeke, Max Schachere, Mi Zhang, and Tanzeem Choudhury. An Intelligent Crowd-worker Selection Approach for Reliable Content Labeling of Food Images. The Proceedings of Wireless Health 2015 [pdf].
  8. 12.

    Mashfiqui Rabbi, Min Hane Aung, Mi Zhang and Tanzeem Choudhury. MyBehavior: Automatic Personalized Health Feedback from User Behavior and Preference using Smartphones. The 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Ubicomp 2015) [pdf]
  9. 11.

    Mashfiqui Rabbi, Angela Pfammatter, Mi Zhang, Bonnie Spring, and Tanzeem Choudhury. Automated Personalized Feedback for Physical Activity and Dietary Behavior Change With Mobile Phones: A Randomized Controlled Trial on Adults. JMIR mHealth uHealth 2015;3(2):e42 [Impact factor 4.7] [pdf]
  10. 10.

    Phil Adams, Mashfiqui Rabbi, Tauhidur Rahman, Mark Matthews, Amy Voida, Geri Gay, Tanzeem Choudhury, and, Stephen Voida. Towards Personal Stress Informatics: Comparing Minimally Invasive Techniques for Measuring Daily Stress in the Wild. International Conference on Pervasive Computing Technologies for Healthcare, 2014 [pdf]
  11.  9.

    Nicholas D. Lane, Mu Lin, Mashfiqui Rabbi, Xiaochao Yang, Hong Lu, Giuseppe Cardone, Shahid Ali, Ethan Berke, Andrew T. Campbell, Tanzeem Choudhury Bewell: Sensing sleep, physical activities and social interactions to promote wellbeing. Mobile Networks and Applications 19, no. 3 (2014): 345-359 [pdf]
  12.  8.

    Mu Lin, Nicholas Lane, Mashfiqui Rabbi, Xiaochao Yang, Hong Lu, Giuseppe Cardone, Shahid Ali, Afsaneh Doryab, Ethan Berke, Andrew Campbell, and Tanzeem Choudhury. BeWell+: Multi-dimensional Wellbeing Monitoring with Community-guided User Feedback and Energy Optimization Appears in the Proceedings of Wireless Health 2012, October 2012 [pdf]
  13.  7.

    Hong Lu, Mashfiqui Rabbi, Gokul Chittaranjan, Denise Frauendorfer, Marianne Schmidt, Andrew Campbell, Daneil Gatica-Perez, and Tanzeem Choudhury. StressSense: Detecting Stress in Unconstrained Acoustic Environments using Smartphones. Appears in the Proceedings of Ubicomp 2012, September 2012 [pdf]
  14.  6.

    Mashfiqui Rabbi, Shahid Ali, Tanzeem Choudhury, and Ethan Berke. Passive and In-situ Assessment of Mental and Physical Well-being using Mobile Sensors. To appear in the Proceedings of Ubicomp 2011, September 2011. Beijing, China. [pdf]
  15.  5.

    Ethan Berke, Tanzeem Choudhury, Shahid Ali, and Mashfiqui Rabbi. Objective Sensing of Activity and Sociability: Mobile Sensing in the Community. Appears in the Annals of Family Medicine, Volume 9, Issue 4, Pages 344-350, July 2011. [pdf] [commentary]
  16.  4.

    Nicholas D. Lane, Mashfiqui Rabbi, Mu Lin, Xiaochao Yang, Afsaneh Doryab, Hong Lu, Shahid Ali, Tanzeem Choudhury, Andrew Campbell, and Ethan Berke, BeWell: A Smartphone Application to Monitor, Model and Promote Wellbeing, Pervasive Health 2011-- 5th International ICST Conference on Pervasive Computing Technologies for Healthcare, Dublin, 23-26 May 2011 [pdf]
  17.  3.

    Andrew T. Campbell, Tanzeem Choudhury, Shaohan Hu, Hong Lu, Matthew Mukerjee, Mashfiqui Rabbi, and Rajeev Raizada. NeuroPhone: Brain-Mobile Phone Interface using a Wireless EEG Headset. Appears in the Proceedings of MobiHeld 2010 [pdf]
  18.  2.

    M Jawaherul Alam, Md Abul Hassan Samee, Mashfiqui Rabbi, and Md Saidur Rahman. Minimum-Layer Upward Drawings of Trees. J. Graph Algorithms and Applications 14, no. 2 (2010): 245-267. [pdf]
  19.  1.

    M Jawaherul Alam, Mashfiqui Rabbi, and Md Saidur Rahman. Upright Drawings of Planar Graphs on Three Layers. Journal of Applied Mathematics and Informatics, 28(56): 1347-1358, 2010 [pdf]




Lightly reviewed posters and workshop papers


  1. 6.

    Mashfiqui Rabbi, Meredith Philyaw-Kotov, Jinseok Lee, Anthony Mansour, Laura Dent, Xiaolei Wang, Rebecca Cunningham et al. SARA: a mobile app to engage users in health data collection In Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers, pp. 781-789. ACM, 2017. [pdf]
  2.  5.

    Mashfiqui Rabbi, Thiago Caetano, Jean Costa, Saeed Abdullah, Mi Zhang, and Tanzeem Choudhury. SAINT: A Scalable Sensing and Inference Toolkit. Hotmobile 2015 poster [pdf]
  3.  4.

    Mashfiqui Rabbi, Syed Ishtiaque Ahmed. Sensing stress network for social coping CSCW Interactive Poster Session, 2014 [pdf | poster]
  4.  3.

    Steven Voida, Mark Matthews, Saeed Abdullah, Mengxi C. Chi, Mattew Green, W. J. Jang, D. Hu, Jon Weinrich, P. Patil, Mashfiqui Rabbi, et al. Moodrhythm: tracking and supporting daily rhythms.In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication, pages 67-70. ACM, 2013 [pdf]
  5.  2.

    Stephen Voida, Tanzeem Choudhury, Geri Gay, Mark Matthews, Phil Adams, Mashfiqui Rabbi, JP Pollak, Mengxi Chi, Matthew Green, Andrew Campbell, Nic Lane, and Hong Lu. Personal Informatics Can Be Stressful: Collecting, Reflecting, and Embedding Stress Data in Personal Informatics. To Appear in the Proceeding of Personal Informatics Workshop, CHI. [pdf]
  6.  1.

    Mashfiqui Rabbi, Chien wen Yuan, and Kirsikka Kaipaien. An exploratory study to identify opportune moments in everyday life to promote healthy eating.Poster in ISBNPA, 2013 [pdf]


Ph.D. Thesis







PROJECT HIGHLIGHTS




MyBehavior

MyBehavior learns a user's physical activity and dietary behavior from phone data and suggests personalized changes to these behaviors. MyBehavior is the first app that combined sensor data with reinforcement learning and provided personalized health recommendations.


See our JMIR and Ubicomp 2015 papers for more details on MyBehavior. We recently extended MyBehavior for chronic back pain and published an article in JMIR.







SARA (Substance Abuse Research Assistant)

Self-report adherence of mobile health apps are generally low. SARA (Substance Abuse Research Assistant) tries to increase self-report adherence with timely rewards. The target population of SARA is adolescents and younger adults at high risk of substance abuse. We recently conducted a micro-randomized trial with SARA. Data analysis of the trial is currently underway.

See our JMIR paper for more details on SARA.






ReVibe: Recalling daily moments with context

ReVibe app tries to increase self-reports by lowering burden of Ecological Momentary Assessments (EMAs). Instead of interrupting several times in a day with EMAs, ReVibe asks to recall these moments in the evening. ReVibe also provides contextual information (e.g., location, movement) from these same moments to improve recall accuracy.

In a 14-day pilot study with 54 participants, we found contextual information increased recall accuracy by 5.6%. The recall completion rates were also 25% higher than EMAs. A manuscript with these results is under preparation.







Past projects (currently inactive)

Click on the project descriptions below for more details.

StressSense

StressSense app infers stressful conversations in continuously-sensed audio data from phone microphones.

BeWell

BeWell app provides multi-dimensional feedback on daily physical activity, sleep and socialization.

Mental health sensing

A 2011 project on passive and in-situ sensing of mental health using audio and accelerometer data.



MoodRhythm

MoodRhythm focuses on improving symptoms of bipolar disorder by stabilizing daily routines.

SAINT

SAINT is an easy-to-use toolkit for reusing past sensing codes and creating new ones.

MyPersonalCoach

MyPersonalCoach extends MyBehavior by providing the right contextual intervention at the right moment.







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