Postdoc/Applied Research Scientist
Health AI
Apple Inc.
Postdoc at Statistics Department, Harvard University (2016-19)
Ph.D. in Information Science, Cornell University (2016)

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 | Google Scholar

Short bio
I am currently a Postdoc/Applied Research Scientist at Health AI at Apple, Inc. From 2016-19, I was a postdoc at Harvard University, where I was advised by Susan Murphy. I completed my PhD at Cornell University (2016). 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

Nov, 2019: HCII seminar at CMU
Oct, 2019: mHealth Research Affinity Group at Children's Hospital of Philadelphia
Oct, 2019: HIV working group. at Harvard Biostatistics Dept

slides

Sep, 2019: School of Biomedical Informatics at University of Texas Health Science Center
June, 2019: Turi at Apple Inc., Seattle, WA [slides]
April, 2019: CS+X colloquium at Northwestern University [slides]
May 2, 2018: Prevention Center at Penn State University [slides]
Oct 23, 2017: Methodology Center at Penn State University [slides]
Sep 26, 2017: Statistics seminar at Harvard University [slides]
Sep, 2013: HCI seminar at University of Rochester [slides]
Sep, 2011: Brown-bag series at Cornell University [slides]

PUBLICATIONS

Citation count: 2208 (updated January 31, 2021)

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

Refereed conference and journal publications

23.

Lara N Coughlin, Inbal Nahum-Shani, Meredith L Philyaw-Kotov, Erin E Bonar, Mashfiqui Rabbi, Predrag Klasnja, Susan Murphy, Maureen A Walton Developing an Adaptive Mobile Intervention to Address Risky Substance Use Among Adolescents and Emerging Adults: sability Study JMIR Mhealth Uhealth 2021;9(1):e24424 [Impact factor 4.3] [pdf]
22.

Shuang Li, Alexandra M Psihogios, Elise R McKelvey, Annisa Ahmed, Mashfiqui Rabbi, Susan Murphy Microrandomized trials for promoting engagement in mobile health data collection: Adolescent/young adult oral chemotherapy adherence as an example Current Opinion in Systems Biology, (2020), Elsevier
21.

Mashfiqui Rabbi, Katherine Li, H Yanna Yan, Kelly Hall, Predrag Klasnja, Susan Murphy ReVibe: A Context-assisted Evening Recall Approach to Improve Self-report Adherence Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 3, 4, 1--27 (2019), ACM New York, NY, USA:[pdf]

20.

Marianne Menictas*, Mashfiqui Rabbi*, Predrag Klasnja, Susan Murphy Artificial intelligence decision-making in mobile health The Biochemist, 41, 5, 20--24 (2019):[pdf] * authors equally contributed

19.

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]

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]
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]
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]
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]
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]
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].
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]
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.

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]
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]
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]
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]
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]
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]
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]
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]
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]
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

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]
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]
4.

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

●

Mashfiqui Rabbi, Automated Mobile Systems For Personalized Health Feedback Cornell University, 2016

PROJECT HIGHLIGHTS

(Click on the image to enlarge)

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.

(Click on the image to enlarge)

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.

(Click on the image to enlarge)

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.

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see more

StressSense

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

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see more

BeWell

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

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see more

Mental health sensing

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

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see more

MoodRhythm

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

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see more

SAINT

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

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see more

MyPersonalCoach

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

MyPersonalCoach is similar to the MyBehavior project, with an important addon - MyBehavior suggestions are pushed in the right time and context to a smartwatch to make user's active.

The suggestions in the right moment promote two kinds of actions (i) repetition of same action to promote habit building (ii) novel or opportunisitic action to make user's active instantly.

I did this project for my 2014 internship at Intel. This project is currently inactive because of my lack of free time. I will expand this work in future. Here are a few slides on MyPersonalCoach link.

Recently in mobile and wearable computing, we have seen a rise in continuous sensing across different types of sensors and in device processing. Combination of these array of sensors and corresponding inference have capability to generate detailed non-trivial multidimensional information about the environment and the individual carrying the device. Also, there is a growing number of third-party applications that use these information for context-aware-computing, personal well-being, and just-in-time health care.

With SAINT, we propose a scalable sensing and real-time sensing and inference toolkit for mobile devices. SAINT uses a client-server architecture. The server can support clients with different sensing (e.g., accelerometer and location) and inference (e.g., human speech) streams on-demand. If more than one client ask for the same sensing or inference data then the server performs necessary computation (e.g., recognizing human speech) once and serves all the clients needing such data with inter process communication. Furthermore, internally SAINT server enables easy sharing of sensing and inference data among different recognition modules (e.g., sleep sensing can use movement and speech inference data) over a simple middle-man >bus like structure. This bus architecture makes any sensing or inference streams available for consumption for any other inference module. This sharing enables complex inference possible by reusing components that has been built before. We show that bus and client server architecture incur low overhead while enabling exponential increase in cpu time and power usage on the number of clients. Also, developers found SAINT framework easy to program and deploy.

Related Publications:

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

Bipolar disorder is a common form of mental illness. Medication is the current gold standard for bipolar depression treatment, but medication can have side effect. Social Rhythm Therapy (SRM) is a purely behavioral approach to reduce bipolar depression symptoms. SRM focuses on stabilizing routines to improve biploar symptoms. MoodRhythm is a mHealth version of SRM which facilitates stabilizing routine by monitoring daily routine behaviors and rewarding positive changes.

My role in this project was to infer daily behavior from sensor data.

Related Publications:

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
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.

StressSense is a continuous sensing application that captures audio from phone microphones and classifies the audio into stressful or regular conversations. The basic intuition is that, during stressful episodes, the airflow through the vocal cord changes (e.g., pitch, speaking rate, teager energy) and this airflow dynamics can be modeled and classified.

We have an implementation of StressSense on the Android platform, so please let us know if you are interested to try StressSense (mrabbi@fas.harvard.edu).

Related publication
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.

This 2011 project captured fine grained motion and privacy-sensitive audio data from Intel MSP, and derived different metrics that correlated with commonly used surveys for assessing mental well-being.

This project later inspired the BeWell and the StudentLife project.

Related Publications:

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.
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. [commentary]

BeWell is a continuous sensing application that infers physical activity, sleep and social interaction from mobile sensor data. BeWell then aggregates the number of minutes of physical activity, social interaction, and sleep in a day, and populates a virtual aquarium in the background of the phone.

BeWell was a milestone project that tied multi-modal sensing with health feedback. It kickstarted a flurry of follow-up projects that include MyBehavior and the StudentLife project at Dartmouth.

Related Publications:

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.
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.
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