About me

sb_1I am a PhD candidate in the Personal Health Informatics program (affiliated with Khoury College of Computer Sciences and Bouve College of Health Sciences) at Northeastern University. I am advised by Stephen Intille from the mHealth Research Group.

I work at the intersection of human-computer interaction, mobile health, and personal informatics to explore new ways of studying behaviors, experiences, and states in the wild.

I am fortunate to work with an amazing group of interdisciplinary researchers including Genevieve Dunton (Univ. of Southern California), Seth Cooper (Northeastern Univ.), Donald Hedeker (Univ. of Chicago), Andrea Parker (Georgia Tech), and Justin Manjourides (Northeastern Univ.).

C.V.  |  Email  |  Google Scholar  |  GitHub  |  LinkedIn


Research work


uEMA data exampleTIME Study: Temporal Influences on Movement and Exercise Study

The goal of TIME study is to examine different micro-temporal processes that influence health behavior change and maintenance in young adults. We developed a smartphone and a smartwatch app (called TIME app) to capture variations in different health constructs using passive sensors, app usage, mobile-based ecological momentary assessment, and microinteractions-based ecological momentary assessment. Read more …


Screenshot 2020-03-13 at 1.28.35 AMSignaligner Pro: Multi-day raw sensor data exploration and annotation

Signaligner-Pro is an interactive tool for exploration and annotation of raw accelerometer data. The tool can be used by researchers interested in using raw accelerometer data to support research in sensor-based activity recognition/machine learning, exercise science, and sleep quality among others. It can be used to visualize, explore, and annotate multi-day and multi-sensor high-sampling-rate raw sensor data. Read more …


sitting-right_nowμEMA: Microinteractions-based ecological momentary assessment on smartwatch

μEMA (micro-EMA) is microinteractions-based ecological momentary assessment (EMA) tool for digital phenotyping where all the self-report prompts are single questions with a “Yes/No” type answer. These self-report prompts can be answered with quick glanceable microinteractions that take ~3s (like checking the time on the watch). With this cognitive simplicity, μEMA could gather self-reported behavioral data with much higher temporal density than traditional EMA. Read more …


Mix-WILD: A program for mixed model analysis with intensive longitudinal data

MixWILD (Mixed model analysis with Intensive Longitudinal Data) is a desktop application for examining the effects of variance and slope of time-varying variables in intensive longitudinal data, especially the ones collected using ecological momentary assessments (EMAs) and wearable sensors. Instead of using programming interfaces such as SAS NLMIXED, Python or R, MixWILD allows researchers to use a simple GUI to build complex multilevel models. Read more …


Human-computation game for sensor data annotation

We explored human-computation games to crowdsource annotations on raw accelerometer data from casual game players. We designed two games Mobots and Signaligner  to enable activity recognition researchers to generate labeled datasets from the real-world acceleration data with the help of casual game players in the crowd.  Read more …


Sideline: An in situ concussion assessment tool

Sideline allows parents, coaches of informal sports clubs, and players to assess the possibility of a concussion on the sports field. It is implemented on the Android platform using clinically approved self-report symptom assessments, working memory, visual memory, reaction time, and physical balance tests. Read more …


Finding Astro: Mobile exergame to promote physical activity

Finding Astro is an Android-based experimental exergame intended to promote small bouts of physical activity among youth. The player finds a missing astronaut in space by moving the smartphone in 3-D space. The direction of the motion is directed by specific exercise moves designed to induce exertion. Read more …


Exploring social capital in low-SES communities using mobile technology

We conducted a qualitative study of a local community program to empower low-income neighborhood residents through a mobile application. Our findings highlight how the mobile technology and offline socio-organizational mechanisms could work in tandem to create gateways for capital building, sparking connections (to people and opportunities) that residents leverage with varying motivations and outcomes. Read more …


User experience (UX) research at Samsung Electronics

I worked on a variety of projects at Samsung electronics in the areas of personal health, text input, mobile media-multitasking, and alternative OS UIs for low cost smartphones in emerging markets. Here, I briefly discuss some of the work done during my stint there. Read more …


Digital chameleons: Investigating the effect of mimicry by a virtual agent

As part of my research internship at the Eindhoven University of Technology (TU/e), I carried out a between-subject experiment to study the effect of mimicry by a virtual agent (an anthropomorphic AI) on user’s perceived trustworthiness. We found that social mimicry (like mimicking the head movements) results in higher perceived trust on the agent, which is mediated through the increased liking for that agent. Read more …