Professional Experience

  • Present 2018

    Research Assistant

    Dartmouth College, NH, USA

  • 2018 2017

    Teaching Assistant

    Deerwalk Institute of Technology, Kathmandu, Nepal

  • 2018 2015

    Co-founder and CTO

    TechLekh Services Pvt. Ltd., Kathmandu, Nepal

  • 2015 2014

    Search Engine Optimization Analyst

    Deerwalk Services Pvt. Ltd., Kathmandu, Nepal


  • Ph.D. 2018 - Present

    Ph.D. in Computer Science

    Dartmouth College, USA

  • B.Sc.2017

    Bachelors in Computer Science & Information Technology

    Tribhuwan University, Nepal

  • GCE Advanced Level2014

    CIE A Level

    Cambridge University, UK

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Predicting Job Performance Using Mobile Sensing

Shayan Mirjafari, Hessam Bagherinezhad, Subigya Nepal, Gonzalo J. Martinez, Koustuv Saha, Mikio Obuchi, Pino G. Audia, Nitesh V. Chawla, Anind K. Dey, Aaron Striegel, Andrew T Campbell
Magazine Paper IEEE Pervasive Computing, Special issue on Future of Work, November 2021 | Impact Factor: 3.175


We hypothesize that behavioral patterns of people are reflected in how they interact with their mobile devices and that continuous sensor data passively collected from their phones and wearables can infer their job performance. Specifically, we study day-today job performance (improvement, no change, decline) of N=298 information workers using mobile sensing data and offer data-driven insights into what data patterns may lead to a high-performing day. Through analyzing workers' mobile sensing data, we predict their performance on a handful of job performance questionnaires with an F-1 score of 75%. In addition, through numerical analysis of the model, we get insights into how individuals must change their behavior so that the model predicts improvements in their job performance. For instance, one worker may benefit if they put their phone down and reduce their screen time, while another worker may benefit from getting more sleep.

On the Transition of Social Interaction from In-Person to Online: Predicting Changes in Social Media Usage of College Students during the COVID-19 Pandemic based on Pre-COVID-19 On-Campus Colocation

Weichen Wang, Jialing Wu, Subigya Kumar Nepal, Alex daSilva, Elin Hedlund, Eilis Murphy, Courtney Rogers, Jeremy F Huckins
Conference Paper Proceedings of the 2021 International Conference on Multimodal Interaction, October 2021


Pandemics significantly impact human daily life. People throughout the world adhere to safety protocols (e.g., social distancing and self-quarantining). As a result, they willingly keep distance from workplace, friends and even family. In such circumstances, in-person social interactions may be substituted with virtual ones via online channels, such as, Instagram and Snapchat. To get insights into this phenomenon, we study a group of undergraduate students before and after the start of COVID-19 pandemic. Specifically, we track N=102 undergraduate students on a small college campus prior to the pandemic using mobile sensing from phones and assign semantic labels to each location they visit on campus where they study, socialize and live. By leveraging their colocation network at these various semantically labeled places on campus, we find that colocations at certain places that possibly proxy higher in-person social interactions (e.g., dormitories, gyms and Greek houses) show significant predictive capability in identifying the individuals’ change in social media usage during the pandemic period. We show that we can predict student’s change in social media usage during COVID-19 with an F1 score of 0.73 purely from the in-person colocation data generated prior to the pandemic.

Assessing the Impact of Commuting on Workplace Performance Using Mobile Sensing

Subigya Nepal, Gonzalo J Martinez, Shayan Mirjafari, Stephen Mattingly, Vedant Das Swain, Aaron Striegel, Pino G Audia, Andrew T Campbell
Magazine Paper IEEE Pervasive Computing, Special issue on Future of Work, October 2021 | Impact Factor: 3.175


Commuting to and from work presents daily stressors for most workers. It is typically demanding in terms of time and cost, and can impact people’s mental health, job performance, and, broadly speaking, personal life. We use mobile phones and wearable sensing to capture location-related context, physiology, and behavioral patterns of N=275 information workers while they commute, mainly by driving, between home and work locations spread across the United States for a one-year period. We assess the impact of commuting on participant’s workplace performance, showing that we can predict self-reported workplace performance metrics based on passively collected mobile-sensing features captured during commute periods.

Current practices in mental health sensing

Subigya Nepal, Weichen Wang, Bishal Sharma, Prabesh Paudel
Magazine Paper XRDS: Crossroads, The ACM Magazine for Students, Volume 28, Issue 1, September 2021


The ubiquity of smartphones and wearables makes it an attractive option to passively study human behavior. We explore the current practices of using passive sensing devices to assess mental health and wellbeing, including the limitations and future directions.

Mental Health and Behavior of College Students During the COVID-19 Pandemic: Longitudinal Mobile Smartphone and Ecological Momentary Assessment Study, Part II

Dante L Mack, Alex W DaSilva, Courtney Rogers, Elin Hedlund, Eilis I Murphy, Vlado Vojdanovski, Jane Plomp, Weichen Wang, Subigya K Nepal, Paul E Holtzheimer, Dylan D Wagner, Nicholas C Jacobson, Meghan L Meyer, Andrew T Campbell, Jeremy F Huckins
Journal Paper Journal of Medical Internet Research, Volume 23, Issue 6, June 2021 | Impact Factor: 5.03


In the initial lockdown phase of the COVID-19 pandemic, people spent more time on their phones, were more sedentary, visited fewer locations, and exhibited increased symptoms of anxiety and depression. As the pandemic persisted through the spring, people continued to exhibit very similar changes in both mental health and behaviors. Although these large-scale shifts in mental health and behaviors are unsurprising, understanding them is critical in disrupting the negative consequences to mental health during the ongoing pandemic.

Detecting Job Promotion in Information Workers Using Mobile Sensing

Subigya Nepal, Shayan Mirjafari, Gonzalo J. Martinez, Pino Audia, Aaron Striegel, and Andrew T. Campbell
Conference Papers Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 4, Issue 3, September 2020


Most people desire promotions in the workplace. Typically, rising through the ranks comes with increased demands, better salary and higher status among peers. However, promoted workers have to deal with new challenges, such as, adjusting to new roles and responsibilities, which can in turn impact their physical and mental wellbeing. In this year long study, we use mobile sensing to track physiological and behavioral patterns of N=141 information workers who are promoted. We show that the workers experience a change in their physiological and behavioral patterns after promotion captured by passive sensing from phones, wearables and Bluetooth beacons. Furthermore, we use a random convolutions based approach to extract patterns from multivariate time series signals and evaluate the performance of different models to classify a worker’s mobile sensing data as belonging to a promoted or non-promoted period with an AUC of 0.72. As a result, we report for the first time that mobile sensing can detect job promotion events by modeling physiological and behavioral changes of information workers in an objective manner.

Differentiating Higher and Lower Job Performers in the Workplace Using Mobile Sensing

Shayan Mirjafari, Kizito Masaba, Ted Grover, Weichen Wang, Pino Audia, Andrew T Campbell, Nitesh V Chawla, Vedant Das Swain, Munmun De Choudhury, Anind K Dey, Sidney K D'Mello, Ge Gao, Julie M Gregg, Krithika Jagannath, Kaifeng Jiang, Suwen Lin, Qiang Liu, Gloria Mark, Gonzalo J Martinez, Stephen M Mattingly, Edward Moskal, Raghu Mulukutla, Subigya Nepal, Kari Nies, Manikanta D Reddy, Pablo Robles-Granda, Koustuv Saha, Anusha Sirigiri, Aaron Striegel
Conference Papers Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 2, June 2019


Assessing performance in the workplace typically relies on subjective evaluations, such as, peer ratings, supervisor ratings and self assessments, which are manual, burdensome and potentially biased. We use objective mobile sensing data from phones, wearables and beacons to study workplace performance and offer new insights into behavioral patterns that distinguish higher and lower performers when considering roles in companies (i.e., supervisors and non-supervisors) and different types of companies (i.e., high tech and consultancy). We present initial results from an ongoing year-long study of N=554 information workers collected over a period ranging from 2-8.5 months. We train a gradient boosting classifier that can classify workers as higher or lower performers with AUROC of 0.83. Our work opens the way to new forms of passive objective assessment and feedback to workers to potentially provide week by week or quarter by quarter guidance in the workplace.

A Multisensor Person-Centered Approach to Understand the Role of Daily Activities in Job Performance with Organizational Personas

Vedant Das Swain, Koustuv Saha, Hemang Rajvanshy, Anusha Sirigiri, Julie M Gregg, Suwen Lin, Gonzalo J Martinez, Stephen M Mattingly, Shayan Mirjafari, Raghu Mulukutla, Subigya Nepal, Kari Nies, Manikanta D Reddy, Pablo Robles-Granda, Andrew T Campbell, Nitesh V Chawla, Sidney D'Mello, Anind K Dey, Kaifeng Jiang, Qiang Liu, Gloria Mark, Edward Moskal, Aaron Striegel, Louis Tay, Gregory D Abowd, Munmun De Choudhury
Conference Papers Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Volume 3, Issue 4, December 2019


Several psychologists posit that performance is not only a function of personality but also of situational contexts, such as day-level activities. Yet in practice, since only personality assessments are used to infer job performance, they provide a limited perspective by ignoring activity. However, multi-modal sensing has the potential to characterize these daily activities. This paper illustrates how empirically measured activity data complements traditional effects of personality to explain a worker’s performance. We leverage sensors in commodity devices to quantify the activity context of 603 information workers. By applying classical clustering methods on this multisensor data, we take a person-centered approach to describe workers in terms of both personality and activity. We encapsulate both these facets into an analytical framework that we call organizational personas. On interpreting these organizational personas we find empirical evidence to support that, independent of a worker’s personality, their activity is associated with job performance. While the effects of personality are consistent with the literature, we find that the activity is equally effective in explaining organizational citizenship behavior and is less but significantly effective for task proficiency and deviant behaviors. Specifically, personas that exhibit a daily-activity pattern with fewer location visits, batched phone-use, shorter desk-sessions and longer sleep duration, tend to perform better on all three performance metrics. Organizational personas are a descriptive framework to identify the testable hypotheses that can disentangle the role of malleable aspects like activity in determining the performance of a worker population.

Mental Health and Behavior of College Students During the Early Phases of the COVID-19 Pandemic: Longitudinal Smartphone and Ecological Momentary Assessment Study

Jeremy F Huckins, Alex W DaSilva, Weichen Wang, Elin Hedlund, Courtney Rogers, Subigya K Nepal, Jialing Wu, Mikio Obuchi, Eilis I Murphy, Meghan L Meyer, Dylan D Wagner, Paul E Holtzheimer, Andrew T Campbell
Journal Paper Journal of Medical Internet Research, Volume 22, Issue 6, June 2020 | Impact Factor: 5.03


The vast majority of people worldwide have been impacted by coronavirus disease (COVID-19). In addition to the millions of individuals who have been infected with the disease, billions of individuals have been asked or required by local and national governments to change their behavioral patterns. Previous research on epidemics or traumatic events suggests that this can lead to profound behavioral and mental health changes; however, researchers are rarely able to track these changes with frequent, near-real-time sampling or compare their findings to previous years of data for the same individuals. By combining mobile phone sensing and self-reported mental health data among college students who have been participating in a longitudinal study for the past 2 years, we sought to answer two overarching questions. First, have the behaviors and mental health of the participants changed in response to the COVID-19 pandemic compared to previous time periods? Second, are these behavior and mental health changes associated with the relative news coverage of COVID-19 in the US media?

On the Quality of Real-world Wearable Data in a Longitudinal Study of Information Workers

Gonzalo J Martinez, Stephen M Mattingly, Shayan Mirjafari, Subigya K Nepal, Andrew T Campbell, Anind K Dey, Aaron D Striegel
Conference PapersIEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), August 2020


Over the past few years, an incredible diversity of consumer-grade wearables has emerged with tremendous breadth in capabilities, form factor, and cost. These wearable devices show significant promise for researchers to conduct expansive research studies in terms of scale, scope, and duration. Unfortunately, there is limited public data shared with respect to the data quality, device longevity, and scaling issues that emerge when trying to execute such studies. To that end, we share real-world results with respect to data quality, participant compliance, and device efficacy on a large scale, longitudinal study involving over seven hundred and fifty working professionals over the period of an entire year. In this paper, we present analyses with respect to the different types of data being collected including sleep, heart rate, physical activity, and stress. Furthermore, we explore participants behavior regarding charging frequency, and device robustness to further aid researchers considering large scale wearable studies.

Improved Sleep Detection Through the Fusion of Phone Agent and Wearable Data Streams

Gonzalo J Martinez, Stephen M Mattingly, Jessica Young, Louis Faust, Anind K Dey, Andrew T Campbell, Munmun De Choudhury, Shayan Mirjafari, Subigya K Nepal, Pablo Robles-Granda, Koustuv Saha, Aaron D Striegel
Conference PapersIEEE Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Aug 2020 | Best Paper Honorable Mention


Commercial grade activity trackers and phone agents are increasingly being deployed as sensors for sleep in large scale, longitudinal designs. In general, wearables detect sleep through diminished movement and decreased heart rate (HR), while phone agents look for lack of user input, movement, sound or light. However, recent literature suggests that commercial-grade wearables and phone apps vary greatly in the accuracy of sleep predictions. Constant innovation in wearables and proprietary algorithms further make it difficult to evaluate their efficacy for scientific study, especially outside of the laboratory. In a longitudinal study, we find that wearables cannot detect when a person is laying still but using their phones, a common behavior, overestimating sleep when compared to self-reports. Therefore, we propose that fusing wearables and phone sensors allows for more accurate sleep detection by capitalizing on the benefits of both streams: combining the movement detection of wearables with the technology usage detected by cell phones. We determine that fusing phone activity to wearables can generate better models of self-reported sleep than either stream alone, and test models in two separate datasets.

Social Sensing: Assessing Social Functioning of Patients Living with Schizophrenia using Mobile Phone Sensing

Weichen Wang, Shayan Mirjafari, Gabriella Harari, Dror Ben-Zeev, Rachel Brian, Tanzeem Choudhury, Marta Hauser, John Kane, Kizito Masaba, Subigya Nepal, Akane Sano, Emily Scherer, Vincent Tseng, Rui Wang, Hongyi Wen, Jialing Wu, Andrew Campbell
Conference PapersProceedings of the 2020 CHI Conference on Human Factors in Computing Systems, April 2020


Impaired social functioning is a symptom of mental illness (e.g., depression, schizophrenia) and a wide range of other conditions (e.g., cognitive decline in the elderly, dementia). Today, assessing social functioning relies on subjective evaluations and self assessments. We propose a different approach and collect detailed social functioning measures and objective mobile sensing data from N=55 outpatients living with schizophrenia to study new methods of passively accessing social functioning. We identify a number of behavioral patterns from sensing data, and discuss important correlations between social function sub-scales and mobile sensing features. We show we can accurately predict the social functioning of outpatients in our study including the following sub-scales: prosocial activities (MAE = 7.79, r = 0.53), which indicates engagement in common social activities; interpersonal behavior (MAE = 3.39, r = 0.57), which represents the number of friends and quality of communications; and employment/occupation (MAE = 2.17, r = 0.62), which relates to engagement in productive employment or a structured program of daily activity. Our work on automatically inferring social functioning opens the way to new forms of assessment and intervention across a number of areas including mental health and aging in place.

TA Experience