Shayan Mirjafari

I am a PhD student in computer science at Dartmouth College and a member of DartNets under the supervision of Andrew T. Campbell. I obtained my BSc in computer science from Sharif University of Technology.

My research is about mobile computing and sensing with particularly focusing on modeling and understanding human behaviors as observed through the sensing data.

Publications

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 et al.

UbiComp 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.

News: Washington Post, Dartmouth, Tech Crunch, Eureka Alert, Medium, New Atlas, Science Daily, Economic Times, India Times, cnBeta, Digiato

The Tesserae Project: Large-Scale, Longitudinal, In Situ, Multimodal Sensing of Information Workers

Stephen M. Mattingly, Julie M. Gregg, Pino Audia , Ayse Elvan Bayraktaroglu , Andrew T. Campbell , Nitesh V. Chawla et al.

CHI 2019

The Tesserae project investigates how a suite of sensors can measure workplace performance (e.g., organizational citizenship behavior), psychological traits (e.g., personality, affect), and physical characteristics (e.g., sleep, activity) over one year. We enrolled 757 information workers across the U.S. and measure heart rate, physical activity, sleep, social context, and other aspects through smartwatches, a phone agent, beacons, and social media. We report challenges that we faced with enrollment, privacy, and incentive structures while setting up such a long-term multimodal large-scale sensor study. We discuss the tradeoffs of remote versus in-person enrollment, and showed that directly paid, in-person enrolled participants are more compliant overall compared to remotely-enrolled participants. We find that providing detailed information regarding privacy concerns up-front is highly beneficial. We believe that our experiences can benefit other large sensor projects as this field grows.

Imputing Missing Social Media Data Streams in Multisensor Studies of Human Behavior

Koustuv Saha, Manikanta D Reddy, Vedant Das Swain, Julie Gregg, Ted Grover, Suwen Lin, Gonzalo J Martinez, Stephen M Mattingly, Shayan Mirjafari et al.

ACII 2019

The ubiquitous use of social media enables researchers to obtain self-recorded longitudinal data of individuals in real-time. Because this data can be collected in an inexpensive and unobtrusive way at scale, social media has been adopted as a "passive sensor" to study human behavior. However, such research is impacted by the lack of homogeneity in the use of social media, and the engineering challenges in obtaining such data. This paper proposes a statistical framework to leverage the potential of social media in sensing studies of human behavior, while navigating the challenges associated with its sparsity. Our framework is situated in a large-scale in-situ study concerning the passive assessment of psychological constructs of 757 information workers wherein of four sensing streams was deployed — bluetooth beacons, wearable, smartphone, and social media. Our framework includes principled feature transformation and machine learning models that predict latent social media features from the other passive sensors. We demonstrate the efficacy of this imputation framework via a high correlation of 0.78 between actual and imputed social media features. With the imputed features we test and validate predictions on psychological constructs like personality traits and affect. We find that adding the social media data streams, in their imputed form, improves the prediction of these measures. We discuss how our framework can be valuable in multimodal sensing studies that aim to gather comprehensive signals about an individual’s state or situation.

Sensing Behavioral Change over Time: Using Within-Person Variability Features from Mobile Sensing to Predict Personality Traits

Weichen Wang, Gabriella M. Harari, Rui Wang, Sandrine R. Müller, Shayan Mirjafari, Kizito Masaba and Andrew T. Campbell.

UbiComp 2018

Personality traits describe individual differences in patterns of thinking, feeling, and behaving ("between-person" variability). But individuals also show changes in their own patterns over time ("within-person" variability). Existing approaches to measuring within-person variability typically rely on self-report methods that do not account for fine-grained behavior change patterns (e.g., hour-by-hour). In this paper, we use passive sensing data from mobile phones to examine the extent to which within-person variability in behavioral patterns can predict self-reported personality traits. Data were collected from 646 college students who participated in a self-tracking assignment for 14 days. To measure variability in behavior, we focused on 5 sensed behaviors (ambient audio amplitude, exposure to human voice, physical activity, phone usage, and location data) and computed 4 within-person variability features (simple standard deviation, circadian rhythm, regularity index, and flexible regularity index). We identified a number of significant correlations between the within-person variability features and the self-reported personality traits. Finally, we designed a model to predict the personality traits from the within-person variability features. Our results show that we can predict personality traits with good accuracy. The resulting predictions correlate with self-reported personality traits in the range of r = 0.32, MAE = 0.45 (for Openness in iOS users) to r = 0.69, MAE = 0.55 (for Extraversion in Android users). Our results suggest that within-person variability features from smartphone data has potential for passive personality assessment.