Congyu Wu

Congyu Wu
Binghamton University | SUNY Binghamton · Department of Systems Science and Industrial Engineering

PhD in Systems Engineering

About

24
Publications
9,209
Reads
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135
Citations
Introduction
Sensing and making sense of human behavior using ubiquitous technology and machine learning is my overarching interest. My past, ongoing, and anticipated work belongs in one or more of the following categories: (1) personalized mobile health; (2) socially-aware artificial intelligence as healthcare information technology; (3) computational social science.
Education
August 2012 - May 2019
University of Virginia
Field of study
  • Systems Engineering

Publications

Publications (24)
Article
Full-text available
Activists have used social media during modern civil uprisings, and researchers have found that the generated content is predictive of off-line protest activity. However, questions remain regarding the drivers of this predictive power. In this paper, we begin by deriving predictor variables for individuals' protest decisions from the literature on...
Conference Paper
Full-text available
Continuous passive sensing using smartphone embedded sensors can drain the battery quickly, interrupting other usages of the device. In order to improve the energy efficiency in continuous mobile sensing applications, we propose a new adaptive sensing framework using reinforcement learning to optimize the sensing timing. We model our adaptive sensi...
Conference Paper
Full-text available
Over 35% of the world's population uses social media. Platforms like Facebook, Twitter, and Instagram have radically influenced the way individuals interact and communicate. These platforms facilitate both public and private communication with strangers and friends alike, providing rich insight into an individual's personality, health, and wellbein...
Preprint
Full-text available
Loneliness is a widely affecting mental health symptom and can be mediated by and co-vary with patterns of social exposure. Using momentary survey and smartphone sensing data collected from 129 Android-using college student participants over three weeks, we (1) investigate and uncover the relations between momentary loneliness experience and compan...
Preprint
Full-text available
In this paper we explore previously unidentified connections between relational event model (REM) from the field of network science and inverse reinforcement learning (IRL) from the field of machine learning with respect to their ability to characterize sequences of directed social interaction events in group settings. REM is a conventional approac...
Preprint
Full-text available
In this article we propose and validate an unsupervised probabilistic model, Gaussian Latent Dirichlet Allocation (GLDA), for the problem of discrete state discovery from repeated, multivariate psychophysiological samples collected from multiple, inherently distinct, individuals. Psychology and medical research heavily involves measuring potentiall...
Article
The Indoor Air Quality (Indoor Air Quality (IAQ)) of the bedroom environment has recently garnered attention since air pollution can affect sleep. Previous studies investigated IAQ and sleep quality in controlled environments which impacts both self-reported and measured sleep quality. Studies within a participant’s home environment are ecologicall...
Article
Full-text available
Objective To identify the differences between circadian rhythm (CR) metrics characterized by different mobile sensors and computational methods. Methods We used smartphone tracking and daily survey data from 225 college student participants, applied four methods (survey construct automation, cosinor regression, non-parametric method, Fourier analy...
Article
Full-text available
With the outbreak of the COVID-19 pandemic in 2020, most colleges and universities move to restrict campus activities, reduce indoor gatherings and move instruction online. These changes required that students adapt and alter their daily routines accordingly. To investigate patterns associated with these behavioral changes, we collected smartphone...
Preprint
Full-text available
UNSTRUCTURED With the outbreak of the COVID-19 pandemic in 2020, most colleges and universities move to restrict campus activities, reduce indoor gatherings and move instruction online. These changes required that students adapt and alter their daily routines accordingly. To investigate patterns associated with these behavioral changes, we collecte...
Preprint
Full-text available
Circadian rhythm is the natural biological cycle manifested in human daily routines. A regular and stable rhythm is found to be correlated with good physical and mental health. With the wide adoption of mobile and wearable technology, many types of sensor data, such as GPS and actigraphy, provide evidence for researchers to objectively quantify the...
Article
Full-text available
Background As mobile technologies become ever more sensor-rich, portable, and ubiquitous, data captured by smart devices are lending rich insights into users’ daily lives with unprecedented comprehensiveness and ecological validity. A number of human-subject studies have been conducted to examine the use of mobile sensing to uncover individual beha...
Article
Full-text available
In this article, we explore previously unidentified connections between relational event model (REM) from the field of network science and inverse reinforcement learning (IRL) from the field of machine learning with respect to their ability to characterize sequences of directed social interaction events in group settings. REM is a conventional appr...
Article
Full-text available
Loneliness is a widely affecting mental health symptom and can be mediated by and co-vary with patterns of social exposure. Using momentary survey and smartphone sensing data collected from 129 Android-using college student participants over three weeks, we (1) investigate and uncover the relations between momentary loneliness experience and compan...
Preprint
As mobile technologies become ever more sensor-rich, portable, and ubiquitous, data captured by smart devices are lending rich insights into users' daily lives with unprecedented comprehensiveness, unobtrusiveness, and ecological validity. A number of human-subject studies have been conducted in the past decade to examine the use of mobile sensing...
Conference Paper
Full-text available
Sensor-based activity recognition seeks to provide higher-level knowledge about human activities from multiple sensors such as accelerometer and gyroscope. Thanks to growing ubiquity of sensor-rich smartphones and wearable devices, activity recognition research has made tremendous progress in recent years. Many sensors, such as motion sensors and c...
Conference Paper
Full-text available
As social media platforms have grown to form the foundation of modern digital communication, digital text message datasets that document interpersonal exchanges on these platforms have proliferated. These exchanges comprise a rich corpus of social context data, which can provide insight into how mental health challenges manifest in social contexts....
Article
Full-text available
Medical research has found strong connections between cognitive stress and various physical and mental health conditions. This paper addresses the need for more timely, less obtrusive measurements of cognitive stress. Given recent advances in and uptake of smartphone technology, we hypothesize significant correlations between a subject's smartphone...
Conference Paper
Full-text available
Momentary experiences of positive and negative emotionality—also referred to as state affect—are core components of well-being and performance. The ability to unobtrusively monitor state affect could raise individuals’ awareness of their mental health status and enable healthcare providers to deliver targeted, just-in-time mental health interventio...
Conference Paper
Full-text available
Social interactions have multifaceted effects on individu-als' mental health statuses, including mood and stress. As a proxy for the social environment, Bluetooth encounters detected by personal mobile devices have been used to improve mental health prediction and have shown preliminary success. In this paper, we propose a vector space model repres...

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