An ability to process, understand, and share data is critical to many of today’s computational tasks. Our group’s work takes a human centered view on data analytic problems. Some of our advances include a machine learning algorithm that tries to minimize the burden on people answering questions needed to support prediction and decreasing the impact of uncertainty by carrying it forward into interactive systems rather than guessing the correct interpretation without any knowledge of the user who is interacting with a system.
Feelings of loneliness are associated with poor physical and mental health. Detection of loneliness through passive sensing on personal devices can lead to the development of interventions aimed at decreasing rates of loneliness. Doryab, Afsaneh, et al. “Identifying Behavioral Phenotypes of Loneliness and Social Isolation with Passive Sensing: Statistical Analysis, Data Mining and Machine Learning … Continue reading Detecting Loneliness
Improving mobile keyboard typing speed increases in value as more tasks move to a mobile setting. Autocorrect is a powerful way to reduce the time it takes to manually fix typing errors, which results in typing speed increase. However, recent user studies of autocorrect uncovered an unexplored side-effect: participants’ aversion to typing errors despite autocorrect. … Continue reading The Limits of Expert Text Entry Speed on Mobile Keyboards with Autocorrect
The rate of depression in college students is rising, which is known to increase suicide risk, lower academic performance and double the likelihood of dropping out. Researchers have used passive mobile sensing technology to assess mental health. Existing work on finding relationships between mobile sensing and depression, as well as identifying depression via sensing features, … Continue reading Leveraging Routine Behavior and Contextually-Filtered Features for Depression Detection among College Students
Academic success and promotion are heavily influenced by publication record. In many fields, including computer science, multi-author papers are the norm. Evidence from other fields shows that norms for ordering author names can influence the assignment of credit. We interviewed 38 students and faculty in human- computer interaction (HCI) and machine learning (ML) at two … Continue reading Understanding gender equity in author order assignment
Improving the information economy for tenants pre-lease signing.
Quantifying Aversion to Costly Typing Errors in Expert Mobile Text Entry Text entry is an increasingly important activity for mobile device users. As a result, increasing text entry speed of expert typists is an important design goal for physical and soft keyboards. Mathematical models that predict text entry speed can help with keyboard design and … Continue reading Aversion to Typing Errors
Leveraging Human Routine Models to Detect and Generate Human Behaviors An ability to detect behaviors that negatively impact people’s wellbeing and show people how they can correct those behaviors could enable technology that improves people’s lives. Existing supervised machine learning approaches to detect and generate such behaviors require lengthy and expensive data labeling by domain … Continue reading Modeling & Generating Routines
Watch-ya-doin is an innovative experienced based sampling framework for longitudinal data collection and analysis. Our system consists of a smartwatch and an android device working unobtrusively to track data. Our goal is to train on and recognize a specific activity over time. We use a simple wrist-worn accelerometer to predict eating behavior and other activities. These are inexpensive … Continue reading Watch-ya-doin
Modeling and Understanding Human Routine Behavior Human routines are blueprints of behavior, which allow people to accomplish their purposeful repetitive tasks and activities. People express their routines through actions that they perform in the particular situations that triggered those actions. An ability to model routines and understand the situations in which they are likely to … Continue reading Modeling Human Routines
In recent years, surveys have been shifting online, offering the possibility for adaptive questions, where later questions depend on responses to earlier questions. We present a general framework for dynamically ordering questions, based on previous responses, to engage respondents, improving survey completion and imputation of unknown items. Our work considers two scenarios for data collection … Continue reading Dynamic question ordering
Improving healthcare decision making with better workflow and information flow.
The goal of the Stepgreen project is to leverage Internet scale technologies to create opportunities for reduced energy consumption. The original vision of the project was to leverage existing online social networks to encourage individual change. Since then the project has broadened to include a number of other ideas. We have explored the impact of … Continue reading StepGreen
Johnson, L., Wilcox, S., Mankoff, J., & Stricker, R. B. (2014). Severity of chronic Lyme disease compared to other chronic conditions: a quality of life survey. PeerJ, 2, e322. The Centers for Disease Control and Prevention (CDC) health-related quality of life (HRQoL) indicators are widely used in the general population to determine the burden of … Continue reading Severity of Chronic Lyme Disease
Carter, Scott, Jennifer Mankoff, Scott R. Klemmer, and Tara Matthews. “Exiting the cleanroom: On ecological validity and ubiquitous computing.” Human–Computer Interaction 23, no. 1 (2008): 47-99. Over the past decade and a half, corporations and academies have invested considerable time and money in the realization of ubiquitous computing. Yet design approaches that yield ecologically valid understandings of … Continue reading Exiting the cleanroom: On ecological validity and ubiquitous computing