Nikola Banovic

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My research focuses on methods, applications, and ethics of Computational Modeling in Human-Computer Interaction (HCI). Understanding and modeling human behavior supports innovative information technology that will change how we study and design interactive user experiences. I envision modeling the human accurately across domains as a theoretical foundation for work in HCI in which computational models provide a foundation to study, describe, and understand complex human behaviors and support optimization and evaluation of user interfaces. I create technology that automatically reasons about and acts in response to people’s behavior to help them be productive, healthy, and safe.

Akbar Waljee

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I use machine-learning techniques to implement decision support systems and tools that facilitate more personalized care for disease management and healthcare utilization to ultimately deliver efficient, effective, and equitable therapy for chronic diseases. To test and advance these general principles, I have built operational programs that are guiding—and improving—patient care in costly in low resource settings, including emerging countries.

Jim Omartian

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My research explores the interplay between corporate decisions and employee actions. I currently use anonymized mobile device data to observe individual behaviors, and employ both unsupervised and supervised machine learning techniques.

Jenny Radesky

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My research focuses on the intersection between mobile technology, parenting, parent-child interaction, and child development of processes such as executive functioning, self-regulation, and social-emotional well-being. Our projects use a combination of methods including surveys, videotaped parent-child interaction tasks, time diaries, and mobile device app logging to examine how parents and young children use mobile technologies throughout their day. We have developed novel content analysis approaches to understand the experience of young children while using commercially available mobile apps – including advertising content, educational quality, and data collection. We emphasize questions that are relevant to everyday parenting experiences, and also consider what design changes would help create an optimal default environment for children and families.

Lorraine Buis

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I conduct research on the use of consumer-facing technologies for chronic disease self management. My work predominantly centers on the use of mobile applications that collect and manage patient generated health data overt time.

Halil Bisgin

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My research is focused on a wide range of topics from computational social sciences to bioinformatics where I do pattern recognition, perform data analysis, and build prediction models. At the core of my effort, there lie machine learning methods by which I have been trying to address problems related to social networks, opinion mining, biomarker discovery, pharmacovigilance, drug repositioning, security analytics, genomics, food contamination, and concussion recovery. I’m particularly interested in and eager to collaborate on cyber security aspect of social media analytics that includes but not limited to misinformation, bots, and fake news. In addition, I’m still pursuing opportunities in bioinformatics, especially about next generation sequencing analysis that can be also leveraged for phenotype predictions by using machine learning methods.

A typical pipeline for developing and evaluating a prediction models to identify malicious Android mobile apps in the market

Jessica Golbus

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I am interested in using digital health technology for the treatment of cardiovascular disease with a particular emphasis on its application to patients with heart failure. More specific, my interests include (1) using non-invasive sensors and digital health technology to improve the delivery of cardiovascular care and (2) optimizing treatment for patients with advanced systolic heart failure through novel statistical tools and risk-modeling

Aditi Misra

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Transportation is the backbone of the urban mobility system and is one of the greatest sources of environmental emissions and pollutions. Making urban transportation efficient, equitable and sustainable is the main focus of my research. My students and I analyze small scale survey data as well as large scale spatiotemporal data to identify travel behavior trends and patterns at a disaggregate level using econometric methods, which we then scale up to the population level through predictive and statistical modeling. We also design our own data collection methods and instruments, be it a network of smart devices or stated preference experiments. Our expertise lies in identifying latent constructs that influence decisions and choices, which in turn dictate demands on the systems and subsystems. We use our expertise to design incentives and policy suggestions that can help promote sustainable and equitable multimodal transportation systems. Our team also uses data analytics, particularly classification and pattern recognition algorithms, to analyze crash context data and develop safety-critical scenarios for automated and connected vehicle (CAV) deployment. We have developed an online game based on such scenarios to promote safe shared mobility among teenagers and young adults and plan to expand research in that area. We are also currently expanding our research to explore the use of NN in context information synthesis.

This is a project where we used classification and Bayesian models to identify scenarios that are risky for pedestrians and bicyclists. We then developed an online game based on those scenarios for middle schoolers so that they are better prepared for shared road conflicts.

Sunghee Lee

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My research focuses on issues in data collection with hard-to-reach populations. In particular, she examines 1) nontraditional sampling approaches for minority or stigmatized populations and their statistical properties and 2) measurement error and comparability issues for racial, ethnic and linguistic minorities, which also have implications for cross-cultural research/survey methodology. Most recently, my research has been dedicated to respondent driven sampling that uses existing social networks to recruit participants in both face-to-face and Web data collection settings. I plan to expand my research scope in examining representation issues focusing on the racial/ethnic minority groups in the U.S. in the era of big data.