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Research Topics

We regard analytics as the translational arm of more foundational areas of data science. In a lab group like no other we know of, we merge multiple fields usually isolated from one another into an interdisciplinary mash-up of technology, psychology, methodology, and business.

These areas are generally in different departments that span multiple colleges or schools in a university environment. They often use different language, terms, and theoretical lenses to explore similar phenomena, which causes great confusion in many contexts. This interdisciplinary mash-up, though, is our playing field. The HAL lab sufficiently intersects these areas and has a way of looking at the world in a unique, and dare we say with a realistic, approach. We are “T-shaped” in structure: wide (horizontal) in foundational theories and deep (vertical) in methods. The HAL approach is one in which analytics have a purpose: problems are framed, critically considered, and evaluated with rigorous methods in an effort to understand the human condition. We find problems and then solutions; identify inputs and assess outputs; test stimuli on responses; identify causes and their effects; explain and predict.

  • Diagram illustrating interconnected networks of authors, channels, communities, and content with colorful lines and labeled circular sections.

    Public Health, Policy, & Social Good

    Research on public health, policy, and social good explores the impact of grand societal challenges

  • Diagram illustrating a complex neural network architecture with tasks, embeddings, and layers, including Char CNN, BiLSTM, and SEM architecture.

    Psychometric NLP & AI Governance

    Research on psychometric NLP, fairness, AI governance includes novel machine learning methods for text classification, user-centric language modeling, and fairness in NLP.

  • Illustration showing a cycle: person to mobile app, sensors, clinical data, big data analytics, and insights on perceptions, behavior, wellness.

    Behavior Modeling & Prediction

    Research on behavior modeling & prediction includes use of statistical methods to understand the human condition and machine learning techniques for predicting behavior.

  • Line graph showing two overlapping bell curves, one black and one red, representing 2nd-grade test scores in reading and math.

    Designing for Digital Experimentation

    Digital experiments and randomized control trials are crucial for understanding the potential effectiveness and implications of decisions and policies.

  • Line graphs showing TPR, FPR, and MSE across different scenarios with methods: aPELT(profile), aPELT(plugin), and PELT.

    Forecasting Adverse Events

    We are exploring statistical and machine learning methods for forecasting events with significant societal implications using time series data.