UVA School of Data Science Logo UVA School of Data Science UVA School of Data Science Logo UVA School of Data Science
Type a word to search or ESC to close

Filter by

Research Labs

Research Labs at the University of Virginia's School of Data Science are dedicated to advancing the field of data science through interdisciplinary research and collaboration. Our labs focus on various aspects of data science, including machine learning, data visualization, and data ethics. We aim to foster innovation and knowledge sharing within the data science community and beyond. Explore our labs and their research initiatives to learn more about the exciting work being done at the intersection of data science and other fields.

Search Results

  • Principal Investigator Daniel Graham

    The 0xC0FFEE Lab explores the frontiers of automated penetration testing and AI-driven cyber defense. Our research blends offensive security with machine learning to uncover vulnerabilities at scale and develop next-generation defensive systems.

    In addition to our research, we provide free penetration testing and secure code review services to startups and non-profits, helping strengthen the broader tech ecosystem. We are also recruiting motivated undergraduate researchers who want hands-on experience in ethical hacking, exploit development, and AI security applications.

    Research Tags: Automated Pentesting; Sensors and Embedded Systems; Artificial Intelligence and Cybersecurity. 

    Research Interests:
    Artificial Intelligence and Machine Learning; Data Ethics and Society
  • Principal Investigator Tyler Cody

    The Artificial General Intelligence (AGI) Lab (AGIL) is focused on data science for an era of real-world AGI, marked by machine learning transitioning from a paradigm where models are produced from data to a paradigm where models are produced from other models—an exciting and pressing reality reshaping the practice of data science. How can AGI be characterized with data? How does AGI shape the use of data? The lab grapples with these questions using formal theory, technical methods, and empirical experiments in diverse application domains, including space, cyber, robotics, and other engineered systems.

    Research Tags: Generative AI; Reinforcement Learning; Graph Learning; Systems Theory; Learning Theory; Cybernetics; Foundation Models; AI Pipelines; Adversarial AI

    Research Interests:
    Artificial Intelligence and Machine Learning; Computational Sciences
  • Principal Investigator Chirag Agarwal

    The Aikyam Lab (Sanskrit: ऐक्यम्; meaning "oneness") focuses on developing Trustworthy Machine Learning Frameworks that go beyond training models for specific downstream tasks and satisfy trustworthy properties, such as explainability, fairness, and robustness. In our work, we develop new explainability algorithms to study the behavior of complex black-box unimodal and multimodal models. The research on multilingual and multimodal explanation methods is at a very nascent stage, and we aim to introduce novel algorithms that generate actionable explanations for multimodal models. Through these efforts, our lab aims to shape the future of explainable AI (XAI), ensuring AI systems are both powerful and interpretable, with far-reaching implications for ethical AI deployment.

    Research Tags: Artificial Intelligence; Trustworthy AI; Multimodal XAI; AI Safety; AI Audits and Transparency

    Research Interests:
    Artificial Intelligence and Machine Learning; Data Ethics and Society
  • Principal Investigator Stephen Baek

    The Visual Intelligence Laboratory @ UVA (the “Baek Lab”) is a research group directed by Professor Stephen Baek at the University of Virginia School of Data Science. The lab tackles both fundamental and applied research problems in geometric data analysis to understand the roles of geometry in various scientific phenomena. Applications include mechanical and aerospace engineering, materials science, applied physics, medical image analysis, and sports biomechanics.

    Research Tags: Geometric Data Analysis; Physics-aware Deep Learning; Data-driven Mechanics; Sports and Human Movement Analysis; Computer Vision and Medical Imaging

    Research Interests:
    Biomedical, Health, and Life Sciences; Engineering, Robotics, and Physical Sciences ; Sports Science and Analytics
  • Principal Investigator Philip E. Bourne

    Our lab, co-directed by Phil Bourne and Cam Mura, works at the junctions of Structural Bioinformatics, Data Science, and Systems Biology/Pharmacology, with the ultimate goals of (i) improving drug discovery and safety pipelines, (ii) deepening our understanding of biomolecular sequence/structure/function relationships, often from an evolutionary perspective, and (iii) broadly contributing open data and methods to further our collective understanding of living systems. Along the way, we work to improve access to knowledge through open scholarly communication.

    Research Interests:
    Biomedical, Health, and Life Sciences
  • Principal Investigator Alex Gates

    The Connected Data Hub conducts research on how interconnected systems both arise from and influence human behavior across the domains of science, art, industry, and national security. We specialize in Network Science, employing it as a foundational framework for understanding complex systems — analyzing how individuals, teams, and organizations interact to generate knowledge and drive innovation. Our work is highly interdisciplinary, integrating methods from Data Science with theoretical perspectives from Sociology, Physics, Computer Science, Computational Social Science, and Complex Systems. 

    Our current research is centered on the Science of Science, where we develop models to examine how collaboration networks, institutional structures, and strategic decisions shape creativity, impact, and long-term success. Increasingly, this work informs data-driven decision-making in high-stakes, dynamic environments — including those relevant to national security, research strategy, and organizational change.

    Research Tags: Network Science; Computational Social Science; Science of Science; Complex Systems; National Security; Data-driven Decision-making; Casual Inference
     

    Research Interests:
    Artificial Intelligence and Machine Learning; Business, Markets, and Policy; Computational Sciences; Human-Centered Design and Interaction; Theory, Foundations, and Advanced Methodologies
  • Principal Investigator Peter Alonzi

    The Criminal Justice Data Training Initiative (CJDTI) is a program that creates unique undergraduate research opportunities and a meaningful impact on the local community. Through hands-on experience with criminal justice data, students develop both technical skills and a contextual understanding of how data science can improve existing issues within the criminal justice system. The initiative works in collaboration with local agencies, including the Charlottesville Police Department and the Albemarle-Charlottesville Regional Jail, to analyze data and support the Evidence Based Decision Making team (EBDM).

    Research Interests:
    Data Ethics and Society
  • Principal Investigator Brian Wright

    The Data Science Active Learning Lab is designed around moving the field of Education and Data Science forward. This includes the development of AI-based tools, creating novel assessments to teach data science methods, advocating for best practices for data science education, and leveraging educational data to make informed decisions. All of these components are centered on ensuring our new field leverages best practices for the betterment of our students and the community at large. 

    Research Tags: Education; Large Language Models (LLMs); Learning Analytics; Applied Research 

    Research Interests:
    Artificial Intelligence and Machine Learning; Data Ethics and Society
  • Principal Investigator Mar Hicks

    The Digital History Lab, led by historian Mar Hicks, conducts research on the history of digital technologies. Lab researchers dig into the history of not only the technologies themselves but also the people, labor forces, places, and infrastructures that make these technologies impactful. Using these insights into the past, lab researchers help write accessible histories of technology that can aid students, faculty, and the general public in making sense of our current and future technological options. Positions in this lab are open to both undergraduate and graduate student researchers currently at UVA.

    Research Tags: History of Technology; AI and Society; Technology and Society; History of Computing; Public History; Labor Rights; Gender and Sexuality in Technology

    Research Interests:
    Data Ethics and Society
  • Principal Investigator Aidong Zhang

    Dr. Zhang's Lab conducts research on Machine Learning, Data Mining, Bioinformatics, and Health Informatics.

    Research Interests:
    Artificial Intelligence and Machine Learning; Biomedical, Health, and Life Sciences
  • Principal Investigator Yue Cheng

    We research Data Systems for Data Science (Ds²). Our aim is to explore and build better systems software to sustain the requirements of next-generation data-intensive applications. We are interested in addressing challenges existing in real-world distributed and storage systems. Our research is driven by the complexities of modern data-intensive computer systems, and the need for more efficient and flexible approaches to manage such complexities. We aim for innovation in systems technologies that range from large-scale distributed systems to cross-layer software-hardware co-design.

    Research Interests:
    Artificial Intelligence and Machine Learning; Computational Sciences
  • Principal Investigator Heman Shakeri

    At DYNAMO Lab, we develop intelligent algorithms to control and understand complex dynamical systems in biomedical applications. Our interdisciplinary approach bridges machine learning, control theory, and data science to tackle challenges ranging from cellular dynamics to human health. While our primary focus is on biomedical applications, we also explore intelligent control and data analysis techniques in areas like traffic optimization and change-point detection in temporal multispectral images.

    Research Interests:
    Artificial Intelligence and Machine Learning; Biomedical, Health, and Life Sciences
  • Principal Investigator Michael D. Porter

    The Event Analytics and Statistical Learning Lab develops statistical and machine learning methods to predict, detect, and link events in complex systems. Our research spans event forecasting with point processes and Hawkes models, anomaly detection in networks and spatial data, and data linkage across disparate sources. Applications range from crime, terrorism, and traffic safety to organ transplantation, disease surveillance, and social dynamics. The lab advances the statistical foundations of data science while delivering insights with direct impact on public safety, security, and health.

    Research Tags: Event Forecasting; Statistical Learning; Machine Learning and Data Mining; Anomaly Detection; Crime Linkage; Transplant Analytics

    Research Interests:
    Artificial Intelligence and Machine Learning; Biomedical, Health, and Life Sciences; Business, Markets, and Policy; Computational Sciences; Data Ethics and Society; Engineering, Robotics, and Physical Sciences ; Environment and Climate Science; Theory, Foundations, and Advanced Methodologies
  • Principal Investigator Natalie Kupperman

    The HOOS (Health, Outcomes & Optimization in Sport) Innovation Lab advances human motion science, sports performance, and translational data research at the University of Virginia. Bridging biomechanics, sports medicine, physiology, and data science, the lab integrates on-field and in-lab data to develop actionable insights for athletes, coaches, clinicians, and researchers. HOOS Innovation Lab serves as a collaborative hub linking UVA Athletics and the School of Data Science to drive progress in technology, motion analytics, and responsible data use. From injury recovery and performance optimization to data privacy and front office strategy, the lab translates research into real-world impact for active human health and sport.

    Research tags: Sports, Biomechanics, Athlete Health, Human motion, Privacy, Databases, Technology, Human performance, Injury risk

    Research Interests:
    Artificial Intelligence and Machine Learning; Biomedical, Health, and Life Sciences; Sports Science and Analytics
  • Principal Investigator Nur Yildirim

    The Human + AI Design Lab investigates how AI technologies can augment people in their work and daily lives. We translate AI technologies into real-world products and services that benefit people and society at large. We are an interdisciplinary team with expertise across interaction design, data science, cognitive science, psychology, and policy with work spanning various application domains, including knowledge work and healthcare.

    Research tags: Human-Centered AI, Human-Centered Design, Human-AI Interaction, Participatory Design

    Research Interests:
    Artificial Intelligence and Machine Learning; Data Ethics and Society; Human-Centered Design and Interaction
  • Principal Investigator Antonios Mamalakis

    We are a group of climate scientists, hydrologists, environmental engineers, and data scientists who work on better understanding hydroclimate and its variability across scales, better quantifying, and attributing predictability of the Earth system, and exploring adaptation strategies in the face of climate change. In doing so, we use, develop, and assess state-of-the-art data science techniques, such deep-learning models and explainable artificial intelligence (AI).

    Research Interests:
    Artificial Intelligence and Machine Learning; Environment and Climate Science
  • Principal Investigator Teague Henry

    The netLab research group brings together researchers across psychology and data science to develop and evaluate methods for studying psychopathology and other dynamical psychological processes. 

    Research Tags: Network Science; Dynamical Systems; Computational Psychiatry; Psychopathology; State-space Models; Control Theory

    Research Interests:
    Biomedical, Health, and Life Sciences
  • Principal Investigator Aiying Zhang

    The NeuroBioInformatics Lab (NBIL) focuses on developing modern statistical and machine-learning approaches leveraging multi-modal imaging and (epi)genetics to understand typical and atypical brain development.

    Research Tags: Network Analysis and Graphical Models; Multimodal Fusion; Causal Inference; Longitudinal Analysis
     

    Research Interests:
    Artificial Intelligence and Machine Learning; Biomedical, Health, and Life Sciences
  • Principal Investigator Jeffrey Blume

    The Prediction, Inference, and Scale in Data Science Models (PRISM) Lab explores the theoretical foundations of prediction and inference in data science, with a focus on how model scale—large or small—shapes statistical properties and decision-making. We study why models optimized for prediction often diverge from those suited for inference, and examine this behavior in both overparameterized and underparameterized settings. We bridge theory and practice by investigating when high-variance prediction machines outperform more stable but less accurate alternatives, and how information is leveraged when overparameterized models rely on (implicit) regularization.

    Research Tags: Prediction; Inference; Large-scale models; Biomedical science; Public Health Science; Diagnostic Imaging; Diagnostic Trials; Statistical Models; Biostatistics

    Research Interests:
    Biomedical, Health, and Life Sciences
  • Principal Investigator Tom Hartvigsen

    The Responsible AI in Varying Environments (RAVE) research group works on machine learning and natural language processing. We work to enable responsible model deployment in ever-changing environments, especially for health care.

    Research Tags: Trustworthy AI; Health Care and Biomedical; Natural Language Processing; Time Series and Multi-modality

    Research Interests:
    Artificial Intelligence and Machine Learning; Biomedical, Health, and Life Sciences; Data Ethics and Society
  • Principal Investigator Sheng Li

    The Reasoning and Knowledge Discovery (RISE) Lab focuses on developing effective and trustworthy artificial intelligence solutions that advance data-centric knowledge discovery and causal reasoning. Our research spans generative AI, trustworthy machine learning, large foundation models, multimodal learning, visual understanding, and causal inference, with interdisciplinary applications in science, health, and education.

    Research Tags: Trustworthy AI; Large Language Models; Machine Learning and Data Mining; Computer Vision; Causal Inference; AI for Education

    Research Interests:
    Artificial Intelligence and Machine Learning; Biomedical, Health, and Life Sciences; Data Ethics and Society
  • Principal Investigator Karsten Siller

    At the Scalable Scientific Imaging Lab (S²IL), we engage in interdisciplinary collaborations to develop extensible workflows for large-scale image analysis. Our goal is to transform raw imaging data into quantifiable scientific insights, with a particular focus on biomedical research spanning single-cell division, organ development, and translational cancer studies. To this end, we build resilient computational pipelines that scale seamlessly from laptops to high-performance clusters and cloud platforms. Paired with intuitive, user-friendly interfaces for interactive data exploration and visualization, these tools are aimed to advance open and reproducible science across disciplines.

    Research Tags: Image & Data Analytics, Data Systems, High-Performance Computing, Data Design & Visualization, Biomedical Research

    Research Interests:
    Biomedical, Health, and Life Sciences; Human-Centered Design and Interaction
  • Principal Investigator Mona Sloane

    The Sloane Lab is an interdisciplinary research group led by sociologist Mona Sloane. It conducts empirical research on the implications of technology for the organization of social life. The focus lies on artificial intelligence (AI) as a social phenomenon that intersects with wider cultural, economic, material, and political conditions. The lab spearheads social science leadership in applied work on responsible AI, public scholarship, and technology policy.

    Research Tags: AI; Social Research; Social Impact; Professions; Responsible AI; Data Policy

    Research Interests:
    Artificial Intelligence and Machine Learning; Data Ethics and Society
  • Principal Investigator Paul Perrin

    Perrin’s fundamental belief is that academic and medical communities have a central role to play in the alleviation of disparities in the context of disability and health. A combination of data science, modern analytic techniques, and community-based participatory research approaches are key tools for identifying the sources of—and potential solutions to—these disparities. With this aim, his lab’s research area of “social justice in disability and health” encompasses three facets: (a) cultural, familial, and international approaches to disability rehabilitation and adjustment, particularly in medically underserved and minority populations with neurological conditions; (b) social determinants of health (e.g., stigma, access to integrated care and telehealth, personal and collective strengths); and (c) social justice approaches to understand and dismantle oppression.

    Research Tags: Trustworthy AI; Health; Data Ethics and Society; Artificial Intelligence and Machine Learning 

    Research Interests:
    Artificial Intelligence and Machine Learning; Data Ethics and Society
  • Principal Investigator Lei Li

    The Spatial AI Lab (SAIL), led by Lei Li, focuses on empowering machines with spatial intelligence to perceive, understand, and interact with 3D worlds, both virtual and real, in a way that mirrors human spatial reasoning. Our research is at the intersection of computer vision, computer graphics, and artificial intelligence. We work on data-efficient learning techniques combined with geometric principles to reason about 3D worlds and the relations within them, advancing areas including scene reconstruction, human-scene interaction, human movement analysis, 3D shape analysis, geometric transfer learning, and 3D generative models.

    Research Tags: Spatial Intelligence; Generative AI; Foundation Models; Human-Centered Computing; Computer Vision; Computer Graphics; Geometric Deep Learning

    Research Interests:
    Artificial Intelligence and Machine Learning; Computational Sciences; Human-Centered Design and Interaction; Sports Science and Analytics
  • Principal Investigator Nicholas Landry

    The Landry Lab at the University of Virginia studies how the structure of networks and groups affect the spread of diseases, information, and ideology. We seek to answer questions like, How do groups change the way that contagion spreads? How do groups form and fracture? How can we use knowledge of the underlying structure of groups to effectively mitigate and optimize the spread of contagion? We use tools from network science, mathematical modeling, Bayesian inference, and open-source development to help answer these questions.

    Research tags: Network Science; Contagion Science; Data-Informed Modeling; Complex Systems; Open Software

    Research Interests:
    Biomedical, Health, and Life Sciences; Human-Centered Design and Interaction
  • Principal Investigator Hannah Bako

    The Visualization and Design Augmentation Research Group (ViDAR) at UVA’s School of Data Science is a creative and collaborative space where we study how people imagine, design, and build data visualizations—and how intelligent systems can help along the way. Our work blends human-centered design, empirical research, and innovative tool development to both understand the creative process and design new ways to support it. We explore themes such as example-aided ideation (how designers draw inspiration from prior work), new metrics for evaluating visualization properties, intelligent co-design interfaces that adapt to designers’ goals, and expressive tools for visualization programming.

    Research Tags: Design; Data Visualization; Human-Computer Interaction; AI Co-Design Tools; Visualization Programming 

    Research Interests:
    Artificial Intelligence and Machine Learning; Human-Centered Design and Interaction
  • Principal Investigator Jianxin Xie

    Xie’s Lab focuses on the development of innovative machine learning and AI methodologies with broad applications in healthcare and beyond. Our research spans multiple systems, including cardiac conduction system modeling, ECG processing, and cardiac imaging analysis; diabetes research through advanced analysis of blood glucose data to derive actionable health insights; and cancer research investigating mechanisms of skin cancer progression and metastasis. Beyond healthcare, we also extend our approaches to material engineering problems, applying data-driven and physics-informed methods to understand and predict complex material behaviors. At its core, the lab is committed to creating robust, generalizable AI frameworks that integrate data, physics, and domain expertise to advance science and improve human health

    Research Interests:
    Artificial Intelligence and Machine Learning; Biomedical, Health, and Life Sciences; Engineering, Robotics, and Physical Sciences
  • Principal Investigator YY Ahn

    At the Y Lab, we study the hidden architectures of complex systems through network science and artificial intelligence. From understanding how neural networks encode information to mapping how beliefs spread through populations, from tracing scientific breakthroughs to assessing AI’s impact on work and society, we tackle fundamental questions across biological, social, and technological domains. We connect different disciplines and methods with curiosity and positivity.

    Research Tags: Network Science; AI and Machine Learning; Representation Learning; Natural Language Processing; Computational Social Science; Science of Science; Complex Systems; Contagion; Belief Dynamics; AI and Labor Market; Computational Neuroscience and Biology; Cultural Analytics

    Research Interests:
    Artificial Intelligence and Machine Learning; Biomedical, Health, and Life Sciences; Business, Markets, and Policy; Computational Sciences; Theory, Foundations, and Advanced Methodologies