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research labs.
Find research groups that list data science, analytics, or data mining. The directory helps you connect these broad labels to particular research questions, supervisors, and published work.
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Look beyond tools to the question being answered. Compare data sources, statistical assumptions, reproducibility practices, and the domain knowledge needed to contribute to a project.
Profiles appear here because their listed research keywords match this topic. Inclusion does not establish an open position. Check the official sources and any published application instructions before reaching out.
Prepare a research introduction ↗14 research-led lab profiles
Browse labs by their listed college affiliation
Independently curated · UnclaimedAlice E. Smith Research Group
The University of Alabama
Alice E. Smith · Endowed Shelby Distinguished Professor
Alice E. Smith's faculty-led research profile covers Advanced Manufacturing, Artificial Intelligence, Autonomous Vehicles, and Data Analytics.
Bing Liu Research Group
University of Illinois Chicago
Bing Liu · Distinguished Professor
How can AI systems continue learning after deployment as the world and their conversations change? The group investigates lifelong and continual learning, autonomous AI and open-world learning. Research also spans natural-language processing, chatbots and data mining, with a longstanding focus on extracting opinions and sentiments from text. Documented work includes detecting deceptive opinions, learning from positive and unlabeled examples, and extracting information from the web. The current program examines self-evolving agents and continually learning dialogue systems, connecting language understanding with adaptation over time.
Christan Grant Research Group
University of Florida
Christan Grant · Associate Professor
How can interactive methods improve data acquisition and labeling quality for downstream models? The group develops tools and workflows for data acquisition and labeling across the data pipeline to reduce manual effort and error. Researchers build and evaluate interactive machine learning interfaces to support human-in-the-loop model training and refinement. The team applies visualization techniques to inspect and debug datasets and model outputs to support iterative dataset curation. The group investigates database and data management mechanisms to scale data-intensive experiments and reproducible analyses for data science workloads.
Independently curated · UnclaimedCristian Román-Palacios Research Group
University of ArizonaCristian Román-Palacios · Assistant Professor
How do lineage relationships explain patterns of species richness across regions and time? The group constructs and analyzes large phylogenetic trees to test drivers of biodiversity using species-level datasets and comparative methods. Researchers develop statistical and machine-learning workflows to infer paleoclimatic variables from proxy datasets and to reconstruct past climates for testing species’ responses. The lab applies data mining and biostatistical pipelines to quantify how historical and recent climate change affect species survival probabilities across taxa. The team implements open-source computational tools and reproducible workflows to run large-scale biodiversity and paleoclimate analyses efficiently.
Elias Bareinboim Research Group
Columbia University
Elias Bareinboim · Associate Professor of Computer Science
How can causal inference methods enable reliable decision-making from biased observational data in real-world systems? The group conducts research on causal inference and probabilistic modeling to build formal methods for reasoning about interventions and counterfactuals. Researchers develop algorithms in sequential decision making that combine probabilistic models with decision-theoretic tools to support robust policy selection. Projects apply causal and probabilistic techniques to domains such as computational biology and computer vision to translate formal causal models into domain-specific analyses. The Causal Artificial Intelligence Lab integrates these methods to study both foundational theory and practical applications in language, vision, and robotics.
Independently curated · UnclaimedJiaqi (Jackey) Gong Research Group
The University of AlabamaJiaqi (Jackey) Gong · Associate Professor, Adjunct Associate Professor of ME, Director for the Alabama Center for the Advancement of Artificial Intelligence
Jiaqi (Jackey) Gong's faculty-led research profile covers Artificial Intelligence, Computer Vision, Data Analytics, and Deep Learning.
Jingyi Jessica Li Research Group
University of California, Los Angeles
Jingyi Jessica Li · Professor of Statistics and Data Science
How can statistical models reveal which molecular processes drive cell-to-cell transcriptome variation across populations and within single cells? The research group develops interpretable statistical methods for biomedical data, building tools that quantify regulatory mechanisms across transcriptomes. The group extracts hidden information from transcriptomics data by designing algorithms and simulators to evaluate data-generation assumptions and benchmarking analysis workflows. Researchers construct synthetic negative controls and other statistical controls to ensure rigor and control false discoveries in high-throughput analyses. The group implements and releases software (for example, scDesign3) to simulate realistic single-cell and spatial omics data for method evaluation and experimental design.
Naren Ramakrishnan Research Group
Virginia Polytechnic Institute and State University
Naren Ramakrishnan · University Distinguished Professor
How can patterns in complex data help researchers anticipate events and make better decisions? The group works on applied machine learning, data science and urban analytics. Research interests include recommender systems, computational epidemiology and visual analytics. Documented projects examine optimization of school boundaries and tools that support interactive redistricting, while related work investigates information extraction from scholarly literature. Participation in pandemic research connects data science with models of disease spread, bringing computational methods to problems that involve both technical predictions and real-world consequences.
Independently curated · UnclaimedNoorbakhsh Amiri Golilarz Research Group
The University of AlabamaNoorbakhsh Amiri Golilarz · Assistant Professor
Noorbakhsh Amiri Golilarz's faculty-led research profile covers Artificial Intelligence, Computer Vision, Data Analytics, and Deep Learning.
Independently curated · UnclaimedPurushotham Bangalore Research Group
The University of AlabamaPurushotham Bangalore · James R. Cudworth Professor
Purushotham Bangalore's faculty-led research profile covers Cyber Security, Data Analytics, Distributed Systems, and High-Performance Computing.
Rad Lab
Northeastern University, US
Tina Eliassi-Rad · Inaugural Joseph E. Aoun Professor
What can networks reveal about patterns hidden in large collections of connected data? The group works at the intersection of data mining, machine learning and network science, developing algorithms for analyzing physical and social phenomena. Applications of this research include fraud detection, cyber situational awareness, drug discovery and online discourse. The program also investigates ethical questions in machine learning and AI. Its algorithms have been incorporated into graph-analysis systems and open-source software, linking theoretical and algorithmic research with practical uses of networked data.
Independently curated · UnclaimedRunlong Yu Research Group
The University of AlabamaRunlong Yu · Assistant Professor
Runlong Yu's faculty-led research profile covers Artificial Intelligence, Data Analytics, Generative AI & LLMs, and Geospatial Analysis.
Independently curated · UnclaimedSudip Mittal Research Group
The University of AlabamaSudip Mittal · Associate Professor
Sudip Mittal's faculty-led research profile covers Artificial Intelligence, Cyber Security, Data Analytics, and Digital Twins.
Independently curated · UnclaimedVipin Chaudhary Research Group
Case Western Reserve UniversityVipin Chaudhary · Kevin J. Kranzusch Professor in Computer & Data Science
How can high-performance computing accelerate machine learning on very large scientific datasets to enable faster discoveries? The group pursues projects in high performance computing and machine learning, applying compiler and network expertise to scale algorithms for large data processing. Researchers develop methods in artificial intelligence and quantum computing to explore new computation paradigms and improve performance of scientific applications. The lab uses computer-aided diagnosis and digital image processing techniques to analyze biomedical data and support intervention research. The group secures and manages large externally funded projects to translate scalable computing methods into deployed research infrastructure.
Showing 1–14 of 14 labs