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Machine Learning and High Dimensional Data

Data points funneling into a stream.
Machine learning focuses on the creation, characterization and development of algorithms that, when applied to data, allow us to understand their structure, make predictions and construct counterfactual analyses. This area of research is fundamental to applied statistics and data science and drives many of their recent advancements. Our faculty actively research methodological and computational approaches to machine learning, especially in high-dimensional data and data-intensive computing, to create new tools for scientific discovery. We are particularly interested in applications of these tools to genetics, clinical trials, neuroimaging and other areas of biomedicine and we regularly collaborate with researchers in those fields.

Faculty of Interest

  • Assistant Professor of Biostatistics (Biostatistics)

    Research Interests
    • Behavioral Sciences
    • Health Plan Implementation
    • HIV
    • Global Health
    • Causality
    • Clinical Trial
    • Social Networking
    • Observational Study
  • Assistant Professor of Biostatistics (Biostatistics)

    Research Interests
    • Genetics
    • Immune System Diseases
    • Neurosciences
    • Computational Biology
    • Statistics
    • Gene Regulatory Networks
    • Metabolic Networks and Pathways
    • Epigenomics
    • Machine Learning
  • Elihu Professor of Biostatistics and Professor of Ecology and Evolutionary Biology

    Research Interests
    • Algorithms
    • Bacteria
    • Bacterial Infections and Mycoses
    • Beer
    • Bread
    • Cell Transformation, Neoplastic
    • Coccidioidomycosis
    • Computing Methodologies
    • Biological Evolution
    • Fungi
    • Genetic Engineering
    • Microbiological Phenomena
    • Models, Genetic
    • Models, Theoretical
    • Mycoses
    • Neoplasm Metastasis
    • Neoplasms
    • Phylogeny
    • Viruses
    • Wine
    • Models, Statistical
    • Likelihood Functions
    • Logistic Models
    • Polymerase Chain Reaction
    • Sequence Analysis, DNA
    • Nonlinear Dynamics
    • Molecular Epidemiology
    • Gene Transfer Techniques
    • Crops, Agricultural
    • Evolution, Molecular
    • Nature
    • Sequence Analysis, Protein
    • Gene Expression Profiling
    • Public Health Informatics
    • Microarray Analysis
    • Genetic Speciation
    • Host-Pathogen Interactions
    • Genetic Phenomena
    • Mathematical Concepts
    • Organisms
    • Phenomena and Processes
  • Associate Professor of Biostatistics (Biostatistics)

    Research Interests
    • Algorithms
    • Eye Diseases
    • Disorders of Environmental Origin
    • Pregnancy Complications
    • Probability
    • Statistics as Topic
    • Stochastic Processes
    • Virus Diseases
    • Statistical Distributions
  • Susan Dwight Bliss Professor of Biostatistics, Professor in the Child Study Center and Professor of Statistics and Data Science

    Research Interests
    • Child Psychiatry
    • Epidemiology
    • Infertility
    • Mental Health
    • Pregnancy
    • Psychiatry
    • Computational Biology
    • Statistics
    • Genomics
    • Biostatistics
  • Department Chair and Ira V. Hiscock Professor of Biostatistics, Professor of Genetics and Professor of Statistics and Data Science

    Research Interests
    • Genetics
    • Public Health
    • Computational Biology
    • Statistics
    • Genomics
    • Proteomics
    • Biostatistics
    • Single-Cell Analysis
    • Microbiota
    • Wearable Electronic Devices