• Request Info
  • Visit
  • Apply
  • Give
  • Request Info
  • Visit
  • Apply
  • Give

Search

  • A-Z Index
  • Map

Mathematics

  • About
    • Assessment Plan
    • Bylaws
    • Mission Statement
    • Newsletter
    • Open Jobs
    • SharePoint Site
  • People
    • Administration
    • Faculty
    • Visiting Scholars
    • Graduate Students
    • Staff
  • Undergraduate
    • Major/Minor/Honors
    • Scholarships
    • Careers
    • Math Placement
    • Testing Support Center
    • The Math Place
  • Graduate
    • Degree Programs
    • Funding & Assistantships
    • Request Information
    • Application Checklist
    • Apply Now
    • Handbook
    • MGSC
  • Research
    • Faculty in Research Areas
    • Grants
    • Labs
  • Alumni and Friends
    • Connect with Us
    • Recent PhDs
    • Board of Visitors
  • Events and Visitors
    • Calendar
    • SIAM SEAS 2025
    • Barrett Memorial Lectures
    • Honors Day 2025

Statistics and Data Analysis

Data Science aims at gaining insights about complex real-world effects through information from existing datasets. Modern data-centric approaches combine deep foundations in Statistics and Applied Mathematics with state-of-the-art algorithms and provide a basis for Computer Science, Artificial Intelligence (AI), and Machine Learning. Data-enabled discoveries, permitted only due to recent methods and the advent of modern computing power, accelerate innovation across the Sciences and Engineering and bridge together distant fields giving rise to Information Engineering and Bioinformatics.

As the editorial (Jasra, Law and Maroulas,  2019) noted, the recent trend driving the enormous interest in the Data Science field is the explosion of available data, which has led to the emerging 4th paradigm of data-intensive science. This 4th paradigm activity includes the wealth of interesting data-driven problems arising from machine learning and AI communities. Complex data problems require a mathematical disciplinary interplay to develop new theories and address interdisciplinary questions. For example, in recent years, there have been many new theoretical developments combining ideas from topology and geometry with statistical and machine learning methods, for data analysis, visualization, and dimensionality reduction. Applications range from classification and clustering in fields such as action recognition, handwriting analysis, and natural language processing, and biology, to the analysis of complex systems, for example, related to national defense and energy networks.

Faculty in the Statistics and Data Analysis group are leaders in cutting-edge research at the frontier of Science and Engineering. Our interests cover both foundational research in Statistics and Computation, Topological Data Analysis, Statistical Learning, Bayesian Nonparametrics as well as numerous cross-disciplinary applications in Engineering and the Sciences.

Statistics and Data Analysis

  • Vasileios Maroulas
  • Jan Rosinski
  • Ioannis Sgouralis

Department of Math

College of Arts and Sciences

227 Ayres Hall
1403 Circle Drive
Knoxville TN 37996-1320

Email: math_info@utk.edu

Phone: 865-974-2461

The University of Tennessee, Knoxville
Knoxville, Tennessee 37996
865-974-1000

The flagship campus of the University of Tennessee System and partner in the Tennessee Transfer Pathway.

ADA Privacy Safety Title IX