Data Science aims at gaining insights about complex realworld effects through information from existing datasets. Modern datacentric approaches combine deep foundations in Statistics and Applied Mathematics with stateoftheart algorithms and provide a basis for Computer Science, Artificial Intelligence (AI), and Machine Learning. Dataenabled 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 dataintensive science. This 4th paradigm activity includes the wealth of interesting datadriven 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 cuttingedge 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 crossdisciplinary applications in Engineering and the Sciences.
RECENT SIGNIFICANT PAPERS

Bryan IV, J. S., Sgouralis, I., & Pressé, S. (2020). Inferring effective forces for Langevin dynamics using Gaussian processes. The Journal of Chemical Physics, 152(12), 124106.
 Jazani, S., Sgouralis, I., Shafraz, O. M., Levitus, M., Sivasankar, S., & Pressé, S. (2019). An alternative framework for fluorescence correlation spectroscopy. Nature Communications, 10(1), 110.
 V. Maroulas, J. Mike, and C. Oballe (2019). Nonparametric Estimation of Probability Density Functions of Random Persistence Diagrams. Journal of Machine Learning Research, 20 (151), pp.149.
 V. Maroulas, F. Nasrin, and C. Oballe (2020). A Bayesian framework for persistence homology. SIAM Journal on Mathematics of Data Science, 2(1), pp. 4874, 2020.
 Tavakoli, M., Jazani, S., Sgouralis, I., Shafraz, O. M., Sivasankar, S., Donaphon, B., … & Pressé, S. (2020). Pitching singlefocus confocal data analysis one photon at a time with bayesian nonparametrics. Physical Review X, 10(1), 011021.
 J. Townsend, C. Micucci, J. H. Hymel, V. Maroulas, and K. Vogiatzis (2020). Representation of Molecular Structures with Persistent Homology Leads to the Discovery of Molecular Groups with Enhanced CO_{2} Binding. Nature Communications, 11(1), pp. 111
CURRENT FUNDING
 PI V. Maroulas is currently supported by the Army Research Office, the Army Research Lab, the National Science Foundation (with 3 awards), and the Oak Ridge Associated Universities (ORAU).
 PI I. Sgouralis is currently supported by the University of Tennessee, Knoxville StartUp.
ALUMNIFORMER MEMBERS
Postdocs:
Farzana Nasrin (2020); Mentor: V. Maroulas; Current Position: TenureTrack Assistant Professor at the University of Hawaii, Manoa.
Ph.D. Students:
Christopher Oballe (2020); Advisor: V. Maroulas; Current Position: Postdoc at University of Notre Dame
Adam Spannaus (2020); Advisor: V. Maroulas; Current Position: Postdoc at Oak Ridge National Lab
Cassie Micucci (2020); Advisor: V. Maroulas; Current Position: Machine Learning Engineer at Eastman
Le Yin (2020); Advisor: V. Maroulas;
Xiaoyang Pan (2018); Advisor: V. Maroulas; Current Position: Quant at BB&T
Andrew Marchese (2017); Advisor: V. Maroulas; Current Position: Senior Data Scientist at the New York Times
Joshua Mike (2017); Advisor: V. Maroulas; Current Position: Postdoc at Michigan State University
Kai Kang (2016); Advisor: V. Maroulas; Current Position: Postdoc at MIT