Vasileios Maroulas
Vasileios Maroulas
Professor, Assistant Vice Chancellor, Deputy Director of AI Tennessee Initiative
Vasileios Maroulas is a Professor of Mathematics with joint appointments at the Business Analytics and Statistics at the Haslam College of Business, and the Data Science and Engineering at Bredesen Center. He is also a Senior Research Fellow at the US Army Research Lab, and Elected Member of the International Statistical Institute. He serves as a co Editor-In-Chief at the Foundations of Data Science published by the American Institute of Mathematical Sciences. His research portfolio engages computational probability, statistics and machine learning with computational topology and geometry for addressing interdisciplinary problems in data science and engineering arising from biology, medicine (physiological and clinical), material science, and national defense. His research is funded by numerous agencies, including AFOSR, ARL, ARO, DOE, NSF, ORAU and Simons Foundation, and is featured in prestigious journals including Nature Communications, Annals of Applied Statistics, Annals of Probability, IEEE Transactions on Aerospace and Electronic Systems, Journal of Machine Learning Research, SIAM on Imaging Sciences, SIAM Scientific Computing, SIAM Mathematics of Data Science, and Stochastic Processes and Applications. For his research, Professor Maroulas has received a prestigious fellowship by the US Army Research Lab, the Leverhulme Trust Visiting Fellowship from the UK, and the UT’s College of Arts & Sciences Excellence in Research and Creative Achievement Award. Professor Maroulas has also strong connections with the industry and he serves in many (data science) advisory boards.
Education
Ph.D., University of North Carolina at Chapel Hill
Research
- Statistics and Data Analysis
- Computational Bayesian Statistics
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- Topological Data Analysis
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- Foundations of Data Science
- Probability and Stochastic Processes
- Applied Probability
- Mathematical Biology
- Statistical Learning Methods in Neuroscience and Molecular Biology
- Computational and Applied Mathematics
- Scientific Machine Learning
- Topology
- Applied Topology