Vasileios
Maroulas
http://www.math.utk.edu/~maroulas 

“It is through science that we prove, but through intuition that we discover.”
Personal
Research Statement:
My research portfolio is by nature
interdisciplinary and focuses on
computational mathematics and statistics
with applications
of their theory in
several engineering
and scientific problems. Precisely,
I work in the
area of computational
Bayesian filtering, large deviations, stochastic
optimization and phylogentics. I also
develop mathematical
data science methods using statistical
learning, stochastic modeling, topology and
geometry. The foci of
applications are big data problems related
to national security and defense, e.g.
multi
object trajectory estimation, biology at
the cellular level, e.g. intracellular
movements, material science, e.g.
quantifying uncertainty related to high
entropy alloys (HEAs) materials,
physiological, e.g. understanding of
kidney functions, and clinical, e.g.
analyzing kidney exchange graphs.
I am deeply grateful
and thankful to the AFOSR, ARL, ARO, DOE,
NSF, the Simons Foundation, and the
Leverhulme Trust Fellowship in the UK, for
funding my research.
Below is a very short sample
of my research. Please look at my resume
or Google Scholar for my publications.
A.
Journal Papers 1.
A.
Budhiraja, P. Dupuis and V. Maroulas. Large
deviations for infinite dimensional stochastic
dynamical systems, Annals of
Probability, 36(4), (2008), 13901420. 2.
A.
Budhiraja, P. Dupuis and V. Maroulas. Large
deviations for stochastic flows of diffeomorphisms.
Bernoulli, 16(1),
234256, 2010. 3.
A.
Budhiraja, P. Dupuis and V. Maroulas. Variational
representations for continuous time processes.
Annales de l’
Institut de Henri Poincare, 47(3), pp. 725747,
2011. 4. V. Maroulas and P. Stinis. Improved
particle filters for multitarget tracking. Journal of
Computational Physics, 231(2), pp.602611, 2012. 5.
V.
Maroulas and J. Xiong. Large
deviations for optimal filtering with fractional
Brownian motion. Stochastic
Processes and their Applications, 123(6), pp.
23402352, 2013. 6. D.C. Jhwueng and V.
Maroulas. Phylogenetic
OrnesteinUhlenbeck regression curves. Statistics &
Probability Letters, 89, pp. 110117, 2014. 7.
V. Maroulas and A. Nebenfuhr. Tracking
rapid intracellular movements: a Bayesian random set
approach. Annals of Applied Statistics,
9(2), pp. 926949, 2015. 8. G. Ren, V. Maroulas and
I.D. Schizas. Distributed
SensorsTargets Spatiotemporal Association and
Tracking. IEEE
Transactions on Aerospace and Electronic Systems, (51) 4, pp.
25702589 2015. 9. I. Sgouralis, V. Maroulas and A. Layton. Transfer function analysis of dynamic blood flow control in the rat kidney, Bulletin of Mathematical Biology, 78(5): 92360, 2016 10. J. Mike, C. Sumrall, V. Maroulas and F. Schwartz. Nonlandmark classification in paleobiology: computational geometry as a tool for species discrimination. Paleobiology, 111, 2016. 11. I. Sgouralis, A. Nebenfuhr,
and V.
Maroulas. A
Bayesian topological framework for the identification
and reconstruction of subcellular motion.
SIAM Journal on Imaging Sciences, 10(2), pp.
871899, 2017. 12. E. Evangelou and V. Maroulas. Sequential Empirical Bayes method for filtering dynamic spatiotemporal processes. Spatial Statistics, 21(Part A), pp. 114129, 2017. 13. F. Bao and V. Maroulas. Adaptive Meshfree Backward SDE Filter. SIAM Scientific Computing 39(6), A26642683, 2017. 14. K. Kang, V.
Maroulas, I. Schizas and F. Bao. Improved
distributed particle filters for tracking in
wireless sensor network. Computational
Statistics and Data Analysis, (117), pp. 90108,
2018.
B.
Code J. Mike and V. Maroulas.
Combinatorial Hodge theory for equitable kidney
paired donation. Submitted 2016. For the
code of the associated paper click here.
C.
Conference Papers
(peerreviewed)
1. A.
Aduroja, I. D. Schizas and V. Maroulas. Distributed
principal component analysis in sensor networks. IEEE
Proceedings of ICASSP, pp. 58505854, 2013. 2. K. Kang and V. Maroulas.
Drift
homotopy methods for a nonGaussian filter.
The
Proceedings of Data Fusion, pp. 10881094,
2013. 3. V. Maroulas, K. Kang, I. D.
Schizas and M. W. Berry. A
Learning Drift Homotopy Particle Filter, The Proceedings of
Data Fusion, pp. 19301937, 2015. 4. A. Marchese
and V. Maroulas. Topological learning for acoustic
signal identification. The Proceedings of Data
Fusion. pp. 13771381, 2016. 5. A.
Marchese, V. Maroulas and J. Mike. KMeans clustering on
the space of persistence diagrams. Wavelets and
Sparsity XVII, SPIE Conference Proceedings, 2017.
D.
Other Contributions
(peerreviewed)
E. PhD Thesis

