Dimitris Giovanis
Research Assistant Professor at The Johns Hopkins University
Baltimore, Maryland, United States
Summary
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Senior🎓
Top SchoolDimitris Giovanis is a Research Assistant Professor at Johns Hopkins University with eight years of postdoctoral and research experience focused on machine learning, probabilistic modeling, and uncertainty quantification for engineering and material systems. He designs algorithms for reliable predictive modeling and inverse problems, translating stochastic finite element theory from his PhD into practical tools for complex systems. Based in Baltimore, he bridges rigorous academic research with applied engineering problems, often working at the intersection of computational mechanics and data-driven inference. Known for blending deep probabilistic insight with numerical methods, he emphasizes reliability and interpretability in models used for decision-critical applications.
8 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy (PhD), Advanced Stochastic Finite Element Simulations and Reliability Analyses, Doctor of Philosophy (PhD), Advanced Stochastic Finite Element Simulations and Reliability Analyses at National Technical University of Athens
English, French, Greek
Github Skills (15)
stochastic10
probability10
parameter-estimation10
mathematical10
uncertainty10
hpc-applications10
curve-fitting10
sensitivity-analysis10
uncertainty-quantification10
monte-carlo10
toolbox10
python10
modeling10
probabilistic10
machine-learning1
Programming languages (1)
Github contributions (5)
SURGroup/UQpy
Dec 2017 - Nov 2022
UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.
Contributions:20 releases, 29 reviews, 721 commits in 5 years
pythonstochastic-processpython-toolboxmathematicalparameter-estimation
dgiovanis/dgiovanis.github.io
May 2022 - Feb 2023
Contributions:163 commits, 148 pushes, 1 branch in 9 months
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