Mechanical Engineering and Applied Mechanics (MEAM)
Honors and Awards: DOE Early Career Award (2018), Doctoral Thesis Award - Circle of Hellenic Academics in Boston (2017), Best PhD Thesis Award in Biomedical Engineering - Conference on Computational and Mathematical Biomedical Engineering (2017)
Research Expertise: Computational Science and Engineering, Machine learning and Data-driven Modeling, Design under Uncertainty, High-performance Computing
Paris' work spans a wide range of areas in computational science and engineering, with a particular focus on the analysis and design of complex physical and biological systems using machine learning, stochastic modeling, computational mechanics, and high-performance computing. Prior to Penn, he spent two years as a post-doctoral researcher at MIT developing machine learning algorithms that synergistically combine multi-fidelity data with prior knowledge (e.g. differential equations and the conservation laws of mathematical physics) towards establishing a new paradigm in predictive modeling and decision making under uncertainty.
Ph.D. - Applied Mathematics - Brown University (2015)
M.Sc. - Applied Mathematics - Brown University (2010) Diploma - Naval Architecture & Marine Engineering
National Technical University of Athens (2009)