After obtaining his PhD degree from the Mechanical and Aerospace Engineering Department of UCLA in 1986, Dr. Dimitris Goussis joined the Department of Mechanical and Aerospace Engineering at Princeton University as a Research Staff until 1991. He then returned to Greece, where he joined the faculty of the Mechanical Engineering Dept. at the University of Patras and then the faculty in the Mechanics Section of the School of Applied Mathematical and Physical Sciences at the National Technical University of Athens. He joined Khalifa University in 2016.
The major focus of Dr. Goussis' work is the development of algorithmic methodologies for the acquisition of the essential physical understanding, by analyzing mathematical models that simulate physical phenomena, with applications in the fields of combustion, biology, pharmacokinetics and mechanics. He has been a member of the organizing committee of the Int. Conference on Numerical Combustion and currently serves in the organizing committee of the Int. Workshop on Model Reduction in Reacting Flows. He has participated in a number of research projects funded by the European Commission, NASA, etc.
Feature selection for Machine Learning algorithms.
Collaborators: Mr. Mohammad Al Radaideh (U. Michigan), Prof. S. Rigopoulos (Imperial College)
Optimal feature selection in Machine Learning (ML) algorithms is extended beyond the dependent variables that are included in the model. The variables in a model might not be the optimal features, so further research is required for the development of methodologies for the optimal ones.
Asymptotic analysis of DME/CH4 ignition
Collaborators: Dr. E.A. Tingas (Napier University) and Prof. E. Mastorakos (Cambridge).
Investigation of the exact mechanisms, related to chemical reactions and transport processes, that control the evolution of non-premixed ignition phenomena, like those met in gas turbines and compression ignition engines.
Model reduction in biology models
Collaborator: D. Patsatzis (University of Napoli).
Acquisition of system-level understanding in biology models, such as Michaelis-Menten, TMDD, monoclonal antibodies, etc.
The major focus of Dr. Goussis' work is the development of algorithmic methodologies for the acquisition of the essential physical understanding, by analyzing mathematical models that simulate physical phenomena, with applications in the fields of combustion, biology and pharmacokinetics. He has been a member of the organizing committee of the Int. Conference on Numerical Combustion and serves in the organizing committee of the Int. Workshop on Model Reduction in Reacting Flows. He has participated in a number of research projects funded by the European Commission, NASA etc.
