Dimitrios Kaltsas, 

Assistant Professor, Department of Physics, University of Ioannina

My primary interests in the study of equilibrium, stability, and dynamics of laboratory and astrophysical plasmas, employing Hamiltonian fluid dynamics, kinetic and hybrid models, as well as modern machine learning techniques. A significant focus of my recent work lies in physics-informed machine learning (PIML). I am particularly interested in the Hamiltonian formulation of novel hybrid fluid-kinetic models that preserve fundamental conservation laws. These models have direct applications in understanding magnetic confinement in fusion devices such as tokamaks, as well as in exploring astrophysical plasma phenomena like magnetic reconnection. My research also includes the development of computational methods that inherently respect conservation laws, and the use of deep learning, especially PIML approaches, to solve the complex differential equations arising in plasma physics and related systems.