Mihalis Kavousanakis

Mihalis Kavousanakis is an Associate Professor at the School of Chemical Engineering, with a focus on computational methods and machine learning applications in chemical engineering. His research spans transport phenomena, tumor growth modeling, and process intensification.

• Research Highlights: Application of Physics-Informed Neural Networks (PINNs) to transport phenomena, machine learning models for polymer properties, computational tumor growth studies, and AI-driven catalysis optimization.
• Conference Participation: Active involvement in organizing and presenting at conferences like EPIC, HICOMP, and EFCE Spotlight Talks.
• Student Supervision: Supervises advanced research projects, including diploma theses on topics ranging from GIS applications to neurotoxin binding energy simulations.