Firas Khasawneh

Dr. Firas Khasawneh has expertise in the field of Mechanical Engineering, with a particular focus on complex dynamical systems. As an Assistant Professor at Michigan State University, he delves into the intricacies of systems characterized by nonlinearities, delays, and stochastic effects. His research is distinguished by the integration of both numerical and experimental methodologies, providing a comprehensive understanding of these complex systems. Dr. Khasawneh's innovative approach includes the application of Topological Data Analysis (TDA) and machine learning techniques, which he leverages to enhance signal processing and advance manufacturing processes. His work in these areas not only contributes to the theoretical understanding of mechanical systems but also offers practical solutions to contemporary engineering challenges. With a Ph.D. in Mechanical Engineering and Materials Science from Duke University, Dr. Khasawneh has established a solid foundation in his field. His academic journey also includes an M.S. in Mechanical and Aerospace Engineering from the University of Missouri-Columbia, and a B.S. in Mechanical Engineering from the Jordan University of Science and Technology, reflecting a diverse and comprehensive educational background. Dr. Khasawneh's contributions to the field are well-documented through his extensive publications in reputable journals. His research has garnered attention for its depth and applicability, making significant strides in understanding and manipulating complex dynamical systems. His work is not only theoretical but also has practical implications, particularly in the realms of advanced manufacturing and signal processing. Beyond his research, Dr. Khasawneh is committed to education and mentorship, guiding the next generation of engineers through his teaching and supervision at Michigan State University. His dedication to both research and teaching underscores his role as a pivotal figure in the field of Mechanical Engineering, continually pushing the boundaries of what is possible in the study and application of complex systems.