As a PostDoc scholar at Berkeley Lab I am continuing working on the development and numerical solutions of forward solvers. This time, the forward solver is Feynman’s Path Integral and originated from Quantum Electrodynamics. A fast solution could revolutionize x-ray and electron microscopy. The second project is the field of machine learning. To experimental x-ray scattering scientists, I am developing a method that can steer experiments autonomously. The ultimate goal is to investigate a microscopic object and discover new characteristics completely without human interaction.
As a PhD Candidate my research was in the field of applied mathematics and computational physics. I solved wave related problems numerically with the aim of minimizing computational costs and still maintaining good accuracy. There is a wide range of applications including experimental solid state physics, medical and geological modeling, telecommunication and machine learning as well as seismic imaging. It is a promising research field, since the wave equation is one of the most influential equation. More recently, I have been dealing with optimization problems. The result is a very sophisticated optimization method that could lead to a paradigm shift in many areas of research and industry. Both fields, wave propagation and optimization, give me the chance to get a deeper understanding of fundamental natural processes.