Johann Rudi (he/him/his) graduated with a Ph.D. in Computational Science, Engineering, and Mathematics at The University of Texas at Austin; advisors: Prof. Omar Ghattas (he) and Prof. Georg Stadler (he).
Prior to joining The University of Texas, he graduated from the Paderborn University in Germany with the German Diploma degree in a multi-disciplinary program called Techno-Mathematics (the Diploma is comparable to a Master's degree with thesis); advisor: Prof. Angela Kunoth (she).
He worked at Argonne National Laboratory's Mathematics and Computer Science division before coming to Virginia Tech. Johann Rudi's research is interdisciplinary and spans large-scale parallel iterative methods for nonlinear and linear systems, development and implementation of algorithms for high-performance computing (HPC) platforms, computational aspects of inverse problems, and quantification of uncertainties in the inferred parameters.
Specifically, he has worked on adjoint-based, sampling-based, and machine learning-based methods for inference, as well as parallel multigrid and Schur complement approximation techniques for nonlinear Stokes equations.
His work has been recognized by honors including the Argonne Wilkinson Fellowship, the George Michael Memorial HPC Fellowship, and the 2015 Gordon Bell Prize.
He has been collaborating with mathematicians, data scientists, and domain scientists, such as, geophysicists (Earth's mantle convection models), physicists (relativistic electron models), neuroscientists (spiking neuron models), and computer scientists (HPC).