Jonathan Madsen earned his Ph.D. from Texas A&M University in Nuclear Engineering in December 2017 under the supervision of Dr. Ryan G. McClarren and Dr. John Ford and earned his M.S. in Nuclear Engineering (Health Physics) in August 2013 under the supervision of Dr. Gamal Akabani.
His doctoral research focused on applying the concepts of compressed sensing to Monte Carlo scoring arrays in an effort to reduce memory allocation and reduce computation time through statistical de-noising during the reconstruction of the solution.
His masters research focused on extending the classical Monte Carlo transport approach to utilize a quantum-mechanical description of the electron probability densities. The goal of this research was to create a generic method for approximating the discrete and molecular-specific low-energy electromagnetic interaction cross-sections for nucleotides and amino acids below the standard low-energy cut-off currently simulated by a continuous slowing-down approximation (CSDA).
High-performance computing, parallel computing, multithreading, GPU-offloading, C, C++, Python, CUDA, Monte Carlo transport, deterministic transport, porting scientific software applications to highly-parallel machines, computational physics
- Madsen, J.R. and G. Akabani, Low-energy cross-section calculations of single molecules by electron impact : a classical Monte Carlo transport approach with quantum mechanical description. Phys Med Biol, 2014. 59(9): p. 2285-305.