Summary
Joseph Kump is a graduate research assistant and PhD candidate at UT Austin’s Oden Institute specializing in applied mathematics, computational science, and machine learning with six years of experience building high-performance scientific software. He develops differentiable sea-ice models in Julia that leverage automatic differentiation for adjoint calculations and potential data assimilation, bridging numerical methods with modern ML tooling. Previously he parallelized MASW algorithms for CPUs and GPUs (MPI/CUDA), published in Computers & Geosciences, and devised a wavelet-domain cross-correlation technique that avoids time-domain reconstruction. Comfortable across HPC, Python, and Julia ecosystems, he has a track record of turning mathematical insight into scalable, open implementations. Colleagues describe him as methodical and inventive—adept at finding algorithmic shortcuts that reduce computation without sacrificing rigor.
6 years of coding experience
4 years of employment as a software developer
Doctor of Philosophy - PhD, Computational Science, Engineering, and Mathematics, Doctor of Philosophy - PhD, Computational Science, Engineering, and Mathematics at The University of Texas at Austin
Master's degree, Mathematics, Master's degree, Mathematics at Virginia Tech
General Education Degree (High School Graduate), College/University Preparatory and Advanced High School/Secondary Diploma Program, General Education Degree (High School Graduate), College/University Preparatory and Advanced High School/Secondary Diploma Program at Maggie L. Walker Governor's School for Government and International Studies