معرفی
Edinah Gnang is an Assistant Professor of Applied Mathematics and Statistics at Johns Hopkins University. His research focuses on discrete mathematics, graph theory, multilinear algebra, and experimental mathematics, with notable contributions to hypermatrix spectral decomposition and arithmetic formula complexity. He has been funded by APL and NSF, and recognized with the Spira Teaching Award and Joel Dean Award.
Education: B.S. in Mathematics and Physics from University of Montreal (2005); Ph.D. in Computer Science from Rutgers University (2013). Postdoctoral positions included the Institute for Advanced Study and Princeton University's Center for Computational Intractability (2013-2014). Joined JHU in 2017 after serving as Golomb Visiting Assistant Professor of Mathematics at Purdue University.
Research highlights include developing the first hypermatrix spectral decomposition theorem, optimal arithmetic formula algorithms, and bridging hypermatrix algebra with machine learning applications. His work integrates combinatorial, numerical, and algorithmic approaches to tackle challenges in signal analysis and medical imaging.
Awards include teaching excellence recognitions and research grants. Current research explores extending linear algebra to hypermatrices and analyzing non-linear systems' solution complexity.
