Bio
I received my Ph.D. in Mathematics in 2019 under the supervision of Prof. David Nicholls at the University of Illinois at Chicago. I am currently a research scientist in the Argonne Leadership Computing Facility Argonne National Laboratory. I develop scalable and efficient machine learning models for scientific and engineering applications. I am also interested in the development of differential equations-based neural networks that aim at utilizing well-grounded differential equations theory to solve differential equations and/or discover hidden dynamics.
I am a Mathematically Gifted and Black (MGB) 2021 honoree and I was also selected as a 2022 rising star in computational and data sciences by UT Austin’s Oden Institute for Computational Engineering and Sciences, Sandia National Laboratories, and Lawrence Livermore National Laboratory.
Selected Publications
Carlo Graziani and Marieme Ngom, "Targeted Adaptive Design", Submitted to SIAM UQ (2022)
Marieme Ngom and Oana Marin, "Fourier Neural Networks as Function Approximators applied to Differential Equation Solvers", Statistical Analysis and Data Mining (2021).
Marieme Ngom and David P. Nicholls, "Well-Posedness and Analyticity of Solutions to a Water Wave Problem with Viscosity", Journal of Differential Equations Volume 365, Issue 10, 5031-5065 (2018)
Supervising
Scalable Gaussian Processes and Targeted Adaptive Design on ThetaGPU, Yuyang (Edward) Tian (University of Chicago), Metcalf fellow, Argonne National Laboratory, Summer 2022.
On the SINDy algorithm at Large Scale, Grant Bruer (Georgia Institute of Technology), Givens associate, Argonne National Laboratory, Summer 2021