Dr. David Williams

Associate Professor of Mathematics

Office: James M. Baker University Center 431
Mathematics
College of Information and Mathematical Sciences
davidwilliams@clayton.edu
Phone: (678) 466-4429

Biography

Dr. Williams received his Ph.D. in Applied Mathematics from the University of Washington. His professional interests lie mainly in solving systems of ODEs and PDEs approximately...both analytically (using perturbation theory) and numerically. Recently, he has added machine learning and artificial intelligence to his list of professional interests after earning an additional M.S. in Data Science from Clayton State University in 2025. He enjoys reading sci-fi/fantasy novels, drinking good beer, and playing games with family and friends.

Education

Ph D, Applied Mathematics, University of Washington, 2005

MS, Data Science, Clayton State University, 2025

MS, Applied Mathematics, University of Washington, 1999

BS, Mathematics, California State University, Stanislaus, 1997

Intellectual Contributions

Blessing Mudavanhu, Robert E O'Malley, Jr., David B Williams, Working with multiscale asymptotics, Journal of Engineering Mathematics – December 2005

Karl R Knaub, Robert E O'Malley, Jr., David B Williams, The slow motion of shock layers for advection-diffusion-reaction equations, Applied Numerical Mathematics – January 2005

Elliot Krop, Christopher Raridan, Michael Dancs, Christian Barrientos, Scott Bailey, Keith Driscoll, David B Williams, Some new bounds for edge-magic graphs, Congressus Numerantium/Utilitas Mathematica Publishing, Inc. –

Mohammed Abdel-Aal, Sarah Minion, Christian Barrientos, David B Williams, The Mean Labeling of Some Crowns, Journal of Algorithms and Computation – December 30 2014

Teaching Interest

Ordinary and Partial Differential Equations, Asymptotic and Numerical Methods, Applied Complex Variables, Introductory Computer Science

Research Interest

Singular Perturbation Theory, Multiscale Asymptotic Analysis, Ordinary and Partial Differential Equations, Applied Mathematics, Machine Learning, Artificial Intelligence