Joshua Lee Padgett
Department of Mathematics and Statistics
Texas Tech University
Broadway and Boston
Lubbock, Texas 79409-1042
Email: firstname.lastname@example.org Office: MATH 219
I am currently a Research Associate in the Department of Mathematics and Statistics at Texas Tech University and an Affiliated Faculty member at the Center for Astrophysics, Space Physics, and Engineering Research. I received my Ph.D. in Mathematics from Baylor University in August 2017 under the advisement of Qin "Tim" Sheng. Before that I received my B.S. in Mathematics from Gardner-Webb University, where I also was a member of the Track and Field team (I competed in the javelin and hammer throw). My undergraduate studies also included cellular biology, which led to several semesters worth of research into various aspects of cancer metabolism (in particular, the Warburg effect).
Here is a copy of my most recent Curriculum Vitae (updated June 2020).
A link with the information regarding the JMM Special Session I co-organized in 2020, may be found here.
A link with the information regarding the XVIII Red Raider Minisymposium I am co-organizing, may be found here. Due to health concerns related to the spread of COVID-19, the minisymposium is being rescheduled for Fall 2020 (more information to come).
With the support of Erica Graham, Candice Price, and Shelby Wilson, an amazing colleague of mine, Raegan Higgins, maintains the website Mathematically Gifted and Black (which can be found by following the link). Please visit this webpage in order to learn more about the issue that black scholars face in academia and mathematics. The page also contains details regarding how one can support and nominate black scholars.
Below you can find some (what I believe to be) important recent updates regarding myself or my research.
My primary research interests lie in the areas of numerical analysis, applied mathematics, and com- putational mathematics. I am particularly interested in problems arising in biology and physics which exhibit nonstandard computational challenges—such as problems with singular, nonlocal, or stochastic influences. My work has employed a variety of mathematical techniques, with a particular focus on combining computational techniques with those from operator theory, spectral theory, and Lie group theory. My most significant contributions have been the development of the abstract numerical analysis for such problems, which allows for the obtained results to have a wider range of applications. Such efforts allow for the construction of qualitatively and quantitatively superior computational algorithms. Moreover, it allows for the results to be applied to numerous physical problems of interest, such as those arising in mathematical biology, combustion theory, and plasma physics.
My recent research efforts have been focused on machine learning and deep artificial networks, with a particular emphasis on how these tools may be employed to efficiently approximate high-dimensional partial differential equations. This area is of great importance in the scientific community and garners interest from a wide array of academic disciplines. My current focus is on the theoretical and mathematical considerations of deep learning; i.e., my focus is on proving theorems regarding deep artificial networks. The mathematics for this field is still in its infancy, and as such, there are a great deal of exciting problems to be pursued in this direction.
Topics of interest:
The following is a list of grant proposals that have either been funded, or are pending.