Biography

Joshua Owolabi Adeleke is a fourth-year PhD candidate in Applied Mathematics at the Illinois Institute of Technology in Chicago focusing on analysis and partial differential equations. His current research focuses on measure-theoretic scalar balance laws, and he has worked on high-dimensional tensor algorithms — such as Tucker-tensor techniques for Kohn-Sham density functional theory. Joshua aims to use mathematics with simulation, and physics, and has had a few national-laboratory appointments. He was a Graduate Student Intern (ST-2) under Nuclear & Particle Physics, Astrophysics & Cosmology group at Los Alamos National Laboratory where he worked on building a framework for machine-learning algorithms for nuclear scattering, and he is a 2025 summer Research Aide at Argonne National Laboratory’s Center for Nanoscale Materials. Besides his passion for mathematics, Joshua relishes taking leisurely walks and sharing scientific history tales with the younger generation. Joshua hopes to combine keen theoretical intuition with a data-driven paradigm to tackle complex problems in physics and technology.

Academic Status

PhD Student - 4th

Research Area/Department

Applied Mathematics; Mathematics; Physics

Major/Specialty

Applied Mathematics

Degrees Earned or in Progress

Ph.D./Applied Mathematics/2027 M.Sc./Mathematical Engineering/2022 B.Sc./Physics/2016

Academic Preparation

I have completed a rigorous blend of graduate-level mathematics and computational courses that directly support a research-focused summer internship. Core coursework includes Partial Differential Equations, Functional Analysis, Measure Theory, and Applied Analysis, which built my foundation in modeling, proof techniques, and analytical problem solving. On the computational side, I have taken Parallel Computing and Numerical Methods, Machine Learning, and High-Performance Computing seminars, where I gained experience with Python, MATLAB, and numerical linear algebra for large-scale simulations.

Research/Academic Interests

My research centers on analysis and partial differential equations, with a focus on measure-theoretic scalar balance laws and Lie symmetry methods for nonlinear PDEs. I am particularly interested in how symmetry structures and entropy principles govern the existence, uniqueness, and qualitative behavior of weak solutions in fluid and kinetic models.

Computational and Data Science Areas

Applied Mathematics; Electrical, Electronic, and Information Engineering; Fluid and Plasma Physics; Materials Engineering; Statistics and Probability

Motivation

I am thrilled to engage in the Sustainable Research Pathways (SRP) initiative because it is closely aligned with my research priorities as well as my values as a scholar committed to inclusive science. As a fourth-year Ph.D. candidate in Applied Mathematics at Illinois Institute of Technology, I am involved in the analysis of partial differential equations, Lie symmetry methods, and high-dimensional tensor algorithms, with applications from quantum materials. I view SRP as the most suitable opportunity to extend this work into the NSF National Artificial Intelligence Research Resource (NAIRR) community. The opportunity to work with scientists and engineers from all backgrounds will expand my perspective on how AI and advanced mathematics can be harnessed together to solve challenging problems in broad research sectors such as materials science, energy, and climate. Equally significant, SRP’s focus on mentorship and belonging resonates with my own mentoring of undergraduate students as well as outreach to younger students. I hope to develop not just new technical skills and interdisciplinary collaborations, but also meaningful professional connections that embody this program’s vision of a science and technology ecosystem where everyone belongs, everyone flourishes, and everyone benefits.

Lightning Talk Title

Unidirectional Euler–Alignment: Entropy, Disintegration, Stability

Keywords (Maximum 20 words)

Nonlocal Partial Differential Equations; Entropy and Weak Solutions; Measure Disintegration; Collective Dynamics; Euler–Alignment Systems; Scientific Machine Learning; Mathematical Physics.