Biography

My name is Temitope Oyedeji, and I am from Ibadan, Nigeria. I earned my Bachelor of Science degree in Mathematics from the University of Ibadan, Nigeria, where I developed a strong foundation in problem-solving and abstract reasoning. In 2023, I began my Ph.D. in Applied Mathematics at the University of Illinois Chicago, and I just received a Master’s degree en route to the Ph.D. in 2025. My research interests focus on numerical analysis, high-performance computing, partial differential equations, and machine learning applications in scientific computing. I have gained experience in developing and implementing scalable algorithms for distributed systems, GPU acceleration, and optimization, and I am motivated by opportunities to apply these tools to real-world challenges. As the third child in my family, I grew up valuing perseverance, adaptability, and collaboration, qualities that continue to guide my academic and professional journey. Looking ahead, I aspire to contribute to impactful research in computational science, with a particular interest in collaborations that address national and global challenges.

Academic Status

PhD Student - 3rd

Research Area/Department

Applied Mathematics; Mathematics

Major/Specialty

Applied Mathematics: Numerical analysis, high-performance computing, partial differential equations, and machine learning applications in scientific computing.

Degrees Earned or in Progress

Ph.D in Mathematics (3.71/4), University of Illinois, Chicago, May 2028 Masters of Science in Mathematics (3.63/4), University of Illinois, Chicago, August 2025 Bachelor of Science in Mathematics (3.87/4), University of Ibadan, Ibadan, Nigeria, Dec 2021

Academic Preparation

Real Analysis, Numerical Analysis, Supercomputing, Optimization, Functional Analysis, Complex Analysis, Ordinary differential equations, Classical methods of Partial Differential Equations. I also use math tools like MATLAB and programming tools like python and Julia

Research/Publications

I conducted technical projects during my PhD program. I worked on the following : Message Passing to Distribute root finding jobs | Julia, Python , MPI • Wrote a parallel program to define the message passing for a program using MPI in a static work load assignment. • Investigated the scalability when more worker nodes are available and determined the granularity of the parallel code. • Developed an algorithm for dynamic load balancing to apply for any number of processors. Shared Memory Parallel Tropical Matrix Multiplication | Julia ,openMP • Wrote a code to compute the multiplication of two matrices, on a shared memory parallel computer. • Discussed the granularity and examined the scalability GPU Accelerated Trapezoidal Rule | Julia, CUDA • Wrote a GPU accelerated program • Demonstrated the correctness by giving sample input and outputs. • Compared the times of my GPU accelerated program with a run against a version on the CPU. Did runs with various degrees and thread configurations.

Research/Academic Interests

My research lies at the intersection of applied mathematics, high-performance computing (HPC), and data-driven scientific computing. As a Ph.D. candidate in Applied Mathematics at the University of Illinois Chicago, my focus is on developing and analyzing computational methods that address large-scale scientific problems involving partial differential equations (PDEs), optimization, and numerical analysis. A major part of my current work involves scalable algorithms for distributed computation. For example, I have designed and benchmarked MPI-based root-finding algorithms, explored shared-memory parallelization of matrix multiplication with OpenMP, and implemented GPU-accelerated trapezoidal integration schemes in CUDA. These projects deepened my understanding of numerical stability and algorithmic complexity and also gave me practical experience in optimizing performance across diverse architectures. Alongside HPC, I am increasingly engaged with machine learning for scientific applications, including integrating neural networks into PDE solvers and using data-driven approaches to enhance predictive accuracy in physical models. I am also developing familiarity with quantum computing concepts, particularly in their potential applications to optimization and simulation. My technical toolkit includes Python, Julia, and MATLAB, supported by strong experience with Linux/Unix systems, Git, MPI, and GPU environments. I am motivated by the challenge of building algorithms and models that are not only mathematically rigorous but also computationally scalable and applicable to real-world systems. I see my long-term research trajectory as contributing to the development of robust, efficient, and secure computational methods that advance both scientific discovery and technological innovation.

Computational and Data Science Areas

Applied Mathematics; Performance Evaluation and Benchmarking; Statistics and Probability

Motivation

I am applying to the Sustainable Research Pathways (SRP) program because I am deeply motivated to contribute my skills in applied mathematics, HPC, and machine learning to impactful scientific collaborations with national laboratories. SRP’s mission of building bridges between underrepresented researchers and the Department of Energy (DOE) labs strongly resonates with my own goals as a researcher seeking to apply computational science to pressing challenges in energy, health, and national security. The program offers a unique opportunity to work alongside world-class scientists while expanding my experience with HPC, AI, and computational modeling at scale. I am particularly drawn to SRP because it emphasizes technical excellence, mentorship, community, and preparing future leaders in computational science. My career objective is to pursue a research-driven role where I can develop advanced algorithms and scalable models that address national challenges in areas such as biomedical monitoring, optimization of energy systems, and next-generation scientific computing. The program is an ideal step toward that goal, providing an environment where I can both contribute and learn, while strengthening my readiness for future collaborations with DOE labs. I look forward to the chance to bring my enthusiasm, technical expertise, and collaborative mindset to SRP and to grow through meaningful engagement with its research community.

Lightning Talk Title

Numerical Methods and Machine Learning for Complex PDE Systems

Keywords (Maximum 20 words)

Numerical Analysis; Partial Differential Equations; Optimization; Machine Learning; Scientific Computing; Applied Mathematics; Computational Modeling; High-Performance Computing; Stability Analysis; Convergence