Biography

Kayode Oluwasegun is a Ph.D. candidate in the Department of Mathematics, Drexel University. His research focuses on the applied analysis of nonlinear systems of partial differential equations (PDEs) with inherent significance in mathematical biology, physics, engineering, and other fields. He studies moving boundary problems, particularly those associated with disease spread. His work has led to the development of novel analytical and computational methods for solving nonlinear PDEs that have been adopted severally in the work of researchers in the United States and globally. Kayode’s career goal is to leverage his skills, knowledge, and passion to make meaningful contributions in the quest to utilize the power of math to solve real-world problems. He wants to be able to apply his analytical and computational skills to drive innovation and problem-solving with real industrial significance. He has collaborated on multiple projects with leading researchers in applied mathematics, leading to several publications. Beyond research, Kayode has extensive leadership experience. His leadership of the Graduate Student Association at Drexel University has been recognized through multiple awards. He is eager to leverage NAIRR’s computational and AI resources to advance sustainable modeling approaches and foster innovative research at the intersection of mathematics, data science, and real-world applications.

Academic Status

PhD Student - 5th

Research Area/Department

Applied Mathematics; Mathematics

Major/Specialty

Mathematics: Specialization in Partial Differential Equations, Applied and Computational Mathematics, and Data-Driven Modeling for Complex Systems

Degrees Earned or in Progress

1) PhD in Applied & Computational Mathematics (in progress) 2) MSc in Applied & Computational Mathematics (May 2021) 3) BSc in Applied Mathematics (February 2017)

Academic Preparation

1) Linear Algebra & Matrix Analysis 2) Numerical Analysis I & II 3) Partial Differential Equations I & II 4) Methods of Optimization 5) LaunchCode Full Stack Web Development Program 6) IMSI Data Science Training Bootcamp 7) INMAS Python and Machine Learning Training

Research/Publications

1. Kayode Oluwasegun, Samuel Ajibola, Udoh Akpan, Lanre Akinyemi, Mehmet Senol, “Investigation of oceanic wave solutions to a modified (2+1)-dimensional nonlinear Schrodinger coupled system” Modern Physics Letters B, 39, 2550036 (2025). https://doi.org/10.1142/S0217984925500368 2. Lanre Akinyemi, Francis Erebholo, Valerio Palamara, Kayode Oluwasegun, ”A Study of Nonlinear Riccati Equation and Its Applications to Multi-dimensional Nonlinear Evolution Equations” Qualitative Theory of Dynamical Systems, 23 (Suppl 1), 296 (2024). https://doi.org/10.1007/s12346-024-01137-2 3. Kayode Oluwasegun, David Ambrose, Douglas Wright, “Beyond Borders: Exploring Existence Theorems in Free Boundary Population Models” ScienceOpen Posters, (2024). https://doi.org/10.14293/P2199-8442.1.SOP-.PXYOFN.v1 4. Pundikala Veeresha, Lanre Akinyemi, Kayode Oluwasegun, Mehmet S¸enol, Bismark Oduro, “Numerical surfaces of fractional Zika virus model with diffusion effect of mosquito borne and sexually transmitted disease” Mathematical Methods in the Applied Sciences, 45, 2994-3013 (2022). https://doi.org/10.1002/mma.7973 5. Lanre Akinyemi, Mehmet Senol, Udoh Akpan, Kayode Oluwasegun, “The optical soliton solutions of generalized coupled nonlinear Schr¨odinger-Korteweg-de Vries equations” Optical and Quantum Electronics, 53, 394 (2021). https://doi.org/10.1007/s11082-021-03030-7

Research/Academic Interests

My research interest lies in the development and analysis of mathematical models using partial differential equations (PDEs) to address complex phenomena in biology, physics, and engineering. I have a strong interest in mathematical biology and disease modeling, particularly in understanding the dynamics of disease spread. My doctoral work involves analyzing free boundary problems to model the interaction between infected and healthy regions, providing insights into spatial disease progression. This research provides analytical and computational insights into the spatial progression of disease, with potential applications to public health. I am also fascinated by oceanic wave phenomena and have recently studied oceanic wave characteristics using a modified integrable generalized (2 + 1)-dimensional nonlinear Schrodinger (NLS) system of equations with variable coefficients. This work has deepened my interest in closing the gap between rigorous mathematical modeling and modern computational and data-driven techniques. Through the SRP-NAIRR program, I hope to further develop expertise in scientific computing, numerical methods, and data science, and apply these tools to solve interdisciplinary research problems using NAIRR's advanced AI and computing resources.

Computational and Data Science Areas

Applied Mathematics; Artificial Intelligence and Intelligent Systems; Climate and Global Dynamics; Ecology; Statistics and Probability

Motivation

I remember completing the post-conference survey for the BE program at SIAM CSE25 and answering the question, “What other SHI projects are you interested in, or have you attended any other programs before?” Part of my response included, “I would appreciate a program that specifically provides a connection between students in my category and prospective employers such as national laboratories and industries, allowing us to gain experience working outside the school setting. I know the Sustainable Research Pathways Program provides such an opportunity, and I am interested in that if it is still running.” It gladdens my heart to see this program opening at this time. Having been submerged in rigorous research activities through my ongoing PhD journey at Drexel University, I see the Sustainable Research Pathways (SRP) program as a platform to find hands-on expression for all my training and a unique opportunity to make a real-world impact. While I am grateful for my academic preparations so far, I know that beyond that is the need to engage with projects that define the realities of our time. The SRP-NAIRR partnership provides the opportunity to expand my understanding and usage of advanced computational methods, data, and AI-driven techniques to advance research that is beneficial for science and society. My research thus far has prepared me for modeling real-life problems, including the spread of invasive species by framing them as a free boundary problem and analyzing oceanic wave phenomena using advanced nonlinear Schrodinger systems, among many others. These experiences on their own have given me a strong mathematical background; however, beyond that is my desire to use the human and infrastructural resources the SRP-NAIRR program offers to build collaborations across disciplines and apply mathematical modeling to broader, societal-relevant spaces. The benefits that have been highlighted for this program are just all the support a student needs. As a graduate student navigating through the terrains of research, career development, identity building, and potential job search, the opportunity for mentoring from faculty and seasoned professionals on cutting-edge projects is all I would want right now. I do not take mentoring for granted. The overall support, including travel, housing, program participation, workshops, summer project experience, conference support, and access, means a whole lot to me as an international student. I am even more excited about how the SRP program is growing, and the new partnership with the High Performance Software Foundation (HPSF) gives me an opportunity to contribute to open-source high-performance computing projects that advance scientific discovery. I desire to work in a space that beautifies mathematics in application, and I am glad this program truly offers a pathway to AI, HPC, computational, and data science careers. I know I will be challenged and more empowered to address complex problems and amplify my research impact in healthcare, robotics, science & engineering by being part of this program.

Lightning Talk Title

Modeling Complex Systems: Applied PDEs and Computational Modeling

Keywords (Maximum 20 words)

Applied Partial Differential Equations; Mathematical Modeling; Mathematical Biology; Free-Boundary Problems; Nonlinear Dynamics; Wave Propagation; Ocean Engineering; Scientific Computing; Computational Modeling