Biography

I am a Ph.D. student in Computational and Applied Mathematics at Rice University, advised by Dr. Illya V. Hicks. My primary research focuses on mixed-integer nonlinear programming (MINLP) and optimization methods for operations research. In particular, I am developing computational frameworks for symbolic regression using MINLP and exploring new optimization strategies for large-scale decision problems. I am also investigating kernel-based numerical methods for solving partial differential equations, bridging scientific computing with optimization techniques. I hold a master’s degree in Mathematical Modelling in Engineering from the University of L’Aquila and a master’s in Financial Engineering from WorldQuant University, where I applied optimization and machine learning to financial strategies. I am proficient in Python, Julia, MATLAB, and optimization solvers such as Gurobi, SCIP, and CPLEX. Through my research and interdisciplinary background, I aim to contribute to SRP’s mission by applying advanced optimization and computational methods to sustainable and impactful problems. Beyond research, I have been actively engaged in mentorship and community service, particularly through youth entrepreneurship and educational initiatives. I am eager to contribute to SRP’s mission of fostering sustainable, interdisciplinary collaborations.

Academic Status

PhD Student - 3rd

Research Area/Department

Applied Mathematics; Data Science; Engineering; Machine Learning/AI; Mathematics; other

Major/Specialty

Ph.D. in Computational and Applied Mathematics (in progress), Rice University Major/Specialty Mixed-Integer Linear Programming (MINLP), Optimization for Operations Research, Computational Methods for PDEs, Machine Learning, HPC.

Degrees Earned or in Progress

Ph.D., Computational and Applied Mathematics, Rice University, In Progress (Expected 2028) M.Sc., Financial Engineering, WorldQuant University, In Progress, 2025 M.Sc., Mathematical Modelling in Engineering, University of L’Aquila, 2022 B.Sc., Mathematics, University of Nairobi, 2018

Academic Preparation

I have completed courses in Convex Optimization, Linear and Integer Programming, and Machine Learning with Graphs at Rice University. In addition, I studied numerical partial differential equations and numerical linear algebra during my master’s program.

Research/Academic Interests

My research interests focus on mixed-integer linear programming (MILP) and optimization methods for operations research, particularly for symbolic regression and large-scale decision problems. I am also interested in computational methods that integrate optimization with scientific computing, such as kernel-based approaches for solving PDEs and machine learning.

Computational and Data Science Areas

Applied Computer Science; Applied Mathematics; Artificial Intelligence and Intelligent Systems; Computer Science; Economics and Business

Motivation

In my graduate studies in computational and applied mathematics at Rice University, I have witnessed directly and indirectly the transformative power of collaboration in tackling the most complex scientific problems. Scientific breakthroughs, for example, in numerical models ranging from simple machine learning models, such as linear regression, to large AI models, like Large Language Models (LLMs), are the result of diverse teams that embrace an interdisciplinary approach by combining expertise from mathematics, engineering, computer science, and domain-specific fields. The main reason I am applying to the Sustainable Research Pathways program: to work alongside scientists from diverse disciplines, learn from their perspectives, and contribute my own skills to address real-world problems. My interest in connecting mathematical theory with computational practice is shown in my academic journey. As a Ph.D. student in Computational and Applied Mathematics at Rice University, my research focuses on kernel methods for partial differential equations (PDEs), symbolic regression for scientific discovery, and mixed-integer nonlinear optimization. Working on this, there has been a growing awareness of the challenges faced when constructing college STEM curricula beyond just the incorporation of mathematics and computer science courses. In particular, as a student from a low-income family in a rural area, I have both seen and experienced firsthand the struggles faced by students coming from these backgrounds. STEM fields are often dominated by students from middle- to upper-class families who have had schooling that better prepares them for a STEM degree; this domination is often implicitly reflected in the curriculum through assumptions of prior exposure to specific topics. This mismatch of curriculum content and student experience is further compounded by the lack of interdisciplinary knowledge among faculty teaching students majoring in other fields. I have witnessed this difficulty both as a student and while working as a mentor to undergraduate math students in Kenya and as a grader in computer science and CMOR courses “designed” for engineering students. My goal in becoming a teaching faculty member is to address these issues by adapting and designing curriculum content that allows students from less advantaged backgrounds to realize their talent, even in classes outside their major. In working towards this goal, I have continued to actively serve in several mentoring and volunteer positions. I have to continue such work at Rice actively. I have attended workshops and conferences by the Center for Teaching Excellence on promoting inclusion in the classroom and student organizations. From serving as a team lead for Summer Rice Data Science Camp (SRDS), I also learned the importance representation can have. I know that my presence in workshops as a volunteer or mentor to the undergrads inspires several other younger students to not only enter the STEM field but also to serve in leadership positions within it, as I experienced the first effects during my undergraduate studies. These experiences in mentorship and leadership with students across multiple majors have educated me on students’ mental, emotional, and intellectual needs in STEM education, but I have by no means learned everything, and I strive to continue to learn more still. My past experiences, coupled with my expertise in mathematics, computer science, and mechanical engineering, have laid a strong foundation for my intended career as a teaching faculty member, where I intend to use my experience to promote interdisciplinary curricula, continue mentoring and guiding students, and conduct advanced research. In particular, I am deeply interested in the intersection of artificial intelligence and scientific computing, specifically, how we can optimize the use of AI resources during training and inference for large-scale scientific applications. As scientific models grow in size and complexity, the computational demands on hardware, algorithms, and data infrastructure are increasing at an exponential rate. I believe that applying principles from operations research to the design and deployment of AI systems can significantly improve their efficiency, scalability, and environmental impact. My coursework and projects, including work on graph neural networks with large language model embeddings, optimization-based symbolic regression, and deep learning approaches for industrial systems, have prepared me to make meaningful contributions to research in this field. Participating in SRP represents a unique opportunity to advance my interests in working with leading scientists on NSF National AI Research Resource (NAIRR) pilot projects and DOE HPC initiatives. I plan to refine my research questions, explore new applications, and develop computational tools that are adopted into real-world scientific workflows. Equally important, SRP is a community where I can learn from mentors, share my own expertise, and form lasting collaborations that extend well beyond the summer research period. Both during and after the summer school, I will channel my passion for mathematics to advance mathematical tools with real-world applications and nurture a passion for mathematics in the next generation of mathematicians, especially those from underrepresented minority groups. I will share this research, as well as my enthusiasm for mathematics, by presenting at research conferences.

Lightning Talk Title

Efficient Symbolic Regression Through Optimization and Machine Learning

Keywords (Maximum 20 words)

Optimization; Mixed-Integer Linear Programming (MILP); Symbolic Regression; Scientific Machine Learning; Operations Research; Kernel Methods; Scientific Computing; HPC