Chinedu Eleh

(PhD Candidate, DMS)

If only we understand science is built on common sense, we would be scientists like the magi

From nature, ten digits; from necessity and imagination, all else. Numbers are the beautiful tools of our creation.

I am a mathematician with a strong foundation in pure mathematics (B.Sc., M.Sc.) and a Master's degree in Data Science, I am passionate about translating theory into practical applications. I am currently pursuing a Ph.D. in Applied Mathematics with a focus on machine learning and its applications in multi-scale modeling. My research revolves around optimal upscaling of differential equations with random coefficients using finite element methods (FEM).

Research Focus

My research tackles the challenge of:

Computational inefficiency: Classical FEM struggles with roughness or randomness in systems, requiring highly refined meshes for accurate solutions, making computations prohibitively expensive.
Upscaling with machine learning: My work explores leveraging state-of-the-art machine and deep learning models to capture the functional and statistical behaviors of these systems, enabling efficient, scalable, and accurate FEM simulations.

Expertise

Applied mathematics
Scientific computing
Data analysis
Machine/Deep learning
Optimization (understanding and improving upon the family of ADAPTIVE gradient descent methods)

I am seeking opportunities to collaborate with passionate individuals and teams on innovative solutions that combine the power of mathematics, statisics, and machine learning across various fields. I am also passionate about teaching and happy to crack any questions you may have about mathematics, statistics or machine learning

My Ph.D. advisor is Dr. Hans Werner van Wyk.