About Me
I'm a Google DeepMind Scholar in AI for Science at the African Institute for Mathematical Sciences (AIMS), South Africa — a path that began not in machine learning, but in the abstract depths of pure mathematics.
During my Master's in Fundamental Mathematics at Université de Rennes, I had the privilege of interning at IRMAR (Institut de Recherche Mathématique de Rennes), where I worked under Prof. Frank Loray researching the Dynamics of the Mapping Class Group on Character Varieties — a field where geometry, topology, and algebra collide in beautiful complexity.
It was in France, immersed in that world of pure structure, that something shifted. I began to see how the same mathematical intuition that untangles abstract symmetries could be pointed at the messiness of real-world data. That realization brought me to AI — not as a departure from mathematics, but as a natural extension of it.
Now as a DeepMind Scholar at AIMS, I channel that foundation into what drives me most: developing interpretable AI models at the intersection of machine learning and scientific discovery — models that don't just predict, but explain. I'm actively honing my skills in machine learning, data analysis, and scientific computing, always pushing to stay at the cutting edge of what's possible when rigorous mathematics meets modern AI.
I thrive at the intersection of precision and curiosity. Whether collaborating across disciplines or working through a hard problem independently, I bring the same commitment: to do careful, impactful work that translates complex mathematics into actionable insights — and to keep growing with every challenge I take on.
Education
Masters in AI for Science
Masters in Fundamental Mathematics
Dynamics of the Mapping Class Group on Character Varieties - Finite and Bounded Orbits
Bachelor of Science in Mathematics
Modelling prey-predator with intraspecific competition of predator and prey refuge
Research Interests
- Deep Learning for Scientific Discovery
- Graph Neural Networks
- Interpretable AI
- Explainable AI
- Machine Learning Operations (MLOps)
- Reinforcement Learning
- Natural Language Processing for Scientific Texts
- Computer Vision in Scientific Imaging
- Applied Machine Learning at scale