Applying machine learning and mathematical models to predict future financial returns.
Machine Learning Research Assistant
University of Cambridge
Investigated uncertainty quantification methods in graph neural networks in the CBL lab, under the supervision of Prof. Jose Miguel Hernandez-Lobato and Prof. Pietro Liò.
Data Scientist
Sopra Steria
Led ETL transformations in Python, implemented machine learning algorithms, and deployed deep learning architectures for production systems.
Research Intern
University of Bristol
Collaborated with Dr. Kacper Sokol on explainability in machine learning algorithms, including LIME and ANCHORS.
Education
PhD in Advance Machine Learning
University of Cambridge
Researching scalable probabilistic deep learning models to quantify uncertainty under the supervision of Prof. Jose Miguel Hernandez-Lobato and Prof. Pietro Liò.
MPhil in Machine Learning and Machine Intelligence
University of Cambridge
Achieved a 74% overall grade with an 84% in dissertation on Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations. Supervised by Prof. Jose Miguel Hernandez-Lobato and Prof. Pietro Liò.
BEng in Engineering Mathematics
University of Bristol
First-class honours with a dissertation grade of 81%, the highest in the cohort, focused on using machine learning to predict movement patterns.
Completed an intermediate course focusing on probabilistic models, TensorFlow 2, and the application of deep learning techniques in uncertainty quantification.
Gained skills in building and applying machine learning models for regression and classification, with a focus on practical implementation and evaluation.