Vincess Dongmo

Machine Learning researcher with interdisciplinary training in Physics and Artificial Intelligence, focusing on scientific AI systems for biomedical imaging, biosensing, and large-scale spatiotemporal modeling. Experienced in deploying production-grade ML systems and developing generative models for medical imaging and Earth system data.

Current research centers on generating synthetic T1CE MRI images from non-contrast MRI sequences using diffusion models and flow matching, comparing 2D slice-based and 3D volume-based architectures (Fatima Institute for Global AI Research, 2026).

Research Interests

Physics-Informed Machine Learning (SciML)
Generative Models for Medical Imaging (Diffusion Models, Flow Matching)
Graph Neural Networks for Industrial & Biomedical Applications
Computer Vision & Deep Learning
Computational Neuroscience & Brain-Inspired AI
Quantum Computing & Quantum Machine Learning
Edge AI & Resource-Constrained Intelligent Systems
Uncertainty Quantification in Deep Learning

Education

M.Sc. in Computer Engineering, Artificial Intelligence & Machine Learning
National Advanced School of Engineering of Yaoundé (ENSPY), GPA: 3.0/4.0, High Distinction
Thesis: “Design and Implementation of a Computer Vision Solution for Security and Surveillance in a Smart City”

Master 1 in Physical Sciences (M2 in progress)
University of Dschang

B.Sc. in Physics
University of Dschang, First Class Honours
Specialisation: Computational Physics & Numerical Simulation
Capstone: “Monte Carlo Simulations for Particle Physics Applications using C++ and Python”

Vincess Dongmo

Research Interests

Education

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