AI Research Engineer
As a versatile Data Scientist, I bring extensive experience across diverse domains such as Earth Observation (Airbus Defence and Space), Spacecraft Rendezvous (IRT Saint Exupéry - RAPTOR), Neural Network Compression and Deployment (IRT Saint Exupéry - Confiance.ai), Anomaly Detection (Hexaglobe) and Natural Language Processing (IPSO Santé).
Skilled in identifying and implementing cutting-edge methodologies to address complex research questions.
Experienced in data preprocessing, cleaning, and transformation to ensure data quality. Proficient in developing predictive models with expertise in computer vision using Deep Learning techniques for space applications.
Proficient in programming languages such as Python and C++ working with a wide range of frameworks and tools like PyTorch, Tensorflow/Keras, Scikit-Learn, Pandas, OpenCV or Plotly. Experienced in containerization technologies (Docker) and cloud platforms (GCP, AWS and Azure) for scalable and reliable deployment.
Experienced in neural network optimization (quantization, pruning, etc.) and deployment on hardware targets (Nvidia GPUs and Xilinx FPGAs).
FPV drone racing (where FPV stands for first-person view) is a sport type where participants control drones, equipped with cameras while wearing googles showing the live stream camera feed from the drones.
The aim of this work is to propose an efficient implementation to perform a binary classification task from annotated X-ray images.
This work implement the architecture proposed in the paper "An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare, Journal of Healthcare Engineering, 2019, Okeke Stephen et al"