Industrial Ph.D. Student In Deep Learning
"Constrained deep learning for MEMS sensors-based applications"CIFRE contract with Inria Grenoble Alpes• Demonstrated ability to collaborate and innovate within international R&D teams through patents, and papers in international conferences• Delivered two prototypes with live demos for audio, and gesture recognition running a tiny neural network• Researched a novel algorithm for flexible parameter precisions down to 1-bit, reducing model size by 50% with acceptable loss• Created and developed an industry-standard software to train and deploy neural networks on the most constrained hardware (< 8KB)Keywords: Neural Networks, TinyML, Model Compression (Pruning, Quantization, Knowledge Distillation, ...), Edge Inference, Sensors, R&D, Innovation, Prototyping