As a postdoc researcher, I led research in computer vision algorithms and neural network architectures for object detection, image processing, classification, and smart sensing within point-of-care (POC) devices. My goal is to seamlessly integrate these technologies to deliver comprehensive solutions that empower POC devices with sophisticated analytical capabilities, advancing the field of healthcare diagnostics. During my PhD studies within the Department of Naval, Electrical, Electronic, and Telecommunications Engineering (DITEN) at University of Genoa, I have focused on Intelligent Electronic Systems, tactile sensing systems, sensorized robotic grippers, E-skin, restoration of the sense of touch, and Electrotactile Stimulation. My research interests encompass Artificial Intelligence, Embedded Machine and Deep Learning, Computer Vision, Edge Computing, Object Detection, computer vision systems, and tactile sensing systems.
							                Title: PhD in Electronics and Telecommunications Engineering
							                Institute: University of Genova
							                Location: Genova
							                Country: Italy
							                From: 2019 To: 2022
						                
							                Title: Master of Science in Biomedical Engineering
							                Institute: Lebanese International University
							                Location: Beirut
							                Country: Lebanon
							                From: 2017 To: 2019
						                
							                Title: Bachelor of Science in Electronics Engineering/Emphasis  Biomedical Engineering
							                Institute: Lebanese International University
							                Location: Beirut
							                Country: Lebanon
							                From: 2014 To: 2017
						                
							                 Programming skills:
							                
										        
								                	- Python: TensorFlow, Keras, Scikit-learn, NumPy, Pandas, OpenCV, Matplotlib, Seaborn
								                
								                	- Deep Learning & Computer Vision: YOLOv4, YOLOv8, CNNs, ResNet, MobileNet
								                
								                	- Machine Learning:  Random Forest, SVM, k-NN, Decision Trees, Logistic Regression
								                
			              					
						                
							                 Embedded Systems & Edge AI
							                
										        
								                	- Languages: C, C++
								                
								                	- Microcontrollers: STM32 (Nucleo H745ZI-Q), ARM Cortex-M
								                
								                	- TinyML & Model Optimization: X-Cube-AI, Memory Caching, Quantization
								                
								                	- Low-Latency ML Deployment: Real-time inference, energy-efficient classification
								                
			              					
						                
							                 Databases & Networking: : MySQL, SQLite, Flask, C#
							                
						                
							                 Software tools: PyCharm, STM32 Cube IDE, MATLAB, LabView, Circuit Design (Proteus), Fusion 360, Pspice