Farid Bounini Email & Phone Number

Montreal

AI Innovation for Warehouse Efficiency: Led the development of cutting-edge machine learning algorithms and computer vision to optimize product handling and warehouse operations. 1- Case Detection & Pose Estimation: Designed, implemented, and deployed AI models for multi-cases (cardboards) pose estimation, enabling automated depalletization and enhancing warehouse efficiency through improved accuracy and reduced manual labor. 2- Multi-Face Case Pose Estimation: Developed and trained machine learning models to accurately identify Stock Keeping Units (SKU) IDs, ensuring seamless re-identification of cases in the warehouse with high precision. 3- Improved Product Handling: Successfully implemented and deployed innovative AI solutions to automate product handling, reducing manual labor, increasing productivity, and enhancing overall warehouse efficiency

Canada

Real-time Hazmats Detection and Tracking: Contributed to the development of cutting-edge real-time hazmat detection systems in industrial and warehouse environments. 1- Designed and implemented computer vision systems to detect and classify hazardous materials, significantly improving safety standards within industrial settings. 2- Led dataset collection and annotation efforts, ensuring accurate data for machine learning model training. 3- Successfully developed and deployed detection algorithms for directional arrows, enhancing product navigation on conveyors and logistics operations within warehouses.

Quebec, Canada

Directed the development of innovative plug-ins for Video Management Systems (VMS) integrating computer vision and artificial intelligence capabilities. 1- Designed and developed cutting-edge solutions using Yolo v4, Center Net, Siamese network, Face detection & recognition, Objects color detection with CNN classifiers, Licence Plate Detection & Recognition (LPR), Generative Adversarial Network (GAN) for fashion/cloths generation, and Yolov3 on Nvidia Jetson toolkit. 2- Successfully implemented features for: - Multi-object detection and tracking in crowded environments - Textile fabric type and color detection & tracking - Animal detection, tracking, and counting - Vehicle detection, tracking, and counting

Montreal, Canada Area

Spearheaded the design, development, and integration of advanced Intelligent and Connected Vehicles (ICVs) for Test and Validation of Advanced Driving Assistance Systems (ADAS): 1- Systems Design & Implementation: Developed and implemented cutting-edge ICV systems, ensuring seamless integration with ADAS test and validation scenarios. 2- Algorithm Development: Designed and implemented sophisticated data processing algorithms to optimize vehicle performance and accuracy in augmented reality driving scenarios. 2- Software Validation: Successfully validated Software in the Loop (SIL) systems across various environmental conditions, resulting in improved ADAS test efficiency and reliability.

Montreal, Canada Area

AI Platform Development: Spearheaded the design, development, and integration of software and hardware architectures for AI platforms dedicated to testing and validation of autonomous and connected vehicles. 1- Collaborated with OPAL-RT Technologies and Sherbrooke University (LIV: Laboratory on Intelligent Vehicles) to develop innovative solutions and drive innovation in intelligent transportation systems. 2- Gained expertise in designing and implementing cutting-edge architectures, contributing to the advancement of AI technologies in the automotive industry.

Montreal, Canada Area

Developing cutting-edge AI platforms for autonomous and connected vehicles, while driving innovation in code optimization and hardware adaptation. With expertise in real-time object detection, classification, and ADAS validation/homologation methods, I deliver innovative solutions for the next generation of transportation. 1- AI Platforms for Autonomous & Connected Vehicles: Designed and developed cutting-edge software and hardware architectures of AI platforms dedicated to testing and validation of autonomous and connected vehicles, in partnership with OPAL-RT Technologies and Sherbrooke University (LIV: Laboratory on Intelligent Vehicles). 2- Expertise in Code Optimization & Hardware Adaptation: Authored a research paper on code optimization and hardware adaptation for real-time and accelerated simulation to achieve autonomous driving, presented at the DSC 2019 Europe VR conference. 3- Hands-on Experience with AI Technologies: Successfully implemented real-time object detection and classification using Convolutional Neural Networks (CNNs) with Jetson TX2 toolkit based on Yolo2, showcased at McGill Code Jam. 4- Industry-Leading Expertise in ADAS & Automated Vehicles: Presented advanced driver assistance systems (ADAS) and validation/homologation methods for automated vehicles at the AQTr Automated Vehicles Forum.

Sherbrooke, Canada

My doctoral research focused on developing a comprehensive real-time simulator for intelligent, autonomous, and connected vehicles, leveraging vehicle dynamic simulations from Pro-SiVIC and OPAL-RT products. This enabled efficient modeling of complex scenarios.The main objectives were to build and validate ADAS control algorithms and autonomous driving strategies using the real-time simulator. I developed novel AI-driven ADAS solutions, including: 1- Advanced Driver Assistance Systems (ADAS) modeling and validation 2- Online trajectory planning for autonomous vehicles using modified potential fields 3- Real-time cooperative localization techniques, such as SLAM and collaborative SLAMThe real-time simulator was integrated with software frameworks like RT-LAB, ROS, and Simulink to facilitate data exchange. This integration allowed for efficient testing and validation of intelligent vehicle systems.The simulator was utilized in research projects, including: 1- Developing AI-driven ADAS control strategies 2- Validating online trajectory planning algorithms 3- Investigating the impact of connected vehicle technologies on traffic flow and safetyThe project outcomes include: 1- A comprehensive real-time simulator that can be used to validate various intelligent transportation systems (ITS) technologies 2- Novel AI-driven ADAS control solutions that have been validated through simulations 3- Improved understanding of the challenges and opportunities associated with autonomous driving and connected vehiclesThis research has been recognized through publications in top-tier conferences and journals, including: 1- IEEE Intelligent Transportation Systems Magazine 2- OPAL-RT International Conference on Real-Time Simulation 3- IEEE Intelligent Vehicles SymposiumThroughout this research, I've maintained collaboration with academia and industry partners, contributing to the advancement of intelligent transportation systems.

Evry Val D'Essonne, France Area

Master of Science (M.Sc.) in Smart Aerospace and Autonomous SystemsResearch Focus: 1- Traffic Estimation and Control: Developed advanced algorithms and models using Fuzzy Logic to estimate and control road traffic flow, ensuring efficient traffic management. 2- Robot Trajectory Planning in Overcrowded Environments: Designed innovative approaches to robot trajectory planning, utilizing Potential Fields to navigate crowded spaces safely and efficiently.Academic Achievement: Recipient of the prestigious Master of Excellence Scholarship, awarded by Évry Val-d’Essonne University, recognizing exceptional academic achievement and research potential.

Doctor Of Philosophy - Phd, Intelligent/Autonomous And Connected Vehicles

Master'S Degree, Smart Aerospace And Autonomous Systems, Master

Master'S Degree, Smart Aerospace And Autonomous Systems (Saas)

Activities and Societies: .......