Ismail Ahl-Lamara

Explainable AI Modeling for the electronic interface of Quantum computers. Grade: 1.0

Project: Detection of Sensor-To-Sensor Variations using Interpretable and Explainable AI. Project: End-to-end ML pipelines for data collection, cleaning, visualization, model optimization, and deployment. Project: Compression-based Early Exiting for Efficient Machine Learning Inference.

Developed a highly scalable end-to-end AI software that includes a website, a dockerized micro-services architecture on the back-end orchestrated with Kubernetes, and deployed the software into large-scale production on Cloud environments. The software integrates the Machine Learning life cycle for the detection/classification of car damages for car insurance claims.

Project ’AI software development for 5G’: Machine Learning/Deep Learning software development for AI based 5G Channel Estimation and Data Synthesis solutions for coverage shortage scenarios.

Focus: Machine Learning/Deep Learning, MLOps, Computer Vision, Cloud Computing and Statistics. Final GPA: 1.8

Member of the President’s List. Final GPA: 1.86