Kh Safkat Amin

Developed a Python based framework for harmonizing driving behavior data and enriching it with environmental data. Integrated MongoDB as a centralized database to store harmonized and enriched data. Developed automated enrichment pipeline developing scripts to incorporate contextual information such as road junction detection, maneuver classification, pairwise interaction labeling, and computation of safety-critical risk metrics. Designed efficient query mechanisms to enable scenario-based data retrieval.

Operated a tribological test bench to collect experimental data on wear and boundary layer formation. Developed and validated predictive models using classical and neural network-based techniques implemented in scikit-learn and TensorFlow to analyze complex tribological phenomena.

Upgraded perception from basic lane detection to a BEV-based solution using IPM and SegNet for semantic segmentation on Jetson AGX Xavier with ROS 2. Development of the vehicle controller with new perception modules for smooth navigation. Trained YOLO models for traffic sign detection using transfer learning and custom augmentation. Validated software components in simulation and real-world tests. Contributed to software setup, debugging, and documentation in an Agile team.

Worked at RAIL, a Bangladesh automotive assembly company specializing in CKD passenger and commercial vehicles. Supervised quality control and repair operations, ensuring regulatory compliance and performing diagnostics, root cause analysis, and FMEA. Led Material Management, optimizing logistics and digitalizing workflows with advanced Excel to enhance efficiency and traceability.