Developing advanced perception warning systems, high-performance simulation integrations, and deep learning models for autonomous vehicle architectures and intelligent systems.
Download CVI am a Software Engineer with a solid foundation in Communication and Electronics Engineering and currently pursuing an M.Sc. in Software Engineering and Informatics. With over 4 years of industrial experience in Advanced Driver Assistance Systems (ADAS), my work bridges the gap between hardware, low-level firmware, simulation validation, and high-level AI/ML algorithms.
I have a passion for building high-performance systems and automating testing frameworks to improve software safety and reliability. I actively design solutions for autonomous robotics, computer vision, sensor fusion, and driver-in-the-loop validation tools.
Specialized in ADAS features, perception algorithms, and safety testing for international automotive OEMs.
Expert in developing QT interfaces, hardware-in-the-loop validation, and TCP/UDP communication tools.
Researching statistical analysis, convex optimization theory, and deep learning architectures for M.Sc.
Specialized in Advanced Driver Assistance Systems (ADAS), overseeing key functionalities including ACC, AEB, LKA, FuSa, CW/CA, LDW, and ESS. Collaborated directly with major automotive OEMs (Stellantis, Renault, Honda, Geely, VinFast, etc.).
Gained intensive training in artificial intelligence foundations, focusing on predictive modeling, machine learning algorithms, and IBM Watson cognitive services.
Guided participants in solving space science challenges. Assisted with technical issues, data API queries, and hardware/software architectures for local hacking teams.
Led a student branch of 100+ members. Supervised technical workshops, community outreach programs, and organized national-level engineering competitions.
Focusing on advanced computing architectures, mathematical optimization, and deep neural network designs to model complex software systems.
Acquired strong competencies in communications theory, signals processing, electronics engineering, and neural network foundations.
Graduation Project: Pharmacy Smart System. Designed and implemented a deep learning-based computer vision application to detect and read handwritten medical prescriptions. (Project GPA: 4.00 / 4.00 (A))
Graduation project scoring 4.00/4.00. Features an end-to-end deep learning pipeline to segment, locate, and read cursive handwriting on medical prescriptions to reduce medication dispensing errors.
Developed a real-time driver-in-loop feedback validation system. Deals with DualSense PS5 controller input signals (haptics, adaptive triggers, and lights) to simulate realistic motorbike behaviors in a virtual ADAS testing environment.
Linked a customized QT-based simulation system with IPG CarMaker over a high-speed socket bridge, feeding real-time dynamic lateral/ground truth parameters (banked road angles, pitch/roll/yaw rates) for ADAS algorithms validation.
Engineered a custom C++ serialization/deserialization library optimized for transmission of high-size data structures over UDP communication sockets. Achieved near-zero copy serialization to prevent network latency in real-time simulators.
An embedded system project utilising ATmega32/Arduino to encode input text into light/sound patterns and decode incoming audio Morse signals using frequency detection algorithms.
Computer vision scripts designed for Remote Operated Vehicles (ROVs). Processes raw underwater video feeds to execute color correction, edge detection for structural integrity analysis, and object size estimates.
Feel free to reach out for professional opportunities, research discussions, or collaborations.