Summary
Danish Nazir is a PhD candidate and Senior Data Scientist with nine years of experience building applied AI systems in computer vision, deep learning, and neural image/video compression. Currently researching at Volkswagen under Jan Piewek and Thorsten Bagdonat, he bridges academic rigor with industry-scale engineering to advance perception and depth-completion methods. His work at DFKI produced state-of-the-art LiDAR depth completion (SemAttNet) and led cross-platform, active-learning object-detection applications, demonstrating strength in multi-modal fusion and attention mechanisms. Prior startup experience as CTO and project lead shows he can productize research—deploying models on AWS/GCP and integrating hardware-software pipelines for smart surveillance. Comfortable teaching and mentoring, he has supported graduate-level deep learning courses while publishing in peer-reviewed venues. Based in Wolfsburg, Germany, he combines practical system delivery with cutting-edge research in neural compression and visual perception.
9 years of coding experience
4 years of employment as a software developer
HSSC, Pre Engineering, HSSC, Pre Engineering at IMPC H-8
BSCS, Computer Science, BSCS, Computer Science at COMSATS Institute of Information and Technology
Masters in Computer Science, Artificial Intelligence, 1.7, Masters in Computer Science, Artificial Intelligence, 1.7 at University of Kaiserslautern
PhD, Computer Vision, PhD, Computer Vision at Technische Universität Braunschweig
English, Urdu, German