Zhiwei Han is a machine learning researcher and PhD candidate at Technical University of Munich with a decade of experience applying theoretical and practical ML to industrial problems from recommendation to predictive maintenance. Currently a research assistant at fortiss GmbH, he focuses on the geometric structure of causal models, graph representations, object-centric learning, and ICA, bridging rigorous theory with deployable solutions. His work includes developing TD-type algorithms for recommendation (NMF-DQN) that improved cold-start performance and prototyping onboard predictive diagnostics for automotive applications. He combines strong academic training (Dr.-Ing. candidate, TUM; M.Sc. with distinction) with hands-on systems work in C++ and parallel computing, and a track record of moving research into industry projects. Colleagues describe him as someone who seeks deep structural understanding of models rather than only empirical gains, which informs both his theoretical analyses and practical implementations.
10 years of coding experience
Dr. -Ing, Electrical Engineering, Dr. -Ing, Electrical Engineering at Technical University of Munich
Master, Elektrotechnik und Elektronik, 1.4/1.0 (german note system, Graduation with distinction), Master, Elektrotechnik und Elektronik, 1.4/1.0 (german note system, Graduation with distinction) at Technische Universität München
Bachelor of Engineering (B.E.), Electrical Engneering and Automation, 3.8/5.0, Bachelor of Engineering (B.E.), Electrical Engneering and Automation, 3.8/5.0 at Zhejiang University of Technology
Contributions:147 commits, 104 pushes, 1 branch in 2 months
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