Summary
Kuan-chun L is a Machine Learning Engineer with over a decade of ML experience and 8+ years focused on deep learning research and applied R&D across academia and industry, including legal tech, pharmaceuticals, and semiconductors. He currently advances AI-assisted chip design automation, developing state-of-the-art algorithms for analog and digital IC tasks such as gradient-based placement optimization and graph-attention classification that materially cut runtime and improved accuracy. His background blends strong theoretical training (Computational Neuroscience, Applied Mathematics) with hands-on engineering—building parallelized data workflows, ETL for text/chemoinformatics, and production-ready PyTorch/Torch-Geometric pipelines. He’s contributed research in reinforcement and unsupervised learning at Max Planck, taught ML to domain scientists, and applies explainable methods (LIME/SHAP) in drug discovery—demonstrating an unusual fluency in translating advanced models into domain-impacting solutions. Outside work he pursues reading across science and humanities, music, gaming, and language learning, reflecting a curious, cross-disciplinary mindset that informs his work.
10 years of coding experience
4 years of employment as a software developer
Bachelor of Science (B.S.), Psychology, Bachelor of Science (B.S.), Psychology at National Chengchi University
Master’s Degree, Computational Neuroscience, Machine Learning, Master’s Degree, Computational Neuroscience, Machine Learning at Eberhard Karls Universität Tübingen
Master of Science (M.Sc.), Applied Mathematics, Master of Science (M.Sc.), Applied Mathematics at National Tsing Hua University
English, Chinese, Mandarin, German, Japanese