Zekun Song

Zekun Song is a Doctoral Researcher and PhD student at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at the Technical University of Berlin since 2024, engaged in the collaborative project 'Machine Learning-Assisted Genealogies' with the Max Planck Institute for the History of Science.

His academic background includes:

• PhD in Machine Learning, 2024 - present, Technical University of Berlin
• M.Sc. in Computer Science, 2021 - 2024, Leibniz University Hanover

His research spans Artificial Intelligence and Machine Learning with concentrated expertise in Reinforcement Learning, Generative Models, and Large Language Models, applied to interdisciplinary challenges at the intersection of computer science and historical research. Current work focuses on developing machine learning frameworks for historical genealogical analysis through the BIFOLD-Max Planck collaboration.

No scientific awards were documented in the source material.

As an early-career researcher, Song does not supervise students. His primary research support comes from the 'Machine Learning-Assisted Genealogies' project, which integrates resources from both BIFOLD and the Max Planck Institute for the History of Science.

He operates within BIFOLD's research ecosystem while maintaining active collaboration with the Max Planck Institute for the History of Science, forming a cross-institutional team that merges computational methodologies with historical scholarship to pioneer new approaches in digital humanities.