Summary
Baran Hashemi is a postdoctoral scientist with a decade of experience at the intersection of AI, mathematics, and theoretical physics, currently working on AI for Mathematics and Science at the ORIGINS Cluster and Max Planck Institute for Mathematics in the Sciences. His PhD and prior research span ML for high-energy physics, geometric deep learning, deep generative models, and simulation-based inference, with a strong foundation in topological and quantum field theory from earlier work. He thrives on hard, high-dimensional problems—describing himself as an “ultramarathoner” for complexity—and builds tools that bridge rigorous mathematical structures and practical AI methods. Notably, his background includes contributions to understanding quantum curves and topological recursion as well as fast detector simulation, reflecting a rare blend of abstract theory and applied machine learning. Based in Leipzig, Germany, he brings a persistent curiosity and appetite for challenge to problems that require both deep theory and scalable computational solutions.
10 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Particle Phsyics, Doctor of Philosophy - PhD Particle Phsyics at Ludwig-Maximilians-Universität München
Bachelor's degree Elementary Particle Physics, Bachelor's degree Elementary Particle Physics at Shahid Beheshti University
High School Mathematical Science, High School Mathematical Science at National Organization for Development of Exceptional Talents (Sampad)
English, German, Persian