Hi, this is JJ (朱家傑). I studied mathematics for my undergraduate degree at Fudan University in Shanghai. Following that, I received doctoral training in numerical analysis and optimization during my Ph.D. study guided by William Hager at the University of Florida. After moving to Europe and being awarded the Marie Curie Individual Fellowship, I learned numerical methods for robust nonlinear optimization and control from Moritz Diehl during a research stay at the University of Freiburg. Recently, I was a postdoctoral researcher in machine learning at the Empirical Inference Department, Max Planck Institute for Intelligent Systems in Tübingen, Germany, where I worked on the theory of kernel methods, distributionally robust optimization, and robust machine learning (group of Bernhard Schölkopf). There, I invented the Kernel DRO, which is a kernel-MMD-constrained optimization algorithm for robust machine learning, inspired by the Wasserstein gradient flow. Recently and since moving to Berlin, I have been very interested in gradient flow in dynamical systems and optimal transport. In my spare time, I enjoy reading science fiction and hiking.