Update: I will be joining the KTH Royal Institute of Technology in Stockholm as an associate professor (tenured).
I am currently the head of an independent research group at the Weierstrass Institute for Applied Analysis and Stochastics, Berlin. Previously, I worked as a postdoctoral researcher in machine learning at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. My Ph.D. study was in optimization and numerical analysis, at the University of Florida. See here for a short bio. I also write a non-research blog here. However, the update frequency depends on how busy I am at the moment.
Overall, I am interested in computational algorithms and dynamical systems. My group focuses on the mathematical foundations of machine learning and optimization. In recent years, I have been interested in ML/OPT applications rooted in the principled theory of optimal transport, gradient flows, and kernel methods.
Specifically, I started my research career in optimization and subsequently became interested in robust probabilistic machine learning and kernel methods. That requires us to use computational optimization tools that can manipulate probability distributions, which are inherently infinite-dimensional. It led me to my current interests in variational methods for machine learning and optimization over probability distributions, rooted in the theory of gradient flows and optimal transport.
For example, in some of my previous works, I invented robust probabilistic ML algorithms that can protect against distribution shifts using principled kernel methods. Those optimization algorithms have deep theoretical roots such as the analysis of PDEs. Following that and after moving to Berlin, I dedicate my current research to interfacing large-scale computational algorithms in machine learning/optimization using PDE gradient flows and optimal transport. Recently, I became interested in the Hellinger geometry (a.k.a. Fisher-Rao) and collaborated with Alexander Mielke on kernel methods and (Wasserstein-)Fisher-Rao, a.k.a. (spherical-)Hellinger-Kantorovich, gradient flows.
To get in touch, click the icon at the bottom of the page. There are sometimes delays in my response to emails, please be patient.
Upcoming events
- 28 July - 1 August 2025: ICSP 2025 invited session on interfacing optimal transport, gradient flows, diffusion models, and machine learning, optimization, at École des Ponts, IP Paris. Organized by Pavel Dvurechensky (WIAS Berlin), Jia-Jie Zhu (WIAS Berlin)
- 24 May 2026 - 29 May 2026: SwissMAP Workshop on “Computational Optimization Meets Gradient Flows and Optimal Transport”. Organized by Niao He (ETH Zurich), Yifan Hu (EPFL), Daniel Kuhn (EPFL), Jia-Jie Zhu (WIAS Berlin)
News and updates
- New publication at Journal of Optimization Theory and Applications (JOTA): An Inexact Halpern Iteration with Application to Distributionally Robust Optimization. Joint work with Ling Liang and Kim-Chuan Toh.
- New preprints:
- Evolution of Gaussians in the Hellinger-Kantorovich-Boltzmann gradient flow. Joint work with Matthias Liero, Alexander Mielke, Oliver Tse.
- Hellinger-Kantorovich Gradient Flows: Global Exponential Decay of Entropy Functionals. Joint work with Alexander Mielke.
- Recent talks:
- The Institute of Statistical Mathematics, Tokyo, Japan
- RIKEN AIP, ABI Team Seminar, Tokyo, Japan
- University of British Columbia, The Kantorovich Initiative Seminar: Kernel Approximation of Wasserstein and Fisher-Rao Gradient flows
- Slides for the EPFL talk, Nov 2024
- Kernel Approximation of Wasserstein and Fisher-Rao Gradient flows by Prof. Jia-Jie (JJ) Zhu (WIAS Berlin) - EPFL
- From distributional ambiguity to gradient flows: Wasserstein, Fisher-Rao, and kernel approximation - EPFL
- New preprints:
- New work to appear in NeurIPS 2024: Globally convergent gradient flows for the MMD-minimization inference problem (a.k.a. MMD-flow). Preprint: Egor Gladin, Pavel Dvurechensky, Alexander Mielke, Jia-Jie Zhu. Interaction-Force Transport Gradient Flows Code: link, Slides (NeurIPS 2024)
- I’m serving as an area chair for AISTATS 2025. If you are interested in contributing to the community via reviewing a paper, please get in touch.
- Summer 2024. New third-party funding awarded: DFG Project on “Optimal Transport and Measure Optimization Foundation for Robust and Causal Machine Learning” within the Priority Program “Theoretical Foundations of Deep Learning” (SPP 2298).
- March 11th - 15th, 2024. I am organizing a Workshop on Optimal Transport from Theory to Applications – Interfacing Dynamical Systems, Optimization, and Machine Learning (OT-DOM) in Berlin, Germany. program and slides
- New preprints available:
- Approximation, Kernelization, and Entropy-Dissipation of Gradient Flows: from Wasserstein to Fisher-Rao. Joint work with Alexander Mielke.
- Analysis of Kernel Mirror Prox for Measure Optimization. Accepted for publication at AISTATS 2024. Joint work with Pavel Dvurechensky.
- I am teaching the nonparametric statistics course at Humboldt University of Berlin (at master level), co-lecturing with Vladimir Spokoinyi, in term 2023/24.
- Recent talks on gradient flow force-balance, especially in robust learning under (strong) structured distribution shifts, and conditional moment restriction for causal inference at
- EUCCO 2023, at Heidelberg University. Talk slides available
- I taught a mini-course on the optimization perspective of gradient flow dynamics, introducing (beginner-friendly) concepts of gradient flows in the Eulidean and Wasserstein space, at the Workshop of Intelligent Autonomous Learning Systems 2023. Lecture slides available:
- July 2023. I gave an invited talk at the ICML 2023 Workshop on Duality Principles for Modern Machine Learning. The slides are available here.
- July 2023. Accepted paper at CDC 2023: Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics Models
- May 2023. Accepted paper at ICML 2023 (link to preprint): Heiner Kremer, Yassine Nemmour, Bernhard Sch ̈olkopf, and Jia-Jie Zhu. Estimation Beyond Data Reweighting: Kernel Method of Moments.
- May 2023. A couple of new preprints are available:
- Apr 2023. Gave a plenary talk at the Leibniz Institute for Agricultural Engineering and Bioeconomy Potsdam, during the workshop “Mathematical Modeling and Simulation” (MMS) Days.
- Served as area chair for AISTATS 2023.
Open positions
- [Postdoc and PhD positions available] There are upcoming openings in my group. If you are interested in joining my group, please feel free to inquire.
- Master thesis: if you are a master’s student interested in optimization, optimal transport, robust/probabilistic machine learning, generative models and dynamical systems (e.g., diffusion/flow models, neural ODE), please feel free to reach out.