My main interests have been in the intersection of three technical fields: optimization, dynamical systems and control, and machine learning, underpinned by principled applied math. One of my recent focuses is on data-driven robust optimization and control using tools such as functional analysis, principled learning, and kernel methods. I believe mathematical optimization, e.g., modern stochastic and nonlinear programming, could play an interesting role in many cutting-edge applications such as large-scale robust machine learning and data-driven robust control.

### Papers

**Kernel Distributionally Robust Optimization**. Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf, 2020. To appear in the Proceedings of the 24th International Conference on Artificial Intelligence and Statistics (AISTATS). paper code

**Worst-Case Risk Quantification under Distributional Ambiguity using Kernel Mean Embedding in Moment Problem**. Jia-Jie Zhu, Wittawat Jitkrittum, Moritz Diehl, Bernhard Schölkopf, 2020. To appear in the 59th IEEE Conference on Decision and Control (CDC)), 2020. paper slides

**Projection Algorithms for Non-Convex Minimization with Application to Sparse Principal Component Analysis.** J.J. Zhu, D. Phan, W. Hager, 2015. Journal of Global Optimization, 65(4):657–676, 2016.
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**A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control**. Zhu, Jia-Jie, Moritz Diehl and Bernhard Schölkopf. 2nd Annual Conference on Learning for Dynamics and Control (L4DC). In Proceedings of Machine Learning Research vol 120:1–9, 2020.
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**A New Distribution-Free Concept for Representing, Comparing, and Propagating Uncertainty in Dynamical Systems with Kernel Probabilistic Programming.** Zhu, Jia-Jie, Krikamol Muandet, Moritz Diehl, and Bernhard Schölkopf. 21st IFAC World Congress. To appear in IFAC-PapersOnLine proceedings, 2020.
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**Fast Non-Parametric Learning to Accelerate Mixed-Integer Programming for Online Hybrid Model Predictive Control**. Zhu, Jia-Jie, and Martius, Georg. 21st IFAC World Congress. To appear in IFAC-PapersOnLine proceedings, 2020.
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**Robust Humanoid Locomotion Using Trajectory Optimization and Sample-Efficient Learning.** Yeganegi, Mohammad Hasan, Majid Khadiv, S Ali A Moosavian, Jia-Jie Zhu, Andrea Del Prete, and Ludovic Righetti. IEEE Humanoids, 2019.
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**Generative Adversarial Active Learning.** J.J. Zhu, J. Bento, 2017. NIPS 2017 Workshop on Teaching Machines, Robots, and Humans.
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**Control What You Can: Intrinsically Motivated Task-Planning Agent.** Blaes, Sebastian, Marin Vlastelica Pogančić, JJ Zhu, and Georg Martius. In Advances in Neural Information Processing Systems (NeurIPS) 32, pages 12541– 12552. Curran Associates, Inc., 2019.
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**Deep Reinforcement Learning for Resource-Aware Control.** D. Baumann, J.J. Zhu, G. Martius, S. Trimpe, 2018. IEEE CDC 2018.
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**A Metric for Sets of Trajectories that is Practical and Mathematically Consistent.** J. Bento, J.J. Zhu, 2016.
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**A Decentralized Multi-Block Algorithm for Demand-Side Primary Frequency Control Using Local Frequency Measurements.** J. Brooks, W. Hager, J.J. Zhu, 2015.
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