- New preprint: Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planning. We provide a new integration of discrete optimization and diffusion models for multi-robot motion planning scaling to 100 of agents in cluttered enviroments. :sparkles:

- New preprint: Stability-Constrained AC Optimal Power Flow–A Gaussian Process-Based Approach. We propose a new, scalable approach to incorporate generator dynamics into the ACOPF using Gaussian Process (GP) models. Crucially, this enables probabilistic stability assessment to be integrated directly into the optimization process!

- NSF-RI proposal on Generative Models for Scientific Exploration funded! We’ll integrate optimization and physical principles dirctly in generative models for a variety of scientific applications! Thank you, NSF!

- Paper Privacy-Preserving Convex Optimization: When Differential Privacy Meets Stochastic Programming accepted at CDC-2025! :sparkles:

- New preprint: SoK: Data Minimization in Machine Learning. We provide introduces a comprehensive framework for data minimization in machine learning and introduce a systematical review the literature on data minimization and adjacent methodologies. :sparkles: