I work at the intersection of optimization and learning for large-scale and streaming data, with a number of application domains as smart grids, intelligent transportation, personalized health, and quantum computing.

Things that I am currently working on

• Online algorithms for time-varying optimization problems (prediction-correction, correction-only, feedback-based).
• Personalized optimization with Gaussian Processes.
• Online algorithms for dynamic ridesharing and shared mobility.
• Optimization algorithms on quantum computers.
• Applications: smart grids, intelligent transportation, personalized health, quantum computing.

Things that I have worked on in the past

• Convex and non-convex optimization: both from a theoretical perspective and algorithmic implementation. Recent works include prediction-correction algorithms to track the solution trajectory of time-varying convex programs. Older works include networked SDP convex relaxations of certain classes of non-convex programs.
• Distributed optimization, especially in connection with robotic and wireless sensor networks.
• Control theory in the context of distributed control design, model predictive control, embedded control systems.
• Signal processing: distributed estimation, sparse reconstructions, compressive sensing, wireless sensor network localization, sensor selection, graph signal processing.
• Parallel computing in the context of nonlinear estimation and specifically particle filtering.