Rajiv Sambharya

Educational Background

Ph.D., Princeton University, Operations Research and Financial Engineering
Postdoctoral Researcher, University of Pennsylvania Electrical and Systems Engineering

- Data-driven computational tools for decision-making
- Theory: optimization, machine learning, control theory.
- Methodology: machine learning for optimization, real-time optimization, optimization-based control, learning for control, statistical learning theory, computer-assisted optimization analysis.
- Applications: autonomous systems, signal processing, robotics, power systems, data science, operations research, finance.

R.Sambharya and B.Stellato, “Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization, "SIAM Journal on Mathematics of Data Science, vol.8, no.3, pp.649–676, 2026.
R. Sambharya and B. Stellato, "Data-Driven Performance Guarantees for Classical and Learned Optimizers," Journal of Machine Learning Research, vol. 26, no. 171, pp. 1–49, 2025.
R. Sambharya, G. Hall, B. Amos, and B. Stellato, "Learning to Warm-Start Fixed-Point Optimization Algorithms," Journal of Machine Learning Research, vol. 25, no. 166, pp. 1–46, 2024.
R. Sambharya, G. Hall, B. Amos, and B. Stellato, "End-to-End Learning to Warm-Start for Real-Time Quadratic Optimization," in Proceedings of The 5th Annual Learning for Dynamics and Control Conference, ser. Proceedings of Machine Learning Research, vol. 211, PMLR, 2023, pp. 220–234.
R. Sambharya, N. Matni, and G. Pappas, "Verification of Sequential Convex Programming for Parametric Non-convex Optimization," 2025.
R. Sambharya, J. Bok, N. Matni, and G. Pappas, "Learning Acceleration Algorithms for Fast Parametric Convex Optimization with Certified Robustness," 2025.