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Bartolomeo Stellato is an Assistant Professor in Operations Research and Financial Engineering at Princeton University. His research focuses on data-driven computational tools, mathematical optimization, machine learning, optimal control, and real-time embedded optimization. He applies techniques from dynamical systems and optimization-based control to develop scalable algorithms for complex systems. Stellato has a keen interest in differentiable optimization and first-order methods which facilitate large-scale optimization tasks. His work spans various applications, including control of fast dynamical systems, finance, robotics, and autonomous systems. Stellato aims to bridge theoretical research in optimization with practical implementations that enhance the performance of dynamic systems, utilizing machine learning alongside traditional optimization techniques. He actively contributes to the academic community through his research and teaching endeavors, fostering the next generation of scholars in the field.
GRE scores are not accepted. Ph.D. is the primary degree; students are not required to hold an M.S.E. prior to admission.