Patricio Vela was born in Mexico City, Mexico and grew up in California. He earned his bachelor of science degree in 1998 and his doctorate in 2003 at the California Institute of Technology, where he did his graduate research on geometric nonlinear control androbotics. Dr. Vela came to Georgia Tech as a post-doctoral researcher in computer vision and joined the ECE faculty in 2005.
His research interests lie in the geometric perspectives to control theory and computer vision. Recently, he has been interested in the role that computer vision can play for achieving control-theoretic objectives of (semi-)autonomous systems. His research also covers control of nonlinear systems, typically robotic systems.
- Ph.D., California Institute of Technology, 2003
- B.S., California Institute of Technology, 1998
Professor Vela’s research centers on statistical signal processing, machine learning, and data science with applications to communication systems and sensor networks. His work involves developing algorithms for learning, detection, and estimation in complex data environments, often under uncertainty. This includes advancing theoretical methods and their application to engineering problems, with a focus on improving system performance and decision-making in real-world scenarios. Graduate and undergraduate student involvement is encouraged.
Professor Vela’s teaching interests focus on foundational and advanced electrical and computer engineering topics at both undergraduate and graduate levels. This includes courses in signal processing, machine learning, communications, and data science. His instruction emphasizes practical applications and theoretical frameworks, aiming to develop student proficiency in analyzing and designing complex engineering systems. Professor Vela is committed to integrating research insights into his teaching to enrich student learning experiences.
- HENAAC Most Promising Engineer 2005
- L. Ticozzi, P.A. Vela, P. Tsiotras, Modeling and Control of a Hybrid Soft–Rigid Space Manipulator System, J. Guidance, Control, and Dynamics 48(10), 2162–2179, 2025.
- Y. Lin et al., OmniPose6D…, IROS 2025.
- Y. Du, S. Feng, C.G. Cort, P.A. Vela, Task‑driven SLAM Benchmarking…, IROS 2025.
- J. Cui et al., Multi‑Task Transfer Learning…, IEEE Transactions on Artificial Intelligence, 2025.
- Z. Chang, H. Yu, P. Vela, Y. Chen, Efficient iterative proximal variational inference motion planning, Robotics and Autonomous Systems, 2025.