Prof. Bewley directs the UCSD Flow Control & Coordinated Robotics Labs. The Flow Control Lab investigates a range of questions ranging from fundamental to applied, including the development of new analysis tools and numerical methods to better understand, optimize, estimate, forecast, and control fluid systems. The Coordinated Robotics Lab investigates the mobility and coordination of low-cost robotic vehicles and multi-vehicle systems, leveraging advanced dynamic models and feedback control algorithms as well as tensegrity design principles, with prototypes built using cellphone and IoT technologies and custom PCBs,. The two labs collaborate closely on a variety of interdisciplinary projects, including the deployment of robotic sensor vehicle swarms for both real-time hurricane measurement as well as real-time contaminant plume detection, estimation, & forecasting.
Dr. Birmead's research focuses on the theoretical end of control systems together with their application in industrial settings. His expertise is in modeling for control, system identification, and state estimation. He has specific skills in stochastic systems and the interface between control and communications. He has worked with the following industry sectors: aerospace, energy, mining, building systems, sugar, steel, petroleum, mobile communications. He has won a number of awards and recognition for his research. He was President of the IEEE Control Systems Society in 2019.
Dr. Cortes' research seeks to unveil the science and engineering that explains and enhances the operation of network systems. The ultimate aim is to understand the mechanisms that make complex networks function they way they do, and to use this knowledge to develop systematic methods to design better networks. His research is interdisciplinary and draws connections between solid theoretical foundations, development of computational methods, and applications in control, robotics, environmental sampling, neuroscience, power systems, and intelligent transportation. He holds a Ph.D. in engineering mathematics from Universidad Carlos III de Madrid, is a Fellow of IEEE and SIAM, and is a recipient of several awards and paper prizes. He is the author of Geometric, Control and Numerical Aspects of Nonholonomic Systems (Springer-Verlag, 2002) and co-author of Distributed Control of Robotic Networks (Princeton University Press, 2009).
DE CALLAFON, RAYMOND
Raymond de Callafon a full professor with the Department of Mechanical and Aerospace Engineering (MAE) at the University of California, San Diego (UCSD). In his role as a professor he is involved in teaching, research, software development and judicial expertise that cover aspects in signal processing, parameter/state estimation, servo/adaptive control and embedded software development. Prof de Callafon is interested in designing and analyzing data-based modeling and control techniques for complex dynamic systems that are not easily captured by physics or first principle based modeling, either due to the complexity or uncertainty of the underlying system. As a result, Prof. de Callafon develops fundamental techniques for data-based estimation, prediction and adaptive control of dynamic systems. The work of Prof. de Callafon covers a wide range of applications that benefit from these fundamental techniques and include structural damage detection problems, adaptive feedback tuning in precision mechanical systems with noise/vibration control, data assimilation in wildfire modeling and dynamic modeling and control of electric power systems. He directs the System Identification and Control Laboratory at the Dept. of MAE and the Synchrophasor Grid Monitoring and Automation (SyGMA) laboratory at the San Diego Supercomputer Center (SDSC) at UCSD for research related to synchrophasor data. He is also a member of Workflow for Data Science (WorDS) Center of Excellence for collaborations on wildfire data assimilation via scientific workflows. His work has been recognized by the North American Synchrophasor Initiative (NASPI) EATT Most Valuable Player Award and HPCwire Reader’s Choice Award for "Best Application of Big Data in High Performance Computing".
Specialties: Dynamic System Modeling from experimental data (System Identification); Control System Design; Adaptive regulation for noise, vibration and motion control applications; Real-time embedded software development.
DE OLIVEIRA, MAURICIO
Teaching and conducting research in the area of Optimization, Dynamic Systems and Control. I concluded my Ph.D. in 1999, M. S. in 1996 and B. S. in 1995, all in Electrical Engineering from the University of Campinas, Brazil. Prior to joining UCSD I was an Assistant Professor at the School of Electrical and Computer Engineering at the University of Campinas, SP, Brazil from 2001 to 2003. From 2005 to 2006 I was the Chief Research Engineer at Dynamic Systems Research, Inc., San Diego, CA, where I led a large team on the development of an energy harvesting station keeping sea drogue. Since 2008, I have worked as a consultant to one of the worlds' largest sovereign wealth funds in the area of portfolio optimization. I have also worked as a consultant for Cymer ‐ an ASML company, and in research and development with Solar Turbines ‐ a Caterpillar company.
My research focuses on applying the principles of biological systems towards the design and control of mobile robots. I am passionate about mechanical design and system dynamics, and I enjoy working at the intersection of these areas with biological locomotion research. The current research in my lab is focused on three core areas: 1) development of novel fabrication methods for bio-inspired mobile robots at small and large scales, 2) translating principles of biological legged locomotion to legged robot design and control, 3) development of bio-inspired energy efficient flapping wing robots.
Professor Hidalgo-Gonzalez directs the Renewable Energy + Advanced Mathematics (REAM) lab which focuses on high penetration of renewable energy using optimization, control theory and machine learning. Dr. Hidalgo-Gonzalez co-developed a stochastic power system expansion model to study the Western North America’s grid under climate change uncertainty. She also works on power dynamics with low and variable inertia, and controller design using machine learning and safety guarantees. She is generally interested in power dynamics, electricity market redesign to aid the integration of renewable energy, microgrids for wildfire risk mitigation, distributed control, and learning for dynamical systems with safety guarantees. Dr. Hidalgo-Gonzalez is part of the IEEE Power & Energy Society Task Force titled “Data-Driven Controls for Distributed Systems”. She holds two M.S. and a Ph.D. from UC Berkeley.
As director of the Safe Autonomous Systems Lab, Prof. Herbert works to efficiently guarantee safe control of autonomous systems based on available models and given information about the environment. These techniques must be able to quickly adapt to unexpected changes and new information in the system or the environment. The lab uses tools from optimal control theory and dynamics games, machine learning, and cognitive science to develop new techniques for safety and efficiency in autonomous systems. These techniques are backed by both rigorous theory and physical testing on robotic platforms.
Multidisciplinary design optimization, with a focus on large-scale optimization using adjoint-based sensitivity analysis. Applications include electric air taxis, UAVs, commercial aircraft, robotic systems, and satellites. He received his PhD from the University of Michigan (2015) and worked at NASA Glenn Research Center before joining UCSD.
Is an Assistant Professor in Mechanical and Aerospace Engineering. He works on computational methods and numerical analysis for control, optimization, design and uncertainty quantification of complex and large-scale systems. He is particularly interested in reduced-order models and multifidelity approaches to solve those problems, and recent work has been on (reactive/thermal) flows. In the presence of uncertainties, he has worked on reliability-based design and design under uncertainty.
Adaptive control, nonlinear systems, real-time optimization, and control of systems modeled by partial differential equations, such as traffic flows and additive manufacturing. Krstic is a coauthor of fourteen books, a Fellow of IEEEI, SIAM, ASME, IFAC, AAAS, and a recipient of many awards and paper prizes, including the ASME Oldenburger Medal and the SIAM Reid Prize. He holds the Alspach Endowed Chair and serves as Senior Associate Vice Chancellor for Research at UC San Diego.
MARTINEZ DIAZ, SONIA
Dr. Martínez' research interests include networked control systems, multi-agent systems, and nonlinear control theory with applications to robotics and cyber-physical systems. In particular, she has focused on the modeling and control of robotic sensor networks, the development of distributed coordination algorithms for groups of autonomous vehicles, and the geometric control of mechanical systems. For her work on the control of underactuated mechanical systems she received the Best Student Paper award at the 2002 IEEE Conference on Decision and Control. She was the recipient of a NSF CAREER Award in 2007. For the paper "Motion coordination with Distributed Information," co-authored with Jorge Cortés and Francesco Bullo, she received the 2008 Control Systems Magazine Outstanding Paper Award. She is a Senior Editor of the IEEE Transactions on Control of Networked Systems and an IEEE Fellow. For more biographical information please look here (updated on Feb 6th, 2019).
Dr. Morimito's research focuses on the design and control of flexible continuum robots for increased dexterity and accessibility in uncertain environments, particularly for minimally invasive surgical interventions. She is also working to address the challenges of designing human-in-the-loop interfaces for controlling these flexible and soft robots, including the integration of haptic feedback to improve surgical outcomes. Current projects aim to solve problems in design optimization, flexible sensor development, and both model-based and model-free control techniques for continuum robots.
SKELTON, ROBERT E
Reduction theory, integration of plant and feedback design, and control applications. Prior to his retirement from UC San Diego, Professor Skelton made major advancements in system design while on active faculty, including his pioneering development of the area of modeling and control of tensegrity structures. He is a member of the National Academy of Engineering, Fellow of AIAA and IEEE, held the Springer Professorship at UC Berkeley, and received the Senior Scientist Award from the von Humboldt Foundation. He has published three books. He founded the Dynamics and Control program in the MAE Department in 1997 and was the inaugural holder of the Alspach endowed chair.
Dr. Tolley’s Bioinspired Robotics and Design Lab seeks to borrow the key principles of operation from biological systems and apply them to robotic design. Research efforts focus on approaches to the design, fabrication, and control of 1) soft robotic systems that navigating the world by walking, digging, and swimming; 2) soft robots that can safely interact with humans and delicate objects; and 3) robots that self-assembly by folding.
Nikolay A. Atanasov is an Assistant Professor at the Department of Electrical and Computer Engineering, University of California, San Diego, CA, USA. His research focuses on robotics, control theory, and machine learning and in particular on autonomous information collection using ground and aerial robots for localization and mapping, environmental monitoring, and security and surveillance. He works on probabilistic environment models that unify geometry and semantics and on optimal control and reinforcement learning approaches for minimizing uncertainty in these models.
William Helton is an operator theorist and one of the founders of the areas of robust control for linear systems (IEEE CAS Outstanding Paper Award) and H-infinity control for nonlinear systems. He is a co-author of two books on these subjects. His recent interests include theory and computer algebra for problems where the variables are matrices: as occur in quantum information, random matrices and linear systems. Helton has worked with Ford and other companies. He is a Fellow of IEEE and the AMS.
Melvin Leok. Computational geometric mechanics, computational geometric control theory, and numerical analysis. He received his Ph.D. in Control and Dynamical Systems from Caltech, and was a three-time NAS Kavli Frontiers of Science Fellow, and the recipient of the NSF CAREER Award, SciCADE New Talent Prize, Leslie Fox Prize in Numerical Analysis, and the SIAM Student Paper Prize.
Ruth Williams Stochastic processes and probability theory, with applications to networks, queueing theory, Internet congestion control, synthetic biology, and the topic of mathematics of finance on which she has a published textbook. She was an NSF Presidential Young Investigator, Guggenheim and Sloan Fellow; she is an elected member of the National Academy of Sciences, a Fellow of the American Academy of Arts and Sciences, AAAS, AMS, IMS, INFORMS and SIAM, and is a Corresponding Member of the Australian Academy of Science. She was awarded the John von Neumann Theory prize in 2016 and holds the Charles Lee Powell Chair in Mathematics I. For further information, see here http://www.math.ucsd.edu/~williams/bio.html
Surgical robotic design, autonomous robotic surgery, learning-based control and motion planning, continuum robots, and dynamic robot manipulation. Dr. Yip's research also includes different facets of model-free control, reinforcement learning, haptics, soft robotics and computer vision strategies, all towards achieving automated surgery. He previously held research positions at Disney Research and Amazon Robotics, and was an IEEE Robotics and Automation Society Distinguished Lecturer. He received his PhD in Bioengineering at Stanford University, following his MS in Electrical Engineering and BS in Mechatronics Engineering.