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Feedback-based decision-making algorithms in AI systems, centralized or decentralized, can be studied via control theoretic tools, enabling the synthesis of novel and better state-of-the-art algorithms.
Sicun Gao
Computer Science and Engineering
sicung@ucsd.edu
Practical algorithms for NP-hard search and optimization problems in the decision, control, and design aspects of computational systems
Nikolay Atanasov
Electrical Engineering
natanasov@ucsd.edu
Robotics, Machine Learning, Control Theory, Optimization, and Computer Vision
Tara Javidi
Electrical Engineering
tjavidi@ucsd.edu
Wireless systems: stochastic and optimal resource allocation, network design and control, multi-access control, and topology design
Massimo Franceschetti
Electrical Engineering
massimo@ece.ucsd.edu
Information science of complex networks and systems: applications to control, computation, communication, and sensing
Miroslav Krstic
Mechanical Engineering
krstic@ucsd.edu
Nonlinear Control, Adaptive Systems, Delay Systems, Extremum Seeking
Jorge I. Poveda
Electrical Engineering
poveda@ucsd.edu
Hybrid control, nonlinear control, optimization, adaptive systems, learning-based control
Yuanyuan Shi
Electrical Engineering
yus047@ucsd.edu
Energy systems, cyber-physical systems, machine learning for energy management
Jun-Kun Wang
Electrical Engineering
yip@ucsd.edu
Theory and Applications of Optimization, Sampling, Online Learning, Game Theory, Trustworthy Machine Learning
Yang Zheng
Electrical Engineering
zhengy@ucsd.edu
Learning, Optimization, and Control of Network Systems, and their Applications to Autonomous Vehicles and Traffic Systems
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