Systems

Research in the systems group covers the fundamental science and technological aspects of a broad range of areas related to complex control, communication, biological, and social systems.  Research projects are led by several faculty members in Electrical Engineering, in collaboration with faculty from other SEAS and Yale departments. 

Current research directions include biomemetic sensing, data analysis, adaptive, cooperative and distributed control of complex systems, biomedical image processing, control with communication constraints, network information theory, graphical models, biological and social dynamical systems, robust design, optimization, coding, and economics of wireless networks, as well as designing and operating efficient, robust, and fair Internet networks.

Research Summaries for EE Systems Area by Faculty:

Roman Kuc: Biomimetic sensing: using biologically-motivated sensors, signals, strategies and physiological spikes for extracting information and perception; data analysis and forecasting using random searches for model parameter estimation from actual data; performance limits in autonomous robotics due to sensing and processing capabilities.

A. Stephen Morse: Sensor networking; cooperative, coordinated and distributed control; adaptive control; applications of graph rigidity theory to formation control.

Kumpati Narendra: Control of rapidly time-varying systems using multiple models; partial communication in decentralized adaptive control of interconnected dynamical systems; diagonal stability in interconnected systems; adaptive identification and control of complex systems using neural networks; application of systems techniques to modeling biological systems.

Lawrence Staib: Biomedical image analysis, processing, and measurement; geometric and statistical models for the analysis and identification of structural and functional biomarkers with applications in neuroimaging; magnetic resonance diffusion weighted image analysis.

Hemant Tagare: Bio-medical image processing and analysis; Bayesian, information theoretic, and machine learning approaches to image segmentation; differential geometric analysis of non-rigid image registration and shape space analysis; 3-d protein structure from cryogenic electron microscopy.

Sekhar Tatikonda: Control in the presence of realistic communication channels; cooperative, multi-agent control; feedback capacity; network information theory; error correcting codes for storage channels; network optimization and resource sharing; network tomography; sum-product algorithm and statistical physics inspired algorithms for combinatorial optimization problems; learning graphical models from data.

J. Rimas Vaisnys: Dynamical system models of experimentally manipulable biological systems, e.g. hydroids; evolution of microorganism systems; social (subcultural) group origination, behavior and disappearance.

Y. Richard Yang: Designing and operating efficient, robust, and fair Internet networks; shadow and virtual networks for network analysis and debugging; high capacity and incentive-compatible end-user contributed wireless networks; large-scale, end-to-end network inference and monitoring; end host fairness and rate control; network localization; computer networks supporting spontaneous and controlled mobility.