Engineering 5. (Photo Taken by Matthew Manjos)
News
The 2011 Symposium on Advanced Intelligent Systems will be held on December 1 - 2, 2011, at the Faculty of Engineering, University of Waterloo, Waterloo Ontario, Canada.
Important Dates
| Paper submission deadline | October 15, 2011 |
| Author notification | November 11, 2011 |
| Camera-ready version | November 18, 2011 |
| Registration (on-site) | December 1 - 2, 2011 |
| Symposium dates | December 1 - 2, 2011 |
About SAIS
The 2011 Symposium on Advanced Intelligent Systems will be held on December 1 - 2, 2011, at the Faculty of Engineering, University of Waterloo, Waterloo Ontario, Canada. The symposium is organized by the University of Waterloo Centre of Pattern Analysis and Machine Intelligence (CPAMI). CPAMI is a centre of excellence in the field of Pattern Analysis and Machine Intelligence. The symposium is intended to provide a forum for the exchange of information on state-of-the-art research in the areas of autonomous and intelligent systems. Keynote lectures, technical and poster sessions will be held over the two-day period of the symposium.
Topics and Themes
SAIS 2011 will be organized into two main themes, with the list of technical areas in each theme given below. Authors shall choose primary and alternative technical areas when submitting their papers. Other research areas that fall within the four tracks are also welcome.
Theme 1: Intelligent Autonomous Systems
Autonomous Systems
Intelligent Vehicle Systems
Autonomous and intelligent robotics
Computational intelligence
Cooperative intelligent systems
Multi-agent systems
Smart sensing and perception
Applications: Intelligent transportation systems, assistive technologies, smart environments, embedded systems, rescue, surveillance and reconnaissance, robotics, industrial automation.
Theme 2: Machine Learning and Data Mining
Non-Bayesian models and estimation (kernel methods, nonparametric models, statistical and computational learning theory, manifolds and embedding, sparsity and compressed sensing)
Bayesian models and estimation (graphical models, causality, Gaussian processes, approximate inference)
Supervised, unsupervised, and semi-supervised learning (classification, regression, density estimation, clustering, topic models)
Reinforcement learning, planning, and control
Algorithms and architectures for high-performance computation in Machine Learning and Data Mining
Software for applications of Machine Learning and Data Mining
