Intelligent Systems Laboratory
The Intelligent Systems Laboratory (ISL) develops state-of-the-art statistical learning and reasoning algorithms to build intelligent systems for real world problems. At ISL, we are developing intelligent systems for electrical power system management, speech translation, mobile context information detection and modeling (e.g., indoor positioning and gesture detection). We perform both analytical as well as experimental studies. Our research on statistical learning, which draws on methods from statistics, machine learning, and data mining, is motivated by the massive amounts of data available nowadays. Statistical learning addresses the question "how can we build intelligent systems that learn from the data?". Using statistical learning algorithms, we can derive statistical models from the training data so that we can build a Bayesian network to automatically diagnosis the cause of system failures or build automatic speech translation systems that translate between Japanese and English without human written rules. Our research on probabilistic reasoning includes diagnosis and decision support in a wide range of application areas of real-world interest and importance. Applications typically involve the use of a probabilistic (or statistical) model, formulated as a Bayesian network, and involve can involve diagnosis, information fusion, and visualization.
In addition to research, ISL faculty members are engaged in education and mentoring of graduate and undegraduate students. Faculty members of ISL offer 18799S: Statistical Discovery and Learning and other Statistical Learning courses in addition to their research.
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