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We live an era, where our devices are smart enough to sense our surroundings and continuously collect data from various sources. As a result, we have a large amount of heterogeneous data, which we need to process as accurately and efficiently as possible to make decisions. Thus, the development and deployment of information–gathering algorithms that arrive at good decisions while satisfying various types of constraints (e.g., energy, time, complexity, noisy or missing data, proprietary data) proves to be indispensable.

The decIsion Making for Ambient INtelligence Environments (IMAgINE) Lab at the Department of Computer Engineering in the University at Albany, SUNY is conducting cutting–edge research in enabling intelligent surroundings by advancing and exploiting tools from machine learning, signal processing, communications and control. We work on a variety of interdisciplinary and innovative applications including but not limited to e-health (e.g., energy-efficient physical activity detection for obesity prevention, emotion change detection for sleep disorder tracking, oral health tracking), smart home (e.g., personalized temperature control), smart cities (e.g., smart facilities management), environment & agriculture (e.g., smart farming), intelligent transportation (e.g., active collision detection in freeways), and active object detection for computer vision applications.

Our vision is to enable intelligent surroundings by continuous and resource–efficient sensing, inference and decision–making that adapts to information learned from unknown and uncertain environments.