Past Events

Infectious diseases, human behavior, and health-related information spread via networks of physical, social, and institutional interactions. By understanding and harnessing the structure of these networks, public health agencies can more effectively monitor and control human diseases. Over the last decade, we have developed new network-based mathematical approaches for predicting the spread of infectious diseases and collaborating with public health officials worldwide to apply these methods to address public health challenges.

Abstract: In recent years dynamic spectrum sharing (DSS) has emerged as a key technique for increasing the radio spectrum utilization by accommodating unlicensed devices (UDs) on underutilized licensed (primary) frequencies. The first authorised application of DSS is specified in recent regulations issued by the Federal Communications Commission (FCC) for operation of UDs on DTV channels. The FCC regulations implement interference control from co-channel UDs by a UD exclusive region (protection region) around the primary system.

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As signal acquisition systems become increasingly interconnected, complex and diverse, new methods of data analysis and processing have become crucial. Often, the development of such methods benefits from modeling the inherent structure present in data.

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Stress is a catch-all term that describes bodily reactions to a range of perceived threats, both physical and psychological. Once essential for survival, the pace of modern life and its myriad demands has turned stress itself into a major threat. If chronic, stress can have serious health consequences, and is a leading risk factor for heart diseases, diabetes, asthma and depression.

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Cross-correlations between two GPS receivers of the unknown encrypted military P(Y) code are used to detect spoofing of civilian GPS signals. This type of detection algorithm is the only known defence against certain sophisticated spoofing attacks. A particularly nefarious spoofing attack strategy starts by overlaying false GPS radio-navigation signals exactly on top of the true signals. It slowly lifts the receiver tracking loops off of the true signals via power increases of its false signals.

Abstract: There are two frameworks commonly considered for machine learning problems. The first is the classical statistical learning framework where the learner is presented with a training sample of instances drawn independently from a fixed but unknown distribution. The goal of the learner is to use this training sample to pick a hypothesis that has comparatively low expected error on future instances drawn from the distribution.