Computer Science - Machine Learning

Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data
Diagnosing epilepsy requires accurate seizure detection and classification, but traditional manual EEG signal analysis is …
Arash Hajisafi, Haowen Lin, Yao-Yi Chiang, Cyrus Shahabi
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Wearables for Health (W4H) Toolkit for Acquisition, Storage, Analysis and Visualization of Data from Various Wearable Devices
The Wearables for Health Toolkit (W4H Toolkit) is an open-source platform that provides a robust, end-to-end solution for the …
Arash Hajisafi, Maria Despoina Siampou, Jize Bi, Luciano Nocera, Cyrus Shahabi
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Holistic Survey of Privacy and Fairness in Machine Learning
Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML). Each …
Sina Shaham, Arash Hajisafi, Minh K. Quan, Dinh C. Nguyen, Bhaskar Krishnamachari, Charith Peris, Gabriel Ghinita, Cyrus Shahabi, Pubudu N. Pathirana
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Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting
The study presents the Busyness Graph Neural Network (BysGNN), designed to forecast visits to urban Points-of-Interest (POIs). BysGNN uncovers multi-context correlations among POIs across temporal, spatial, and semantic dimensions, resulting in a comprehensive dynamic graph. This approach significantly improves forecasting accuracy over traditional methods, offering a deeper understanding of urban dynamics.
Arash Hajisafi, Haowen Lin, Sina Shaham, Haoji Hu, Maria Despoina Siampou, Yao-Yi Chiang, Cyrus Shahabi
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Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting