16 citations · 26 across the 5 of their papers we have counts for
5 papers
Small Graph Is All You Need: DeepStateGNN for Scalable Traffic Forecasting
Yannick Wölker, Arash Hajisafi, Cyrus Shahabi +1
We propose a novel Graph Neural Network (GNN) model, named DeepStateGNN, for analyzing traffic data, demonstrating its efficacy in two critical tasks: forecasting and reconstructio…
WaveGNN: Integrating Graph Neural Networks and Transformers for Decay-Aware Classification of Irregular Clinical Time-Series
Arash Hajisafi, Maria Despoina Siampou, Bita Azarijoo +2
Clinical time series are often irregularly sampled, with varying sensor frequencies, missing observations, and misaligned timestamps. Prior approaches typically address these irreg…
Dynamic GNNs for Precise Seizure Detection and Classification from EEG Data
Arash Hajisafi, Haowen Lin, Yao-Yi Chiang +1
Diagnosing epilepsy requires accurate seizure detection and classification, but traditional manual EEG signal analysis is resource-intensive. Meanwhile, automated algorithms often…
Holistic Survey of Privacy and Fairness in Machine Learning
Sina Shaham, Arash Hajisafi, Minh K Quan +6
Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and trustworthy Machine Learning (ML). Each objective has been independently studied in the…
Learning Dynamic Graphs from All Contextual Information for Accurate Point-of-Interest Visit Forecasting
Arash Hajisafi, Haowen Lin, Sina Shaham +4
Forecasting the number of visits to Points-of-Interest (POI) in an urban area is critical for planning and decision-making for various application domains, from urban planning and…