97 citations · 136 across the 19 of their papers we have counts for
7 papers · 1 filter
Towards Personalized Quantum Federated Learning for Anomaly Detection
Ratun Rahman, Sina Shaham, Dinh C. Nguyen
Anomaly detection has a significant impact on applications such as video surveillance, medical diagnostics, and industrial monitoring, where anomalies frequently depend on context…
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…
Fair Spatial Indexing: A paradigm for Group Spatial Fairness
Sina Shaham, Gabriel Ghinita, Cyrus Shahabi
Machine learning (ML) is playing an increasing role in decision-making tasks that directly affect individuals, e.g., loan approvals, or job applicant screening. Significant concern…
Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities
Sara Abdali, Sina shaham, Bhaskar Krishnamachari
As social media platforms are evolving from text-based forums into multi-modal environments, the nature of misinformation in social media is also transforming accordingly. Taking a…
When Machine Learning Meets Privacy: A Survey and Outlook
Bo Liu, Ming Ding, Sina Shaham +3
The newly emerged machine learning (e.g. deep learning) methods have become a strong driving force to revolutionize a wide range of industries, such as smart healthcare, financial…
Privacy Preserving Location Data Publishing: A Machine Learning Approach
Sina Shaham, Ming Ding, Bo Liu +3
Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private info…