H. Eldardiry

We propose novel approaches to detect malicious insiders from large amounts of work practice data by examining various activity domains, and detecting behavioral inconsistencies across these domains. Expand

We introduce the Role2Vec framework which uses the flexible notion of attributed random walks, and serves as a basis for generalizing existing methods such as DeepWalk, node2vec, and many others that leverage random walks. Expand

We propose a novel ensemble mechanism for collective classification that reduces both learning and inference variance, by incorporating prediction averaging into the collective inference process itself. Expand

We develop a novel method for resampling from relational data that uses a subgraph sampling approach to preserve the local relational dependencies while generating a pseudosample with sucient global variance. Expand

We describe a new problem space for provenance data management, contrast it with related problem spaces addressed by prior work on provenance abstraction and sanitization, and highlight challenges and future directions toward solutions to the provenance segmentation problem. Expand

This paper presents results of analysis performed on a physiologic time-series dataset that was collected from a wearable ECG monitoring system worn by individuals who suffer from panic disorder. Expand

We introduce the Role2Vec framework based on the proposed notion of attributed random walks to learn structural role-based embeddings for node embedding and network representation learning. Expand

We present first steps towards a mobile and wearable system intended to help people who experience regular and spontaneous panic attacks due to panic disorder. Expand

This paper presents steps towards a mobile and wearable system that aims to help reduce the severity of symptoms experienced by individuals with this condition. Expand

We present information fusion across multiple levels in a layered architecture to ensure accurate and reliable detection of anomalies from heterogeneous data in multi-dimensional data. Expand