Add to ORKGRESEARCH INTERESTS My research is in the fields of data mining and knowledge discovery, applied machine learning and statistics, and network science; with a focus on pattern discovery, anomaly, fraud, and event detection in large, time-varying network data by developing and using scalable algorithms and tools
Anomaly detection is a huge fi\u80eld of research focused on the task of \u80finding weird or outlyi...
My research bridges data mining and human-computer interaction (HCI) to synthesize scalable, interac...
Complex networks emerge as a natural framework to describe real-life phe- nomena involving a group o...
Large real-world graph (a.k.a network, relational) data are omnipresent, in online media, businesses...
Networked systems are everywhere - such as the Internet, social networks, biological networks, trans...
My main research interest lies in developing machine learning and large-scale data mining methods fo...
Network analysis and graph mining play a prominent role in providing insights and studying phenomena...
Thesis (M.S.)--Boston UniversityThis thesis focuses on the problem of anomaly detection in computer ...
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Syste...
Anomalies could be the threats to the network that have ever/never happened. To protect networks aga...
Knowledge discovery from disparate data sources can be very useful for gaining a better understandin...
Most intrusion detection approaches rely on the analysis of the packet logs recording each noticeabl...
Contemporary organizations live in an environment of networks: internally, they manage the networks ...
Abstract. The article deals with detection of network anomalies. Network anomalies include everythin...
Thesis (Ph.D.)--Boston UniversityPLEASE NOTE: Boston University Libraries did not receive an Authori...
Anomaly detection is a huge fi\u80eld of research focused on the task of \u80finding weird or outlyi...
My research bridges data mining and human-computer interaction (HCI) to synthesize scalable, interac...
Complex networks emerge as a natural framework to describe real-life phe- nomena involving a group o...
Large real-world graph (a.k.a network, relational) data are omnipresent, in online media, businesses...
Networked systems are everywhere - such as the Internet, social networks, biological networks, trans...
My main research interest lies in developing machine learning and large-scale data mining methods fo...
Network analysis and graph mining play a prominent role in providing insights and studying phenomena...
Thesis (M.S.)--Boston UniversityThis thesis focuses on the problem of anomaly detection in computer ...
Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, Engineering Syste...
Anomalies could be the threats to the network that have ever/never happened. To protect networks aga...
Knowledge discovery from disparate data sources can be very useful for gaining a better understandin...
Most intrusion detection approaches rely on the analysis of the packet logs recording each noticeabl...
Contemporary organizations live in an environment of networks: internally, they manage the networks ...
Abstract. The article deals with detection of network anomalies. Network anomalies include everythin...
Thesis (Ph.D.)--Boston UniversityPLEASE NOTE: Boston University Libraries did not receive an Authori...
Anomaly detection is a huge fi\u80eld of research focused on the task of \u80finding weird or outlyi...
My research bridges data mining and human-computer interaction (HCI) to synthesize scalable, interac...
Complex networks emerge as a natural framework to describe real-life phe- nomena involving a group o...