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Vandana Janeja

Professor

Information Systems
Information Technology & Engineering, Room 4104556238
Phone
410-455-6238
vjaneja@umbc.edu
Education
MBA, Rutgers University (2007)
Ph D, Rutgers University (2007)
MS, New Jersey Institute of Technology (2001)
MS, Devi Ahilya Vishwa Vidyalaya (1999)
BS, Devi Ahilya Vishwa Vidyalaya (1997)
Other, National Institute of Information Technology (1997)
Website
About

Vandana Janeja is an Associate Professor at the Information Systems department at UMBC. She has taught various courses including Advanced data analytics for cybersecurity, Data Mining. Her research is in the area of Data Mining with a focus on anomaly detection in traditional and spatio-temporal data in the domains of cybersecurity, Health Informatics and Traffic networks. She has published in various refereed conferences such as ACM SIGKDD, SIAM Data Mining, IEEE ICDM, National Conference on Digital Government Research, IEEE ISI and journals such as IEEE TKDE, DMKD and IDA. She has also recieved a AAAS Science and Technology Policy fellowship for the year 2017-2018, serving as a fellow at the National Science Foundation, CISE, Office of the Assistant Director during her sabbatical. She holds a Ph.D. in Information technology from Rutgers University.

Research Interests

Data Mining, Spatio-temporal data mining, Big Data Analytics

Teaching Interests

Data Mining, Databases

Contracts, Fellowships, Grants, and Sponsored Research

Karabatis, George (Principal) Janeja, Vandana (Co-Principal) Semantic, contextual, and scalable detection of zero-day attacks for cloud environments Grant (Not Funded) Sponsored by Office of Naval Research (Jan 1, 2015 – Jan 2, 2015)

Intellectual Contributions

18 Olney, Bucks International Journal on Web Engineering Technology

STOUT: Spatio-Temporal Outlier detection Using Tensors New York ACM SIGKDD 2014 Workshop, ODD2 Outlier Detection & Description under Data Diversity

Computational Models to Capture Human Behavior in Cybersecurity Attacks The Third ASE International Conference on Cyber Security

Discovery of anomalous windows through a Robust nonparametric Multivariate Scan Statistic (RMSS), 1 vol. 9 28-55 International Journal of Data Warehousing and Mining