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Profile Photo for Vandana Janeja

Vandana Janeja


Information Systems
Information Technology & Engineering, Room 4104556238
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)

Vandana Janeja is Professor and Interim Chair of the Information Systems department at the University of Maryland, Baltimore County (UMBC). She is member of the UMBC ADVANCE Executive committee focusing on diversity in STEM. She is a member of the ADVANCE leadership cohort (2020-2021), and a UMBC innovation fellow (2020-2022) advancing the ideas of including ethics in data science.
She is also serving as an intermittent expert at NSF supporting data science activities in the CISE directorate (2018-present).
Her research is in the area of data science with a focus on data heterogeneity across multi-domain datasets for anomaly detection, spatio-temporal data mining, as applied to cybersecurity, health care applications and traffic networks. She has published over 70 research papers and multiple visioning/community reports in the area of data science. Her research has been funded through federal, state and private organizations including NSF, U.S. Army Corps of Engineers, MD State Highway Administration, and CISCO. 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

5th Workshop on Mining and Learning from Time Series KDD2019

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