Vandana Janeja
Professor
Phone |
410-455-6238
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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)
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Website | http://userpages.umbc.edu/~vjaneja/ |
About
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
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
(2019) Multi-Domain Anomalous Temporal Association (Multi-DATA) -- Moving towards explainability from multiple notions of time 5th Workshop on Mining and Learning from Time Series KDD2019
(2015) Context Aware Discovery in Web Data through Anomaly Detection Olney, Bucks International Journal on Web Engineering Technology
(2014) STOUT: Spatio-Temporal Outlier detection Using Tensors New York ACM SIGKDD 2014 Workshop, ODD2 Outlier Detection & Description under Data Diversity
(2014) Computational Models to Capture Human Behavior in Cybersecurity Attacks The Third ASE International Conference on Cyber Security
(2013) Discovery of anomalous windows through a Robust nonparametric Multivariate Scan Statistic (RMSS), International Journal of Data Warehousing and Mining