Dr. Vandana Janeja
Professor · Tenured
Department of Information Systems
College of Engineering and Information Technology
Available for media inquiries
About
Vandana Janeja is Associate Dean of Research in the College of Engineering and Information Technology, Professor of Information Systems, at the University of Maryland, Baltimore County (UMBC). She served as Associate Dean for research and faculty development from 2023-2026, chair of Information Systems from 2019 to 2023. She is the director of iHARP, an NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions and heads the Multi Data lab at UMBC. She is member of the UMBC ADVANCE Executive committee focusing on diversity in STEM, and a UMBC innovation fellow (2020-2022) advancing the ideas of including ethics in data science.She served as an intermittent expert at NSF supporting data science activities in the CISE directorate (2018-2021). She holds a Ph.D. in Information Technology from Rutgers University.
Research interests
Data science, Artificial Intelligence, Trustworthiness, ethical thinking in data science, spatio temporal analytics
Teaching interests
Data science, Artificial Intelligence, ethical thinking in data science, foundations of data science
Education
- MBA, Management, Information Technology major — Rutgers University (2007)
- Ph D, Management, Information Technology major — Rutgers University (2007)
- MS, Computer Science — New Jersey Institute of Technology (2001)
- MS, Computer Management — Devi Ahilya Vishwa Vidyalaya (1999)
- BS, Geology — Devi Ahilya Vishwa Vidyalaya (1997)
- Other, Honors Diploma — National Institute of Information Technology (1997)
Publications
- Learning Subglacial Bed Topography from Sparse Radar with Physics-Guided Residuals 2026
- Improving Greenland Bed Topography Mapping with Uncertainty-Aware Graph Learning on Sparse Radar Data 2025
- Engaging K-12 Learners in Data Annotation for AI Climate Models 2025
- Rethinking Data Science Pedagogy with Embedded Ethical Considerations 2022
- Multi-Domain Anomalous Temporal Association (Multi-DATA) -- Moving towards explainability from multiple notions of time 2019
- Context Aware Discovery in Web Data through Anomaly Detection 2015
- STOUT: Spatio-Temporal Outlier detection Using Tensors 2014
- Computational Models to Capture Human Behavior in Cybersecurity Attacks 2014
- Discovery of anomalous windows through a Robust nonparametric Multivariate Scan Statistic (RMSS), 2013
- Research Teams Case Study: iHARP - NSF HDR Institute for Harnessing Data and Model Revolution in the Polar Regions
- Elevating Citizen Science Opportunities to Advance Machine Learning: A Case Study in Anomaly Detection
- Written testimony in support of Maryland house bill 1174 State Government – Technology Advisory Commission – Established, by Delegates Hill, Guzone, Howard, Qi, and Wu
- Written testimony in support of Maryland house bill 1132 state government – technology and science advisory commission – established, by Delegates Hill, Charles, Qi, Ruth, and Wu.
Presentations
- Engaging K-12 Learners in Data Annotation for AI Climate Models 2025
- A Preliminary Open Science Pipeline to Facilitate AI Reproducibility for Interdisciplinary Communities 2024
- ASSESSING ANNOTATION ACCURACY IN ICE SHEETS USING QUANTITATIVE METRICS 2024
Grants and Contracts
- EAGER: CI PAOS: Open Science Approach to regridding satellite datasets to a uniform spatial and temporal resolution for Complex Research Systems 2025
- Semantic, contextual, and scalable detection of zero-day attacks for cloud environments 2015
- PIPP Phase I: Integrating Data, Knowledge, and Expertise Through Modeling and Immersive Analytics for Equitable Pandemic Prevention
Research in Progress
- A Benchmark Dataset and Algorithm Evaluation for Supraglacial Lake Detection on the Greenland Ice Sheet 2025
Press topics
AI and data science, information trustworthiness, technology for social impact