Applied Artificial Intelligence (AAI) Research Group
- Artificial Intelligence Applications
- Machine Learning for Policy
- Data Democracy and Open Data
- Context-Driven Data Science
About our work:
We are a team of interdisciplinary researchers focused on developing AI and machine learning applications for policy making, economics, healthcare, education, and software engineering. Our work is focused on providing actionable insights to decision makers and policy analysts through data-driven algorithms. Deploying AI methods across different domains have assisted in providing intelligent solutions to persisting problems, for example: policy making (at the government), defining customer behavioral trends (within commerce), improving the quality of service (hospitals and healthcare providers), predicting international trade trends (for economists), and predicting bugs and errors (in software development). However, throughout these deployments, serious show-stopper problems are still unresolved: the lack of context in datasets, data democratization, blind spots in data collection, data validation, hidden data bias, dataset incompleteness, and adversarial attacks. My research work is at the intersection of these issues.
Feras A. Batarseh (firstname.lastname@example.org)
Ruixin Yang (email@example.com)
Students - in alphabetical order
- Abhinav Kumar
- Ajay Kulkarni
- Deri Sondor Chong
- Ganesh Nalluru
- Po-Hsuan Su
- Xiaotong Hu
Alumni - in alphabetical order
- Gayatri Nambiar
- Gowtham Ramamoorthy
- Jash Pithadi
- Lin Deng
- Manish Dashora
- Nithin Kotte
Books by the Group:
Data Democracy: At the Nexus of Artificial Intelligence, Software Development, and Knowledge Engineering. Elsevier's Academic Press, release date: Dec 2019.
Federal Data Science: Transforming Government and Agricultural Policy using Artificial Intelligence. Elsevier's Academic Press, ISBN: 9780128124437, Oct 2017.
Top 10 Recent Publications:
- Batarseh, F., Nalluru, G., Gopinath, M., Beckman, J., “Application of Machine Learning in Econometrics and Predicting International Trade Trends”, Proceedings of the AAAI Fall Symposium Series: Artificial Intelligence in Government and Public Sector, Washington, D.C., November 2019.
- Batarseh, F., and Kulkar, A., “Context-Driven Data Mining Lifecycle through Bias Removal and Data Incompleteness Mitigation”, Proceedings of the 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2019), SIAM’s International Conference on Data Mining, Calgary, Canada, May 2019.
- Batarseh, F., Gendron, J., Laufer, R., Madhavaram, M., and Kumar, A., “A Context-Driven Data Visualization Engine for Improved Citizen Service and Government Performance Evaluation”, Proceedings of iSTE Open Science Journal of Modelling and Using Context, Oct 2018.
- Batarseh, F., Nambiar, G., Gendron, G., and Yang, R., “Geo-Enabled Text Analytics through Sentiment Scoring and Hierarchical Clustering”, IEEE Proceedings of the Seventh International Conference on Agro-Geoinformatics, Hangzhou, China, Aug 2018.
- Batarseh, F., Chanthati, S., Kotte, N., and Kumar, A., “Augmenting Policy Making got Autonomous Vehicles through Geoinformatics and Psychographics”, IEEE Proceedings of the Seventh International Conference on Agro-Geoinformatics, Hangzhou, China, Aug 2018.
- Batarseh, F., Yang, R., and Deng, L., “A Comprehensive Model for Management and Validation of Federal Big Data Analytical Systems”, Published at Springer's Journal of Big Data Analytics, Jan 2017.
- Batarseh, F., and Yang, R., “Intelligent Methods for Managing Geospatial Data at the US Federal Government”, IEEE Proceedings of the Sixth International Conference on Agro-Geoinformatics, Fairfax, VA, Aug 2017.
- Batarseh F., Pithadia, J., “Context-Aware User Interfaces for Intelligent Emergency Applications”, Proceedings of the Tenth International and Interdisciplinary Conference on Modeling and Context, Springer's Lecture Notes in Artificial Intelligence, Paris, France, Jun 2017.
- Batarseh, F., and Abdul-Latif, E., “Assessing the Quality of Service Using Big Data Analytics - with Application to Healthcare”, Published at Elsevier's Journal of Big Data Research, Nov 2015.
- Batarseh, F., and Gonzalez, A. J., “Predicting Failures in Agile Software Development through Data Analytics”, Published at Springer's Transactions: The Software Quality Journal, Aug 2015.
George Mason University
College of Science MSN 6C3
Research Hall #244
4400 University Dr, Fairfax, VA 22030