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Faculty
Nada Basit
Associate Professor of Data Science by Courtesy
Department of Computer Science
Additional Roles
Assistant Professor, Academic General Faculty
Department of Computer Science
Email:
basit@virginia.edu
Phone: (434) 982-2213
Nada holds a joint appointment with the School of Data Science and the Department of Computer Science. With a background in computer science and a passion for data science, Nada Basit’s research interests include machine learning, bioinformatics, data mining, pattern recognition, biometrics, and computer science education.
In 2019, Basit received the Jefferson Scholars Foundation “Hartfield Excellence in Teaching” award, which recognizes engineering faculty who exemplify the highest standards and practices of teaching. She has a variety of published research including, “An Application of Data Mining Techniques to Explore Congressional Lobbying Records for Patterns in Pediatric Special Interest Expenditures Prior to the Affordable Care Act” and “Function Prediction for in silico Protein Mutagenesis Using Transduction and Active Learning.” Basit teaches several courses at UVA, from introductory programming courses to machine learning and database systems courses. Prior to coming to UVA, she worked as adjunct faculty at Mary Washington College, where she taught computer science and mathematics courses.
Basit holds a PH.D. in Computer Science from George Mason University. She also earned her M.S. in Computer Science from George Mason University and her B.S. in Computer Science from Mary Washington College.
Education
- PH.D., Computer Science, George Mason University
- M.S., Computer Science, George Mason University
- B.S., Computer Science, Mary Washington College
Areas of Practice
Bioinformatics, Machine Learning And Data Mining, Pattern Recognition
Selected Publications
Basit, Nada, Zhang, Y., Wu, H., Liu, H., Bin, J., He, Y., & Hendawi, A. M. (2016, December). MapReduce-based deep learning with handwritten digit recognition case study. 2016 IEEE International Conference on Big Data (Big Data). Presented at the 2016 IEEE International Conference on Big Data (Big Data), Washington DC,USA. doi:10.1109/bigdata.2016.7840783
Basit, Nada, Hendawi, A., Chen, J., & Sun, A. (2019, Februarie 22). A Learning Platform for SQL Injection. Proceedings of the 50th ACM Technical Symposium on Computer Science Education. Presented at the SIGCSE ’19: The 50th ACM Technical Symposium on Computer Science Education, Minneapolis MN USA. doi:10.1145/3287324.3287490
Hendawi, A. M., Alali, F., Wang, X., Guan, Y., Zhou, T., Liu, X., … Stankovic, J. A. (2016, Desember). Hobbits: Hadoop and Hive based Internet traffic analysis. 2016 IEEE International Conference on Big Data (Big Data). Presented at the 2016 IEEE International Conference on Big Data (Big Data), Washington DC,USA. doi:10.1109/bigdata.2016.7840901
Basit, Nada, & Wechsler, H. (2011). Prediction of enzyme mutant activity using computational mutagenesis and incremental transduction. Advances in Bioinformatics, 2011, 958129. doi:10.1155/2011/958129
Floryan, M., Dragon, T., Basit, N., Dragon, S., & Woolf, B. (2015). Who needs help? Automating student assessment within exploratory learning environments. In Lecture notes in computer science. Lecture Notes in Computer Science (bll 125–134). doi:10.1007/978-3-319-19773-9_13
Basit, N., & Wechsler, H. (2011, November). Function prediction for in silico protein mutagenesis using transduction and active learning. 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). Presented at the 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), Atlanta, GA. doi:10.1109/bibmw.2011.6112511
Harrison, E., Dreisbach, C., Basit, N., & Keim-Malpass, J. (2018). An application of data mining techniques to explore congressional lobbying records for patterns in pediatric special interest expenditures prior to the affordable care act. Frontiers in Big Data, 1, 3. doi:10.3389/fdata.2018.00003
Basit, Nada, & Wechsler, H. (2013). Explanation and prediction of nsSNP-induced pathology using association mining, transduction, and active learning. Advanced studies in biology, 5, 199–214. doi:10.12988/asb.2013.325