Assistant Professor
Computing Sciences
College of Arts and Sciencestayeen@hartford.edu 860.768.4104 Dana Hall 333
Education
PhD in Computer Science, New Mexico State University
MS in Computer Science, New Mexico State University
MS in Computer Science & Engineering, University of Dhaka
BS in Computer Science & Engineering, University of Dhaka
Dr. Abu Saleh Md Tayeen is an Assistant Professor in the Department of Computing Sciences at the University of Hartford. Dr. Tayeen joined as a faculty in August 2024 after completing a year-long postdoctoral research position at New Mexico State University. Dr. Tayeen’s teaching philosophy emphasizes hands-on learning, critical thinking, and collaboration. He teaches courses on data mining, application of deep learning, data structures, and programming languages, and is dedicated to mentoring students both in and out of the classroom. In addition to his academic endeavors, Dr. Tayeen is passionate about learning new technologies and exploring scenic destinations. He is also committed to giving back to the community through volunteer work, particularly in educational programs.
Dr. Tayeen’s research expertise lies in machine learning, and application of deep learning with a focus on federated learning, large language models for network intrusion detection and privacy preservation. Additionally, he is interested in applying data science methods to extract insights from diverse fields such as social media, smart grid, and biometrics.
Kiran KC, Md Hossain and Abu Saleh Tayeen; “Analyzing Capacitive Swipe Gesture towards User Identification”. [Accepted] In 2025 IEEE International Joint Conference on Biometrics (IJCB), Osaka, Japan.
ASM Tayeen, S. Misra, H. Cao, and J. Harikumar, “CAFNet: Compressed Autoencoder-based Federated Network for Anomaly Detection”. In 2023 IEEE Military Communications Conference (MILCOM 2023), Boston, MA, USA. pp. 325-330.
Kumar, J. Liu, ASM Tayeen, S. Misra, H. Cao, J. Harikumar, and O. Perez, “FLNET2023: Realistic Network Intrusion Detection Dataset for Federated Learning”. In 2023 IEEE Military Communications Conference (MILCOM 2023), Boston, MA, USA. pp. 345-350.
Liu, H. Cao, ASM Tayeen, S. Misra, P. Kumar, and J. Harikumar, “Multi-Model-based Federated Learning to Overcome Local Class Imbalance Issues”. In the 22nd IEEE International Conference on Machine Learning and Applications (ICMLA 2023), Jacksonville, FL, USA.
ASM Tayeen, M. Biswal, and S. Misra, “DP-AMI-FL: Secure Framework for Machine Learning-based AMI Applications”. In the 14th Conference on IEEE Power & Energy Society Innovative Smart Grid Technologies, North America (ISGT NA 2023), Washington DC, USA. pp. 1-5. DOI: https://doi.org/10.1109/ISGT51731.2023.10066415
ASM Tayeen, A. Mtibaa, S. Misra, and M. Biswal, “Impact of Locational Factors on Business Ratings/ Reviews: A Yelp and TripAdvisor Study”. In C¸ akırta¸s, M., Ozdemir, M.K. (eds) Big Data and Social Media Analytics. Lecture Notes in Social Networks. Springer 2021, pp. 25–49. DOI: https://doi.org/10.1007/978-3-030-67044-3_2
ASM Tayeen, Thanh H. Nguyen, Van D. Nguyen, Enrico Pontelli, “Design and Implementation of Phylotastic, a Service Architecture for Evolutionary Biology”. In International Journal of Software Engineering and Knowledge Engineering (IJSEKE 2020), Volume: 30, Issue 10 (2020), pp. 1525-1550 DOI: https://doi.org/10.1142/S0218194020500382
Van D. Nguyen, Thanh H. Nguyen, ASM Tayeen, H. Dail Laughinghouse IV, Luna L. S´anchez-Reyes, Enrico Pontelli, Dmitry Mozzherin, Brian O’Meara, and Arlin Stoltzfus, “Phylotastic: improving access to tree-of-life knowledge with flexible, on-the-fly delivery of trees”. In Evolutionary Bioinformatics, Volume: 16 (2020). DOI: https://doi.org/10.1177/1176934319899384
Biswal, S. Misra, and ASM Tayeen, “Black Box Attack on Machine Learning Assisted Wide Area Monitoring and Protection Systems”. In the 11th Conference on IEEE Power & Energy Society Innovative Smart Grid Technologies, North America (ISGT 2020), Washington DC, USA. pp. 1-5. DOI:https://doi.org/10.1109/ISGT45199.2020.9087762