Publications and Preprints

More details on my publications can be found on my Google Scholar page

• N. Ahad, M. Davenport, and E. Dyer, “SSSS_TSA: Time Series Domain Adaptation via Channel-Selective Representation Alignment”, to appear in Transactions on Machine Learning Research (TMLR), 2025.
• N. Ahad, E. Dyer, K. Hengen, Y. Xie, and M. Davenport, “Learning Sinkhorn divergences for supervised change point detection”, in revision, IEEE Transactions on Signal Processing
• N.Ahad, M. Davenport, and Y. Xie, “Data Adaptive Symmetrical CUSUM”, to appear in Sequential Analysis
• N. Ahad, M. Davenport, “Semi-supervised Sequence Classification through Change Point Detection”, in Proc. AAAI Conf. on Artificial Intelligenec (AAAI), 2021.
• N.Ahad, S. Sonenbum, M. Davenport,and S. Sprigle, “Validating a Wheelchair In-Seat Activity Tracker”, Assistive Technology, 34(5), pp. 588-598, 2021.
• C. Uzray, N.Ahad, M. Abazou,and E. Dyer, “Detecting change points in neural population activity with contrastive metric learning”, in Proc. IEEE Conf. on Neural Engineering (IEEE NER) , 2023
• M. Azabou, M. Mendelson, N. Ahad, M. Sorokin, S. Thakoor, C. Urzay,and E. Dyer, “Relax, it doesn’t matter how you get there: A new self-supervised approach for multi-timescale behavior analysis”, in Proc. Conf. on Neural Information Processing Systems (NeurIPS), 2023
• F. Zhu, A. Sedler, H. Grier, N. Ahad, M. Davenport, M. Kaufman,A. Giovannucci,and C.Pandarinath “Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time”, in Proc. Conf. on Neural Information Processing Systems (NeurIPS), 2021
• J. Quesada, L. Sathidevi, R. Liu, N. Ahad, J. Jackson, M. Azabou, J. Xiao, C. Liding, M. Jin, C. Urzay, W. Gray-Roncal, E. Johnson,and E. Dyer, “MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction”, in Proc. Conf. on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, 2022
• A. D. McRae, A. Xu, J. Jin, N. Nadagouda, N. Ahad, P. Guan, S. Karnik, and M. A. Davenport, “Delta distancing: A lifting approach to localizing items from user comparisons”, in Proc. IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 2022

Peer-reviewed Abstracts and Workshop Papers

• N.Ahad, N. Nadagouda, E. L. Dyer, and M. A. Davenport, “Active Learning for Time Instant Classification”, Data-centric Machine Learning Research Workshop, Int. Conf. on Machine Learning (ICML), 2023
• C. Uzray, N.Ahad, M. Abazou,and E. Dyer, ”Detecting change points in neural population activity with contrastive metric learning”, Conf. on Cogntive and Computational Neuroscience (CCN), 2022

Patent Applications

• T. Mizoguchi, L. Tong, Z. Chen, W. Cheng, H. Chen,and N. Ahad, “Ordinal Classification through Network Decomposition”, US Patent App. 17/896,747 , 2023
• L. Tong, T. Mizoguchi, Z. Chen, W. Cheng, H. Chen, and N. Ahad, “Semi-supervised framework for efficient time-series ordinal classification”, US Patent App. 18/152,238, 2023

Dissertation

N. Ahad, “Detecting and Leveraging Changes in Temporal Data”, Ph.D. Thesis, Georgia Instiute of Technology, August 2024. [Local Copy], [Slides]