| TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks A Geiger, D Liu, S Alnegheimish, A Cuesta-Infante, K Veeramachaneni 2020 IEEE International Conference on Big Data (Big Data), 33-43, 2020 | 359 | 2020 |
| AER: Auto-encoder with regression for time series anomaly detection L Wong, D Liu, L Berti-Equille, S Alnegheimish, K Veeramachaneni 2022 IEEE International Conference on Big Data (Big Data), 1152-1161, 2022 | 30 | 2022 |
| Using natural sentence prompts for understanding biases in language models S Alnegheimish, A Guo, Y Sun Proceedings of the 2022 Conference of the North American Chapter of the …, 2022 | 26* | 2022 |
| MTV: Visual analytics for detecting, investigating, and annotating anomalies in multivariate time series D Liu, S Alnegheimish, A Zytek, K Veeramachaneni Proceedings of the ACM on Human-Computer Interaction 6 (CSCW1), 1-30, 2022 | 24 | 2022 |
| Sintel: A machine learning framework to extract insights from signals S Alnegheimish, D Liu, C Sala, L Berti-Equille, K Veeramachaneni Proceedings of the 2022 International Conference on Management of Data, 1855 …, 2022 | 21 | 2022 |
| Cardea: An open automated machine learning framework for electronic health records S Alnegheimish, N Alrashed, F Aleissa, S Althobaiti, D Liu, M Alsaleh, ... 2020 IEEE 7th International Conference on Data Science and Advanced …, 2020 | 16 | 2020 |
| Can Large Language Models be Anomaly Detectors for Time Series? S Alnegheimish, L Nguyen, L Berti-Equille, K Veeramachaneni 2024 IEEE 11th International Conference on Data Science and Advanced …, 2024 | 6* | 2024 |
| Orion–a machine learning framework for unsupervised time series anomaly detection S Alnegheimish Massachusetts Institute of Technology, 2022 | 4 | 2022 |
| OrionBench: Benchmarking Time Series Generative Models in the Service of the End-User S Alnegheimish, L Berti-Equille, K Veeramachaneni | 1* | 2024 |
| Single word change is all you need: Designing attacks and defenses for text classifiers L Xu, S Alnegheimish, L Berti-Equille, A Cuesta-Infante, ... arXiv preprint arXiv:2401.17196, 2024 | 1 | 2024 |
| Weakly-supervised multi-sensor anomaly detection with time-series foundation models Z He, M Reimherr, S Alnegheimish, A Chandrayan | | 2024 |
| Probabilistic Programming Bots in Intuitive Physics Game Play F Alhasoun, S Alneghiemish Proceedings of the AAAI Conference on Artificial Intelligence 35 (1), 778-783, 2021 | | 2021 |
| Explingo: Explaining AI Predictions using Large Language Models A Zytek, S Pido, S Alnegheimish, L Berti-Equille, K Veeramachaneni | | |
Kalyan VeeramachaneniPrincipal Research Scientist, Massachusetts Institute of TechnologyVerified email at csail.mit.edu
