| Self-supervised transformer for sparse and irregularly sampled multivariate clinical time-series S Tipirneni, CK Reddy ACM Transactions on Knowledge Discovery from Data (TKDD) 16 (6), 1-17, 2022 | 68* | 2022 |
| Xlcost: A benchmark dataset for cross-lingual code intelligence M Zhu, A Jain, K Suresh, R Ravindran, S Tipirneni, CK Reddy arXiv preprint arXiv:2206.08474, 2022 | 34 | 2022 |
| Structcoder: Structure-aware transformer for code generation S Tipirneni, M Zhu, CK Reddy ACM Transactions on Knowledge Discovery from Data 18 (3), 1-20, 2024 | 23 | 2024 |
| Execution-based code generation using deep reinforcement learning P Shojaee, A Jain, S Tipirneni, CK Reddy arXiv preprint arXiv:2301.13816, 2023 | 19 | 2023 |
| Least-squares solutions to polynomial systems of equations with quantum annealing TH Chang, TCH Lux, SS Tipirneni Quantum Information Processing 18 (12), 374, 2019 | 15 | 2019 |
| Preparing for the next pandemic via transfer learning from existing diseases with hierarchical multi-modal BERT: A study on COVID-19 outcome prediction K Agarwal, S Choudhury, S Tipirneni, P Mukherjee, C Ham, S Tamang, ... Scientific reports 12 (1), 10748, 2022 | 12 | 2022 |
| A self-supervised learning-based approach to clustering multivariate time-series data with missing values (SLAC-Time): An application to TBI phenotyping H Ghaderi, B Foreman, A Nayebi, S Tipirneni, CK Reddy, V Subbian Journal of Biomedical Informatics 143, 104401, 2023 | 7 | 2023 |
| Recurrent neural network based time-series modeling for long-term prognosis following acute traumatic brain injury A Nayebi, S Tipirneni, B Foreman, J Ratcliff, CK Reddy, V Subbian AMIA Annual Symposium Proceedings 2021, 900, 2021 | 7 | 2021 |
| WindowSHAP: An efficient framework for explaining time-series classifiers based on Shapley values A Nayebi, S Tipirneni, CK Reddy, B Foreman, V Subbian Journal of Biomedical Informatics 144, 104438, 2023 | 4 | 2023 |
| Preparing for the next pandemic: Transfer learning from existing diseases via hierarchical multi-modal BERT models to predict COVID-19 outcomes K Agarwal, S Choudhury, S Tipirneni, P Mukherjee, C Ham, S Tamang, ... | 4 | 2021 |
| An empirical comparison of explainable artificial intelligence methods for clinical data: a case study on traumatic brain injury A Nayebi, S Tipirneni, B Foreman, CK Reddy, V Subbian AMIA annual symposium proceedings 2022, 815, 2022 | 2 | 2022 |
| Self-Supervised Clustering of Multivariate Time-Series Data for Identifying TBI Physiological States H Ghaderi, B Foreman, A Nayebi, S Tipirneni, CK Reddy, V Subbian arXiv preprint arXiv:2303.13024, 2023 | 1 | 2023 |
| Identifying TBI Physiological States by Clustering of Multivariate Clinical Time-Series H Ghaderi, B Foreman, A Nayebi, S Tipirneni, CK Reddy, V Subbian arXiv e-prints, arXiv: 2303.13024, 2023 | 1 | 2023 |
| Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data H Ghaderi, B Foreman, A Nayebi, S Tipirneni, CK Reddy, V Subbian AMIA Annual Symposium Proceedings 2023, 379, 2023 | 1 | 2023 |
| Tracking the Evolution of COVID-19 via Temporal Comorbidity Analysis from Multi-Modal Data. S Choudhury, K Agarwal, C Ham, P Mukherjee, S Tang, S Tipirneni, ... AMIA, 2021 | 1 | 2021 |
