Publications with accompanying open-source implementations. Each link includes code and instructions to reproduce or extend the work.
Izadkhah, Saba, Manikanta Kotthapalli, Banafsheh Rekabdar, Andrew Wagner, Sirisha Kothuri, and Nathan McNeil. “Cyclist Detection in Urban Traffic Using Fine-Tuned YOLO Models and Diverse Real-World Video Data.” In Proceedings of the 20th IEEE International Conference on Semantic Computing (ICSC), 2026. GitHub1, GitHub2
Golchin, Bahareh, and Banafsheh Rekabdar. “LLM-Enhanced Reinforcement Learning for Time Series Anomaly Detection” Accepted in IEEE ICSC 2026. GitHub
Izadkhah, Saba, and Banafsheh Rekabdar. " Multi-modal group recommendation with visual and textual fusion via deep reinforcement learning ." In Proceedings of the 2025 Conference on AI, Science, Engineering, and Technology (AIxSET). IEEE, September 2025. GitHub
Izadkhah, Saba, Banafsheh Rekabdar, Andrew Wagner, Joseph Broach, and Sirisha Kothuri. “A Time Series Transformer Attention Model for Enhancing Bicyclist Volume Estimation Using Data Fusion and Feature Selection Techniques.” In Proceedings of the 19th IEEE International Conference on Semantic Computing (ICSC), pp. 60–67, 2025. GitHub
Shayan Jalalipour, Danielle Justo, and Banafsheh Rekabdar. Understanding adversarial vulnerabilities and emergent patterns in multimodal RL. Accepted in Proceedings of the IEEE International Conference on Semantic Computing (ICSC), 2025. GitHub
Shayan Jalalipour, Danielle Justo, and Banafsheh Rekabdar. Understanding adversarial vulnerabilities and emergent patterns in multimodal RL. Neurips 2025 UniReps workshop (blog post), 2025. GitHub
Golchin, Bahareh, and Banafsheh Rekabdar. “Dynamic Reward Scaling for Multivariate Time Series Anomaly Detection: A VAE-Enhanced Reinforcement Learning Approach.” In Proceedings of the IEEE International Conference on Cognitive Machine Intelligence (CogMI), 2025. GitHub
Golchin, Bahareh, Banafsheh Rekabdar, and Ke Liu. “DRTA: Dynamic Reward Scaling for Reinforcement Learning in Time Series Anomaly Detection.” In Proceedings of the AI, Science, Engineering, and Technology Conference (AIxSET), 1–8. 2025. GitHub
Izadkhah, Saba, and Banafsheh Rekabdar. "Enhanced Deep Reinforcement Learning based Group Recommendation System with Multi-head Attention for Varied Group Sizes." In Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2024), Bruges, Belgium, 2024. i6doc.com publ., ISBN: 978-2-87587-090-2. GitHub
Izadkhah, Saba, Andrew Wagner, Banafsheh Rekabdar, Joseph Broach, and Sirisha Kothuri. “Enhancing Bicyclist Volume Estimation with Data Fusion and Deep Learning Techniques.” In Proceedings of the 2024 Conference on AI, Science, Engineering, and Technology (AIxSET), pp. 35–44. IEEE, 2024. GitHub
Kothuri, Sirisha, Banafsheh Rekabdar, Joseph Broach, Saba Izadkhah, and Andrew Wagner. “Improving the Accuracy and Precision of Bicycle Volume Estimates Using Advanced Machine Learning Approaches.” NITC-TT-1614, 2024. GitHub
Jalalipour, Shayan, and Banafsheh Rekabdar. "Noisy-defense variational auto-encoder (ND-VAE): An adversarial defense framework to eliminate adversarial attacks." In 2023 Fifth International Conference on Transdisciplinary AI (TransAI), pp. 50-57. IEEE, 2023. Github
Shayan Jalalipour, Sriharshitha Ayyalasomayjula, Hashem Damrah, Junfan Lin, Banafsheh Rekabdar, and Ruopu Li. Deep learning-based spatial detection of drainage structures using advanced object detection methods. In 2023 Fifth International Conference on Transdisciplinary AI (TransAI), pages 1–10. IEEE, 2023. GitHub
Izadkhah, Saba, and Banafsheh Rekabdar. "Deep Reinforcement Learning based Group Recommendation System with Multi-head Attention Mechanism." In Proceedings of the 2023 Fifth International Conference on Transdisciplinary AI (TransAI ), pp. 120–127. IEEE, 2023. Github
S. Jalalipour, S. Ayyalasomayjula, H. Damrah, J. Lin, B. Rekabdar and R. Li, "Deep Learning-Based Spatial Detection of Drainage Structures using Advanced Object Detection Methods," 2023 Fifth International Conference on Transdisciplinary AI (TransAI), Laguna Hills, CA, USA, 2023, pp. 1-10, doi: 10.1109/TransAI60598.2023.00007. Github