Jacob Buckelew is a graduate student in the Department of Electrical Engineering and Computer Science at Vanderbilt University and works as a research assistant at the Institute for Software Integrated Systems. He received his Bachelor’s degree in Computer Science and Mathematics from Rollins College in May 2022.

Jacob Buckelew Publications

1. J. Buckelew, J. P. Talusan, V. Sivaramakrishnan, A. Mukhopadhyay, A. Srivastava, and A. Dubey, WENFlow: Wavelet-Enhanced Normalizing Flows for Real-Time Anomaly Detection in CPS, in Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems, 2026.
   Bibtex Abstract

   @inproceedings{iccps2026_wenflow,
     author = {Buckelew, Jacob and Talusan, Jose Paolo and Sivaramakrishnan, Vasavi and Mukhopadhyay, Ayan and Srivastava, Anurag and Dubey, Abhishek},
     title = {WENFlow: Wavelet-Enhanced Normalizing Flows for Real-Time Anomaly Detection in CPS},
     year = {2026},
     booktitle = {Proceedings of the HSCC/ICCPS 2026: 29th ACM International Conference on Hybrid Systems: Computation and Control and 17th ACM/IEEE International Conference on Cyber-Physical Systems},
     location = {Saint Malo, France},
     keywords = {anomaly detection, normalizing flows, cyber-physical systems, unsupervised learning},
     note = {Acceptance rate: 28\%; Regular Paper; Track: Foundations},
     series = {HSCC/ICCPS '26}
   }
   

   Real-time anomaly detection in high-dimensional data is crucial for ensuring the security of cyber-physical systems (CPS) such as power grids and water distribution networks. Such data commonly take the form of multivariate time series, often unlabeled and necessitating the need for unsupervised detection methods. However, many unsupervised deep learning methods make assumptions about the normality of training data, which is unrealistic in real-world CPS where training data often contain anomalies or rare patterns. Furthermore, these methods rely on inefficient mechanisms to learn spatiotemporal dependencies in the data and scale quadratically with the number of system features. To address these problems, we propose Wavelet-Enhanced Normalizing Flows (WENFlow), an unsupervised deep learning model that identifies anomalies in low-density regions of the data distribution and does not assume access to anomaly-free training data. Notably, WENFlow leverages a scalable Gated Selective Self-Attention mechanism for capturing the most critical spatial dependencies between features. Compared to existing models, WENFlow scales linearly with respect to the number of system features and meets real-time inference requirements for anomaly detection. In our experiments, WENFlow achieves superior AUC scores against baseline methods across datasets with varying anomaly ratios, showcasing its robustness against contaminated training data. We evaluate WENFlow on 2 real-world benchmark datasets and a simulated phasor measurement unit dataset collected from a power grid testbed.

2. A. Zulqarnain, J. Buckelew, J. P. Talusan, A. Mukhopadhyay, and A. Dubey, TRACE: Traffic Response Anomaly Capture Engine for Localization of Traffic Incidents, in 2025 IEEE International Conference on Smart Computing (SMARTCOMP), 2025.
   Bibtex Abstract PDF

   @inproceedings{zulqarnain2025,
     author = {Zulqarnain, Ammar and Buckelew, Jacob and Talusan, Jose Paolo and Mukhopadhyay, Ayan and Dubey, Abhishek},
     booktitle = {2025 IEEE International Conference on Smart Computing (SMARTCOMP)},
     title = {TRACE: Traffic Response Anomaly Capture Engine for Localization of Traffic Incidents},
     year = {2025},
     month = jun,
     contribution = {lead}
   }
   

   Effective traffic incident management is critical for road safety and operational efficiency. Yet, many transportation agencies rely on reactionary methods, where incidents are reported by human agents and managed through rule- based frameworks like traditional Traffic Incident Management (TIM) systems. However, these are vulnerable to human error, oversight, and delays during high-stress conditions. Although recent initiatives incorporating real-time sensor data for cor- ridor monitoring and enhanced roadway information systems represent strides toward modernization, these systems often still require substantial human intervention. Recent advancements in graph-based deep learning models offer promising potential for addressing the limitations of traditional methods. While state- of-the-art models exist, the complexities of incident localization within dynamic and interconnected road networks, along with limited availability of high-quality labeled data and variability in real-time traffic measurements, are still open challenges. To address these, we propose the Traffic Response Anomaly Capture Engine (TRACE), a novel approach that combines graph neural networks, transformers, and probabilistic normalizing flows to accurately detect and localize traffic anomalies in real time. TRACE captures spatial-temporal dependencies, manages data uncertainty, and enhances automation, supporting more precise and timely incident localization. Our approach is validated on real-world traffic data and improved incident localization by 0.6 miles (17%) than SOTA methods while maintaining similar incident detection accuracy and mean detection delay.

3. J. Buckelew, S. Basumallik, V. Sivaramakrishnan, A. Mukhopadhyay, A. K. Srivastava, and A. Dubey, Synchrophasor Data Event Detection using Unsupervised Wavelet Convolutional Autoencoders, in 2023 IEEE International Conference on Smart Computing (SMARTCOMP), 2023, pp. 326–331.
   Bibtex Abstract DOI PDF

   @inproceedings{Buckelew2023,
     author = {Buckelew, Jacob and Basumallik, Sagnik and Sivaramakrishnan, Vasavi and Mukhopadhyay, Ayan and Srivastava, Anurag K. and Dubey, Abhishek},
     booktitle = {2023 IEEE International Conference on Smart Computing (SMARTCOMP)},
     title = {Synchrophasor Data Event Detection using Unsupervised Wavelet Convolutional Autoencoders},
     year = {2023},
     acceptance = {31},
     pages = {326-331},
     contribution = {lead},
     doi = {10.1109/SMARTCOMP58114.2023.00080},
     keywords = {Deep learning;Training;Event detection;Time series analysis;Power system stability;Feature extraction;Transformers;convolutional neural network;hardware-in-the-loop;unsupervised machine learning;phasor measurement units}
   }
   

   Timely and accurate detection of events affecting the stability and reliability of power transmission systems is crucial for safe grid operation. This paper presents an efficient unsupervised machine-learning algorithm for event detection using a combination of discrete wavelet transform (DWT) and convolutional autoencoders (CAE) with synchrophasor phasor measurements. These measurements are collected from a hardware-in-the-loop testbed setup equipped with a digital real-time simulator. Using DWT, the detail coefficients of measurements are obtained. Next, the decomposed data is then fed into the CAE that captures the underlying structure of the transformed data. Anomalies are identified when significant errors are detected between input samples and their reconstructed outputs. We demonstrate our approach on the IEEE-14 bus system considering different events such as generator faults, line-to-line faults, line-to-ground faults, load shedding, and line outages simulated on a real-time digital simulator (RTDS). The proposed implementation achieves a classification accuracy of 97.7%, precision of 98.0%, recall of 99.5%, F1 Score of 98.7%, and proves to be efficient in both time and space requirements compared to baseline approaches.