93 citations · 128 across the 15 of their papers we have counts for
10 papers · 1 filter
Evaluating Forecasting Techniques for Hardware Errors on a Large-scale HPC System
Kaiyuan Liao, Xiwei Xuan, Tanwi Mallick +3
Hardware error logs in high-performance computing (HPC) systems provide early signals of abnormal behavior, yet there remain challenges in effectively forecasting these errors usin…
Wavelet-Inspired Multiscale Graph Convolutional Recurrent Network for Traffic Forecasting
Qipeng Qian, Tanwi Mallick
Traffic forecasting is the foundation for intelligent transportation systems. Spatiotemporal graph neural networks have demonstrated state-of-the-art performance in traffic forecas…
A Comparative Study of Loss Functions: Traffic Predictions in Regular and Congestion Scenarios
Yangxinyu Xie, Tanwi Mallick
Spatiotemporal graph neural networks have achieved state-of-the-art performance in traffic forecasting. However, they often struggle to forecast congestion accurately due to the li…
Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach
Tanwi Mallick, Joshua David Bergerson, Duane R. Verner +3
Natural language processing (NLP) is a promising approach for analyzing large volumes of climate-change and infrastructure-related scientific literature. However, best-in-practice…
Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting
Weiheng Zhong, Tanwi Mallick, Hadi Meidani +2
Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic fore…
Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting
Tanwi Mallick, Prasanna Balaprakash, Jane Macfarlane
Deep-learning-based data-driven forecasting methods have produced impressive results for traffic forecasting. A major limitation of these methods, however, is that they provide for…
A data-centric weak supervised learning for highway traffic incident detection
Yixuan Sun, Tanwi Mallick, Prasanna Balaprakash +1
Using the data from loop detector sensors for near-real-time detection of traffic incidents in highways is crucial to averting major traffic congestion. While recent supervised mac…
Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks
Tanwi Mallick, Mariam Kiran, Bashir Mohammed +1
Wide area networking infrastructures (WANs), particularly science and research WANs, are the backbone for moving large volumes of scientific data between experimental facilities an…
Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting
Tanwi Mallick, Prasanna Balaprakash, Eric Rask +1
Highway traffic modeling and forecasting approaches are critical for intelligent transportation systems. Recently, deep-learning-based traffic forecasting methods have emerged as s…
Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecasting
Tanwi Mallick, Prasanna Balaprakash, Eric Rask +1
Traffic forecasting approaches are critical to developing adaptive strategies for mobility. Traffic patterns have complex spatial and temporal dependencies that make accurate forec…