93 citations · 128 across the 15 of their papers we have counts for
15 papers
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…
WildfireGPT: Tailored Large Language Model for Wildfire Analysis
Yangxinyu Xie, Bowen Jiang, Tanwi Mallick +10
Recent advancement of large language models (LLMs) represents a transformational capability at the frontier of artificial intelligence. However, LLMs are generalized models, traine…
Analyzing Regional Impacts of Climate Change using Natural Language Processing Techniques
Tanwi Mallick, John Murphy, Joshua David Bergerson +3
Understanding the multifaceted effects of climate change across diverse geographic locations is crucial for timely adaptation and the development of effective mitigation strategies…
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…
Bharatanatyam Dance Transcription using Multimedia Ontology and Machine Learning
Tanwi Mallick, Patha Pratim Das, Arun Kumar Majumdar
Indian Classical Dance is an over 5000 years' old multi-modal language for expressing emotions. Preservation of dance through multimedia technology is a challenging task. In this p…
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…
Beat Detection and Automatic Annotation of the Music of Bharatanatyam Dance using Speech Recognition Techniques
Tanwi Mallick, Partha Pratim Das, Arun Kumar Majumdar
Bharatanatyam, an Indian Classical Dance form, represents the rich cultural heritage of India. Analysis and recognition of such dance forms are critical for the preservation of cul…
Posture and sequence recognition for Bharatanatyam dance performances using machine learning approach
Tanwi Mallick, Partha Pratim Das, Arun Kumar Majumdar
Understanding the underlying semantics of performing arts like dance is a challenging task. Dance is multimedia in nature and spans over time as well as space. Capturing and analyz…
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…