14 citations · 41 across the 6 of their papers we have counts for
6 papers
Multi-scale Digital Twin: Developing a fast and physics-informed surrogate model for groundwater contamination with uncertain climate models
Lijing Wang, Takuya Kurihana, Aurelien Meray +6
Soil and groundwater contamination is a pervasive problem at thousands of locations across the world. Contaminated sites often require decades to remediate or to monitor natural at…
Cohorting to isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway
Alok Talekar, Sharad Shriram, Nidhin Vaidhiyan +10
The Mumbai Suburban Railways, \emph{locals}, are a key transit infrastructure of the city and is crucial for resuming normal economic activity. To reduce disease transmission, poli…
Examining Deep Learning Models with Multiple Data Sources for COVID-19 Forecasting
Lijing Wang, Aniruddha Adiga, Srinivasan Venkatramanan +3
The COVID-19 pandemic represents the most significant public health disaster since the 1918 influenza pandemic. During pandemics such as COVID-19, timely and reliable spatio-tempor…
Wisdom of the Ensemble: Improving Consistency of Deep Learning Models
Lijing Wang, Dipanjan Ghosh, Maria Teresa Gonzalez Diaz +5
Deep learning classifiers are assisting humans in making decisions and hence the user's trust in these models is of paramount importance. Trust is often a function of constant beha…
TDEFSI: Theory Guided Deep Learning Based Epidemic Forecasting with Synthetic Information
Lijing Wang, Jiangzhuo Chen, Madhav Marathe
Influenza-like illness (ILI) places a heavy social and economic burden on our society. Traditionally, ILI surveillance data is updated weekly and provided at a spatially coarse res…
Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction
Songgaojun Deng, Shusen Wang, Huzefa Rangwala +2
Forecasting influenza-like illness (ILI) is of prime importance to epidemiologists and health-care providers. Early prediction of epidemic outbreaks plays a pivotal role in disease…