Struct-MMSB: Mixed Membership Stochastic Blockmodels with Interpretable Structured Priors
Introduction Modeling the complex and intricate interactions existing within a community is an important network science problem that has gained
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Machine Learning
A3SL: Learning Interpretable Relational Structures of Hinge-loss Markov Random Fields
Introduction Machine-learning models that possess superior interpretability and ease of specification without compromising modeling power have the potential to positively
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Ensemble Regression Models for Short-term Prediction of Confirmed COVID-19 Cases
Introduction COVID-19 is a major global pandemic that has impacted the lives of people around the world. In spite of
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Predictive Analytics for Smart Water Management in Developing Regions
Introduction Water availability and management is an important problem plaguing many developing and under-developed countries. Many factors including geographic, political,
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SWaP: Probabilistic Graphical and Deep Learning Models for Water Consumption Prediction
Introduction With climate change exacerbating extreme weather conditions including droughts and famines, understanding and predicting human water consumption is critical
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NYCER: A Non-Emergency Response Predictor for NYC using Sparse Gaussian Conditional Random Fields (GCRFs)
Introduction Cities have limited resources that must be used efficiently to maintain their smooth operation. To ensure smooth operation of
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