Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, Xiaomeng Wang, Tanwi Mallick, Weijie J Su, Camillo Jose Taylor, Dan Roth. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Deep-learning-based data-driven forecasting methods have achieved impressive results for traffic forecasting. Specifically, spatiotemporal graph neural networks have emerged as a promising class of approaches because of their ability to model both spatial and temporal patterns in traffic data. A major limitation of these methods, however, is that they provide forecasts without estimates of data and model uncertainty, which are critical for understanding inherent variations of the data and forecast limitations due to a lack of training data. We develop a scalable deep ensemble approach to quantify data and model uncertainties for spatiotemporal graph neural networks. Our approach consists of four stages: 1) using a Gaussian-assumption-free simultaneous quantile regression loss for training a spatiotemporal graph neural network to model the traffic distribution; 2) applying a scalable Bayesian optimization method to tune the hyperparameters of the spatiotemporal graph neural network; 3) fitting a Gaussian copula generative model to capture the joint distributions of the high-performing hyperparameter configurations, and training an ensemble of models by sampling a new set of hyperparameter configurations from the generative model; and 4) decomposing the data and model uncertainties from the spatiotemporal graph neural network ensembles. We illustrate the effectiveness of our approach on a diffusion convolutional recurrent neural network, a state-of-the-art method for short-term traffic forecasting. We demonstrate the efficacy of our ensemble-based uncertainty quantification method by comparing it with other uncertainty estimation techniques. We show that our generic and scalable approach outperforms the current state-of-the-art Bayesian and a number of other commonly used frequentist uncertainty estimation techniques. The code is available on GitHub: https://github.com/tanwimallick/DESQRUQ.

Large language models (LLMs) are a transformational capability at the frontier of artificial intelligence and machine learning that can support decision-makers in addressing pressing societal challenges such as extreme natural hazard events. As generalized models, LLMs often struggle to provide context-specific information, particularly in areas requiring specialized knowledge. In this work, we propose a retrieval-augmented generation (RAG)-based multi-agent LLM system to support analysis and decision-making in the context of natural hazards and extreme weather events. As a proof of concept, we present WildfireGPT, a specialized system focused on wildfire hazards. The architecture employs a user-centered, multi-agent design to deliver tailored risk insights across diverse stakeholder groups. By integrating natural hazard and extreme weather projection data, observational datasets, and scientific literature through an RAG framework, the system ensures both the accuracy and contextual relevance of the information it provides. Evaluation across ten expert-led case studies demonstrates that WildfireGPT significantly outperforms existing LLM-based solutions for decision support.

Bowen Jiang, Yangxinyu Xie, Xiaomeng Wang, Yuan Yuan, Zhuoqun Hao, Xinyi Bai, Weijie J Su, Camillo Jose Taylor, Tanwi Mallick. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.