Fact-checking systems have gained traction as scalable solutions, yet they often face challenges such as handling diverse evidence sources, integrating multimodal data, and presenting comprehensive narratives. In this work, we propose cluster-based retrieval augmented verification with explanation (CRAVE), a novel framework that integrates retrieval-augmented large language models (LLMs) with clustering techniques to address multimodal misinformation on social media. The framework is designed to process multimodal inputs (text and images) and iteratively refine evidence through agent-based mechanisms. We validated the framework on multiple real-world and synthetic datasets, showing that breaking up evidence into narrative clusters improves both retrieval precision, clustering quality, and judgment accuracy, showcasing its potential as a robust decision-support tool for fact-checkers.

Neural architecture search (NAS), which automates the design of neural network (NN) architectures for scientific datasets, requires significant computational resources and time — often on the order of days or weeks of GPU hours and training time. We design the Analytics for Neural Network (A4NN) workflow, a composable workflow that significantly reduces the time and resources required to design accurate and efficient NN architectures. We introduce a parametric fitness prediction strategy and distribute training across multiple accelerators to decrease the aggregated NN training time. A4NN rigorously record neural architecture histories, model states, and metadata to reproduce the search for near-optimal NNs. We demonstrate A4NN’s ability to reduce training time and resource consumption on a dataset generated by an X-ray Free Electron Laser (XFEL) experiment simulation. When deploying A4NN, we decrease training time by up to 37% and epochs required by up to 38%.

Hyperspectral imaging is used for a wide range of tasks from medical diagnostics to crop monitoring, but traditional imagers are prohibitively expensive for widespread use. This research strives to democratize hyperspectral imaging by using machine learning to reconstruct hyperspectral volumes from snapshot imagers. I propose a tunable lens with varying amounts of defocus paired with 31-channel spectral filter array mounted on a CMOS camera. These images are then fed into a reconstruction network that aims to recover the full 31-channel hyperspectral volume from a few encoded images with different amounts of defocus.

Neural networks (NNs) are increasingly utilized in high-throughput scientific workflows. In this context, NN efficiency is essential for successful workflow management. We use a multi-objective Neural Architecture Search (NAS), NSGA-Net, to search for highly accurate NNs while optimizing for efficient use of computational resources by minimizing FLoating-point Operations Per Second (FLOPS). We define a domain-agnostic methodology to generate NNs with the support of NSGA-Net, select promising NNs that balance accuracy and FLOPS usage, and refine a subset of NNs in order to curate networks suitable for efficient data analysis. We apply this methodology to a protein diffraction use case. Preliminary results show NNs that efficiently classify conformation of proteins with a final accuracy of 97.7% or higher and using only 187 FLOPS.