PhD student Sarah Alnegheimish is developing Orion, an open-source, user-friendly machine learning framework for detecting anomalies in large-scale industrial and operational settings. She focuses on making machine learning systems accessible, transparent, and trustworthy, and is exploring repurposing pre-trained models for anomaly detection.
MIT researchers have developed a method using large language models to detect anomalies in complex systems without the need for training. The approach, called SigLLM, converts time-series data into text-based inputs for the language model to process. Two anomaly detection approaches, Prompter and Detector, were developed and showed promising results in initial tests.
