Anomaly detection involves identifying deviations from expected patterns or behaviors in various industries. A main component of the work involves developing algorithms and systems that can automatically detect anomalies, ranging from detecting fraud in financial transactions to identifying errors in industrial processes. Anomaly detection holds immense importance in the industry because it enables early detection of irregularities or malfunctions, helping to prevent potential disasters, minimize financial losses, and optimize operations. For example, real-time fraud detection systems in financial services can instantly flag suspicious transactions, preventing financial losses. In manufacturing, real-time monitoring can detect equipment malfunctions as they occur, minimizing downtime and maintenance costs. By leveraging AI and machine learning techniques, we can automate the process of anomaly detection across different fields, ensuring timely intervention and mitigation of risks.

In SEPE lab, we focus on anomaly detection in the pipeline industry, specifically targeting leak detection. Leaks in pipelines can lead to significant environmental damage, safety hazards, and economic losses. The diverse operating conditions and the risk of small leaks going undetected for long periods make detecting these leaks challenging. Our work involves applying novel deep learning and AI algorithms to analyze vast amounts of sensor data in real time, improving the accuracy of leak detection while minimizing false alarms. Implementing our techniques has notably reduced false alarms by 15%. By leveraging these advanced technologies, we aim to enhance the reliability and efficiency of pipeline monitoring systems, ensuring our algorithms are robust and effective under different scenarios.Looking ahead, our future work centers on leveraging Large Language Models (LLMs) for interpreting machine learning models, particularly for human-in-the-loop applications. This approach aims to enhance the interpretability and transparency of AI systems, allowing human operators to understand and trust the decisions made by these models. By integrating LLMs into anomaly detection frameworks, we can provide actionable insights and facilitate informed decision-making in various industries. The opportunities in this field are vast and diverse. Anomaly detection has the potential to address a wide range of challenges across industries, including but not limited to fraud detection, fault diagnosis, cybersecurity, and predictive maintenance. Machine learning and artificial intelligence can unlock new possibilities for anomaly detection, allowing systems to be more efficient, and sustainable.