Leman Akoglu

Leman Akoglu is an Associate Professor and Affiliated Faculty member in the Computer Science Department at Carnegie Mellon University. Her research focuses on Artificial Intelligence, with an emphasis on advancing zero-shot foundation models for tabular outlier detection and developing benchmarks for tabular outlier detection. She has contributed to publications such as 'Can Machine Learning Target Health Care Fraud?' and 'CoBAD: Modeling Collective Behaviors for Human Mobility Anomaly Detection.' Her work is available on her profile page at https://csd.cmu.edu/people/faculty/leman-akoglu.

Leman Akoglu is an Associate Professor at Carnegie Mellon University, specializing in AI/ML, Unsupervised Learning, Anomaly/Fraud/Event Mining, Graph Learning, and Neural Networks. Her research focuses on developing advanced methods for detecting anomalies, fraud, and events in complex data structures, particularly within graph-based frameworks. She has contributed significantly to the fields of graph neural networks, structural role extraction, and network-based fraud detection. Her work includes comprehensive surveys on graph anomaly detection and innovative approaches for scalable anomaly ranking and opinion spam detection. Her research emphasizes improving the robustness and efficiency of machine learning models in real-world applications.