Philip Chan

Philip Chan is an Associate Professor at Florida Institute of Technology's Department of Electrical Engineering and Computer Science, part of the College of Engineering and Science. His research focuses on machine learning, data mining, anomaly detection, and imbalanced learning with applications in cybersecurity, healthcare, and astrophysics. He leads the Laboratory for Learning Research (LLR), exploring adaptive intelligent systems and open-set recognition challenges. Chan teaches courses like Machine Learning and advises students on projects such as SEP event prediction and malware classification. His work emphasizes practical applications of AI, including fraud detection and device monitoring. Collaborations include interdisciplinary projects with physics and biomedical engineering.

Research interests include representation learning, cost-sensitive learning, and meta-learning. He actively publishes in top venues, with recent studies on solar energetic particle prediction and novel category discovery. Advises several students, including Daniel Griessler (SEP forecasting) and Josias Moukpe (imbalanced learning).