Professor Lipor's research focuses on active and adaptive methods in machine learning, with an emphasis on applications in environmental sensing and monitoring. One area of his research is focused on low-dimensional and low-rank methods and their use in unsupervised/semi-supervised learning. Another aspect of his research focuses on developing algorithms that guide autonomous vehicles in understanding spatial phenomena ranging from air quality to geothermal energy resources.
Research Interest and Expertise:
- Active Machine Learning
- Subspace Methods
- Reinforcement Learning
Course Taught:
- EE 520 Random Processes
- EE 521 Mathematical Foundations of Machine Learning
- EE 524 Machine Learning Theory & Algorithms