Laboratory for Learning Research
We perform research in learning (as well as learning how to perform
research). We are interested in machine (computational) learning
algorithms for adaptive and intelligent systems. Data mining is a
related area. While machine learning attempts to emulate human
learning to achieve artificial intelligence, data mining tries to gain
insights from data.
- Fundamental areas include anomaly/outlier detection, open set
recognition, category discovery, representation learning, imbalanced
learning, cost-sensitive learning, meta-learning, and distributed
learning.
- Application areas include credit card fraud detection, computer
security, web personalization, device monitoring, health care,
biology, and physics.
If you like learning and/or a challenge,
contact Philip Chan
Members
- Daniel Griessler
machine learning for forecasting >100MeV SEP events and intensity
- Josias Moukpe
Imbalanced and representation learning for SEP forecasting
- Alumni and Theses
Past Projects
Dec 2015
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Nov 2015
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