Dragos D. Margineantu
Research & Technical Interests
Robust Machine Learning for decision systems. Learning models that estimate their competence.
Embedded Machine Learning - designing and training learned models that will be embedded into complex systems with humans and other automated or autonomous components.
Inverse Reinforcement Learning. Intent recognition and anomalous action understanding.
Ensemble learning, Cost-sensitive learning, Active learning.
Machine learning for computer vision and for large scale streaming sensor data.
Statistical evaluation and validation of decision systems. The validation and certification of decision systems for deployment.
Knowledge-based learning; integrating domain knowledge into learning algorithms.
Other Topics of Interest
Game theoretic decision making and intelligent systems capable of scalable game theoretic decisions.
Geometry and the reasoning required by it. Inference for solving synthetic geometry problems.