Dragos Margineantu - Current Research


My work and research interests are in machine learning. Lately, I am mostly focused on machine learning algorithms for systems that interact with humans and other automated components, form teams with humans, and make decisions that optimize a global system function (rather than a local loss function)..

Specifically, together with Rich Caruana (Microsoft Research) and Tom Dietterich (Oregon State University), we started looking at the novel research questions raised by Embedded Machine Learning (2015 AAAI Fall Symposium). I am particularly interested in human-machine embedded systems for anomaly detection, change detection, human intent recognition, real-time decisions, and sequential decisions. Inverse reinforcement learning (IRL) techniques, in particular, offer good means for studying human and computer decision making and elegant practical solutions for intent recognition and anomalous action detection. My Boeing colleagues and I have developed several interactive IRL-based solutions for explainable anomalous action detection and for intent analysis. About half of my time is dedicated to learning methods for practical machine learning solutions for temporal events and time series data. I also work on machine learning methods for object detection in images, especially from small data. I am mainly focused on methods that learn and interact with the users and the systems flawlessly, and therefore do feature construction and make decisions based on robustness and safety requirements. I served as the Boeing PI of DARPA's Bootstrapped Learning program where my colleagues and I focused on learning efficiently from small samples, semi-supervised learning, and inverse planning.

I am aware that most predictions should ultimately lead to decisions and actions and those decisions and actions require have costs and risks associated with them. Therefore I am very interested in learning and decision making techniques that deal with costs, budgets and risks (typically these are non-uniform functions). Cost-sensitive learning, active learning, and hierarchical learning are typically required in any practical application of machine learning algorithms.

I am also interested and working on statistical tests and validation methods for decision systems, learned models, and learning algorithms.

General categories of machine learning methods that myself and my colleagues have implemented and have experience with: ensemble learning, active learning, semi-supervised learning, clustering, deep learning, inverse reinforcement learning, sequential decision making and reinforcement learning.

In general, I am interested in listening and talking about learning, methods for scaling up and improving the performance of learning techniques, computational and statistical learning theory, unsupervised learning, reinforcement learning, and game theory.


Dragos Margineantu, 2016.