Dragos Margineantu

A Vision for Advancing Applied AI Research and Engineering

AI approaches are successful nowadays on tasks with fairly narrow contexts, and need to be engineered correctly. Aside of data availability and core AI algorithmic ideas, the success of most deployed machine learning and AI solutions, depends on appropriate problem formulations and on engineering approaches that can correctly tap into the right strengths of the AI approaches. In my talk, I will outline the five main directions that AI research and engineering will need to embrace to widen the contexts of applicability and to ease the deployment of solutions.

Dragos Margineantu is a Boeing Technical Fellow and AI Chief Technologist with Boeing Research & Technology. His research interests include machine learning, in particular methods for robust machine learning, reasoning and planning for decision systems, anomaly detection, reinforcement learning, human-in-the-loop learning, inverse reinforcement learning, cost-sensitive, active, and ensemble learning. Dragos Margineantu was one of the pioneers in research on ensemble learning and cost-sensitive learning, and in statistical testing of learned models. At Boeing, he developed machine learning and AI based solutions for airplane maintenance, autonomous systems, airplane performance, surveillance, design, autonomous systems, and security. Dragos serves as the Boeing AI lead for DARPA’s “Assured Autonomy” program, and served as the Boeing principal investigator (PI) of the DARPA “Bootstrapped Learning” project for which he is designed and developed learning effects by example, by explanation, active learning, and inference components. He was also the PI of DARPA's "Learning Applied to Ground Robots" (LAGR) program. He serves and served as the PI of several Boeing IRAD research projects in machine learning, data science, and intelligent systems. Dragos designed and developed the learning and computer vision components of Boeing’s “Opportune Landing Site” effort (AFRL). Dragos serves as the Editor of the Springer book series on “Applied Machine Learning” and as the Action Editor for Special Issues for the Machine Learning Journal (MLj). He serves on the editorial board of both major machine learning journals (MLj and JMLR), and served as senior program committee member of ICML (the premier machine learning conference), KDD (the premier data mining conference) and AAAI (the premier AI conference). He was the chair of the KDD 2015 Industry and Government Track and he organized and chaired a number of scientific workshops on anomaly detection, testing of decision systems, cost-sensitive and budgeted learning. He has edited a special issue of the Machine Learning Journal on Event Detection (Machine Learning 79:3, June 2010). Dragos Margineantu served as a senior program committee member or organizer of all major machine learning, AI, and data mining conferences and as a reviewer of all major ML, AI, and data mining scientific journals. Dragos Margineantu has a Ph.D. in Computer Science from Oregon State University (2001).

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