A unifying framework for integrating domain knowledge into machine learning algorithms for multidisciplinary industrial applications

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Though supervised learning methods demonstrate superior performance in predictive analytics, to date, they learn only from labeled data. The role of human beings in the process has been restricted to "mere labeler," and the knowledge of experts is not being fully utilized. This research envisages the development of a general framework to represent and integrate experts' opinions into learning systems, and, if possible, to transfer their invaluable knowledge for decision-making processes in real-life applications. Over the next five years, we plan to work on developing a novel unifying framework for utilizing human advice in an efficient manner to facilitate machine learning (ML) algorithms and we intend to develop novel solutions to human-guided learning for industrial applications. The long-term goals of the proposed research include deep investigation and development of novel, automated, efficient real-time ML algorithms capable of providing solutions to industrial problems by exploiting domain knowledge in both supervised and unsupervised methods. Innovation: Novel approaches for incorporating experts' knowledge into ML algorithms in noisy, structured domains will be developed to accelerate learning effective models in which, so far, humans have been merely used as labelers. We plan to develop a natural framework which will allow experts to encode their knowledge, both as general advice about the domain and as specific advice about particular examples. The proposed framework will offer generality by capturing different types of advice through preferential, cost function-based, qualitative constraints, as well as privileged information. Practical Applications: In practice, this research has the potential to significantly impact: (i)industrial applications in fields such as medical imaging and forensics, which can benefit from image segmentation and registration techniques and in which human advice can be exploited by using deep learning in an effective manner; (ii)the welding industry, by accelerating welding sequence optimization and reducing structural deformations; (iii)the manufacturing industry, by automating operations such as peg-hole insertion tasks; and (iv)the robotics industry, by providing solutions to robot inverse kinematics problems for higher degrees of freedom. Highly Qualified Personnel Training: The proposed research program intends to train students and produce data scientists with unique expertise in ML and artificial intelligence (AI), which will enable them to be world-class experts in the fields of ML, AI, and computer vision.