Barriers to Machine Learning Adoption in Aerospace and Building Systems

Abstract: Recent advances in machine learning techniques such as deep learning (DL) has rejuvenated data-driven analysis in aerospace and integrated building systems. DL algorithms have been successful due to the availability of large volumes of data and its ability to learn the features during the learning process. In spite of tremendous success of DL in technology companies, it’s applications to aerospace and building systems has been limited. This talk identifies the unique challenges these industries face and demonstrates the need for data-efficient machine learning, and context-aware machine learning with compelling use cases and results.

Bio: Kishore K. Reddy is a Staff Research Scientist at the United Technologies Research Center (UTRC) working in the area of computer vision, human machine interaction (HMI) and machine learning. He is currently leading the Digital Initiative at UTRC primarily focusing on Deep Learning applications in aerospace and building systems to perform outliers and anomalies detection, multi-modal sensor fusion and data compression. Kishore earned his Ph.D. in 2012 from University of Central Florida, where he developed advanced video and image analysis algorithms, primarily segmentation and classification approaches, for multiple contracts funded by DARPA, IARPA, and NIH.