Hamed Valizadegan

Machine learning For Space Projects: Example Engineering and Science Case Studies

The success of the space projects depends much on our ability to understand and analyze their collected science and engineering data. In the science domain, the amount of collected data is so very large that requires building automatic tools to make sense of them. In this domain, often the data is not annotated well and/or there is not enough representative features for effective model construction. In the engineering domain, there are components that are designed and built for the first time and there is a limited domain knowledge available for them. This makes the hand-coded physics-based models less accessible and models that can utilize data for prediction more desirable. However, often there is a small number of working and failed units to learn from.

In this talk, I provide examples of both cases and show how machine learning can help in these imperfect scenarios. First, I use our efforts in life time prediction of Fine Guidance Sensors of Hubble Space Telescope as an example to demonstrate how little domain knowledge can help us develop effective machine learning models when the data availability is scarce. And then, I demonstrate our experience of using machine learning to classify the transit-like signals of Kepler spacecraft when annotation is imperfect and data features are not representative.

Holder of a PhD in computer science with focus on machine learning and data mining from Michigan State University, Hamed Valizadegan joined NASA Ames Research Center (UARC) as a machine learning research scientist in 2013. At Ames, he has been involved with multiple projects including Hubble Space Telescope, Kepler mission and ASRS aviation safety reports! Before joining NASA Ames, he spent three years at University of Pittsburgh conducting research in Medical Informatics. He has published in prestigious venues such as International Conference on Neural Information Processing Systems (NIPS), ACM SIGKDD conference on Knowledge discovery and Data Mining (KDD), and Artificial Intelligence and Statistics (AISTATS).

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