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Vijay Janakiraman: "Discovering Precursors to Aviation Safety Incidents: Algorithms and Applications."

Abstract:Precursors give insights into why and how a safety incident occurred and helps in proactively managing Aviation safety risks. Mining precursors in Aviation time series data is challenging because of several reasons including high dimensionality, data heterogeneity, high class imbalance and lack of ground truth information. In this talk, I will introduce the problem of precursor discovery, two algorithms for mining precursors and their application to safety incidents such as high energy landing, stall risk during take-off and go-around.

Bio: Vijay Janakiraman is a member of the NASA University Affiliated Research Center (UARC) and the Data Sciences Group in the Intelligent Systems Division at NASA Ames Research Center. Vijay researches data mining algorithms and applications to NASA problems involving high-dimensional time series and operational data. His work includes anomaly detection, discovery of precursors and prediction of adverse events in flight operational data, traffic flow management, and network shortest path problems. Vijay is also actively exploring novel deep learning and anomaly detection algorithms that can run fast with less overhead. He collaborates as part of a NASA Center Innovation Fund award in using biologically inspired machine intelligence models and representational learning for space image processing and flight anomaly detection. Vijay also collaborates with the NASA Quantum Artificial Intelligence Lab (QuAIL) exploring quantum algorithms for data mining.

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