Opportunity at National Institute of Standards and Technology (NIST)
Data Science in Action
Information Technology Laboratory, Statistical Engineering Division
Please note: This Agency only participates in the February and August reviews.
|Lu, ZQ John
This project takes advantage of increasing activities at NIST on using machine learning in some engineering applications and is aimed at addressing issues of what to measure and what inputs to use when data such as sensor signals and time series. In both prediction or classification problems, selecting what input variables or features based on high-dimensional measurement data is crucial for developing algorithms for prediction or classification and we think that both transparency and subject matter understanding of the predictive features are equally important in analysis of complex and time-varying processes. The related issue of statistically principled use of metrics for evaluation is also important as demonstrated in diagnostic medicine. This opportunity will contribute to develop statistical approaches for signal feature extraction such as using derivative estimation, and classification from multiple or many inputs. The potential applications at NIST include fire prediction from sensors, anomaly detection in time series, and remote sensing from hyperspectral images.
A.E. Mensch, A.P. Hamins, J. Lu, W.C. Tam, (September 2019) Evaluating Sensor Algorithms to Prevent Kitchen Cooktop Ignition and Ignore Normal Cooking.
Statistical learning; engineering applications; logistic regression; smoothing derivative estimation
Open to U.S. citizens
Open to Postdoctoral applicants