Research focuses on developing multivariate data analysis techniques to elucidate chemical information from the responses of chemical sensors and analytical instruments. These methods include neural networks, genetic algorithms, signal processing, and multivariate statistical methods as applied to pattern recognition and multivariate calibration. We use data analysis techniques to improve selectivity, sensitivity, and the robustness of chemical sensor systems that are used for monitoring hazardous chemical species in air or liquid for personal protection or environmental monitoring. Identification of chemical warfare agents, toxic industrial chemical, or fires is achieved by multivariate analysis and detection systems that use arrays of semi-selective sensors. Complex mixture analysis is greatly improved by multivariate calibration techniques as demonstrated by a variety of applications including membrane inlet mass spectrometry and gas chromatography mass spectrometry.

 

key words

Algorithms; Artificial intelligence; Chemical sensors; Environmental toxicology; Multivariate analysis; Pattern analysis; Signals and signal processing;

Citizenship:  Open to U.S. citizens and permanent residents

Level:  Open to Postdoctoral applicants