Diane Parry received the 2017 SAS Distinguished Service award at the SciX 2018 conference, held on October 21–26 in Atlanta, Georgia.
Diane Parry received the 2017 SAS Distinguished Service award at the SciX 2018 conference, held on October 21–26 in Atlanta, Georgia.
Diane Parry receives the 2017 SAS Distinguished Service award at the SciX 2018. (Photo courtesy of Glen P. Jackson.)
The FACSS Distinguished Service Award is presented to an individual who has demonstrated exceptional long-term service to the FACSS organization. The organization typically chooses a person who has served with excellence in many different capacities and who has contributed to the continuing success of FACSS through consistent dedication and sacrifice.
Parry, who is recently retired from Procter and Gamble and who is currently the treasurer of the Society for Applied Spectroscopy (SAS), has volunteered with SAS and FACSS/SciX in a range of capacities, including as the 2015 SAS president; as a program section creator and organizer; as a three-term SAS parliamentarian from 2008–2011; as the developer of the role of metrician for FACSS from 2004–2014; as the 2006 FACSS governing board chair; and as a workshop co-presenter or presenter to undergraduate students from 1995–2014.
She received her PhD in physical and analytical chemistry from the University of Utah (Salt Lake City, Utah) in 1989, and completed her post-doctoral work at IBM’s Almaden Research Center in San Jose, California, before joining Proctor and Gamble in 1991.
New SERS-Microfluidic Platform Classifies Leukemia Using Machine Learning
January 14th 2025A combination of surface-enhanced Raman spectroscopy (SERS) and machine learning on microfluidic chips has achieved an impressive 98.6% accuracy in classifying leukemia cell subtypes, offering a fast, highly sensitive tool for clinical diagnosis.
Advancing Soil Carbon Analysis Post-Wildfire with Spectroscopy and Machine Learning
January 14th 2025Researchers from the University of Oviedo used diffuse reflectance spectroscopy (DRS) and machine learning (ML) to analyze post-wildfire soil organic carbon fractions, identifying key spectral regions and algorithms for advancing remote sensing applications.