Shilpa Madhavan Shinde is a Project Engineer with FedEx Express and earned her PhD in Industrial Engineering from Arizona State University in Nov 2012. She also earned her Master of Science degree in Industrial Engineering at the School of Computing, Informatics and Decision Systems Engineering, Arizona State University (ASU). She is the recipient of the Ellis R. Ott Scholarship for Applied Statistics and Management for the year 2011-12. It is awarded yearly by the Statistics Division of the American Society for Quality. She is also the recipient of the Student Travel grant for the Fall Technical Conference for the year 2011 and the recipient of the Early Career Grant for the Fall Technical Conference for the year 2013.
Before moving to the US to pursue her graduate studies, she received her Bachelor of Engineering degree in Electronics from the University of Mumbai. After which she has worked as a management consultant in India for 3.5 years with various industries to help improve their processes. She has helped setup the Quality Management & Information Management Systems for small, medium and large service and manufacturing companies. While completing her MS degree at ASU, she worked as a Research Assistant at the Biodesign Institute under the supervision of Dr. Frederic Zenhausern who was the director of the Center for Applied Nanobioscience. Her primary work at the center involved using designed experiments to help optimize their bio-assays and processes. She completed her Masters thesis under the supervision of Dr. Douglas C. Montgomery.
After completing her Masters degree, she joined the PhD program for Industrial Engineering in 2009 and continued to work under the supervision of Dr. Douglas C Montgomery. Her PhD dissertation is titled Analysis of Non Regular Fractional Factorial Designs. Currently she is an intern with Henkel Corporation where her primary role is to support Research and Development, Supply Chain and other Support Services with their statistical needs.
Her main research interests are industrial statistics, designed experiments for industrial and research environment, data analytics and process improvement using analytical/statistical tools.