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Hariharan K. Iyer

Hari Iyer was born in Chennai (then known as Madras), India. After graduating from high school he joined St. Xavier's college in Mumbai and completed his B.Sc degree in Mathematics in 1970. Subsequently he attended the University of Notre Dame in Indiana and completed his MS and his PhD degrees in Mathematics (Theory of Finite Simple Groups) under the direction of Professor W. J. Wong. He was an instructor of Mathematics at the University of Utah from 1975 to 1977.

In June 1977, Hari decided to work with Professor Raj Chandra Bose in the field of Experimental Design, at Colorado State University, and received his PhD in Statistics in 1980. Immediately following this he joined the faculty in the Department of Statistics at Colorado State University in 1980. From 1985 until recently, Hari was a faculty appointee at NIST (Boulder).

From 2007 to 2012 Hari held a position in the information analytics division of Caterpillar Inc. From 2012 to 2014 he was part of the analytics group at CGN Inc. Hari joined NIST (SED Gaithersburg) in February 2014.

During the recent years Hari, collaborating with Jack Wang and Jan Hannig, has contributed to research related to quantification of uncertainty as proposed in the Guide to the Expression of Uncertainty in Measurements (GUM). Much of this research is based on Fiducial Inference Methodology. After moving to NIST (Gaithersburg) Hari has been involved in research in forensic statistics, particularly relating to likelihood ratios for fingerprint and footwear fields (pattern comparison). He is currently collaborating with Steve Lund (SED) on these topics. Hari is also involved in machine learning applications in genomics research with NIST collaborators in Material Measurement Laboratory (MML).


Combinatorial Cassettes for Higher-Throughput Screening of Osteogenesis

Carl G. Simon Jr., Subhadip Bodhak, Hariharan K. Iyer, Luis Fernandez de Castro Diaz, Pam Robey, Azusa Maeda, Sergei Kuznetsov
Animal models are the most biologically relevant method for measuring the osteogenic capability of bone graft formulations. However, animal experiments are slow

Pivotal Methods in the Propagation of Distributions

Chih-Ming Wang, Jan Hannig, Hariharan K. Iyer
We propose a method for assigning a probability distribution to an input quantity. The distribution is used in the Monte Carlo method for uncertainty evaluation
Created October 9, 2019