Brenda L. Dietrich, IBM Fellow and VP
Brenda Dietrich is an IBM Fellow and Vice President. She joined IBM in 1984 and has worked in the area now called analytics for her entire career. Her early work involved applying mathematical models to improve the performance of IBM manufacturing lines, and during her career she has worked with almost every IBM business unit and applied analytics to a numerous IBM decision processes. For over a decade she led the Mathematical Sciences function in the IBM Research division where she was responsible for both basic research on computational mathematics and for the development of novel applications of mathematics for both IBM and its clients. In addition to her work within IBM, she has been the president of INFORMS, the world’s largest professional society for Operations Research and Management Sciences, is an INFORMS Fellow, and has received multiple service awards from INFORMS. She has served on the Board of Trustees of SIAM and on several university advisory boards. She holds more than a dozen patents, has co-authored numerous publications, and frequently speaks on analytics at conferences. She holds a BS in Mathematics from UNC and an MS and Ph.D. in OR/IE from Cornell. Her personal research includes manufacturing scheduling, services resource management, transportation logistics, integer programming, and combinatorial duality. She currently leads the emerging technologies team in the IBM Watson group.
Merlise A. Clyde, Professor of Statistical Science, Duke University
Merlise Clyde is a Professor of Statistical Science at Duke University and have served as Chair of the Department of Statistical Science at Duke since 2013. She received her PhD in 1993 from the University of Minnesota and joined the faculty at the Institute of Statistics and Decision Sciences (now the Department of Statistical Sciences) at Duke University in the fall of 1993. She is a past President of the International Society of Bayesian Analysis (ISBA), and an elected Fellow of ISBA and the American Statistical Association. She received the Zellner Medal from ISBA in 2016. Her research focuses on Bayesian solutions to the related problems of feature/variable selection, model selection and prediction using an ensemble of models to account for model uncertainty using Bayesian Model Averaging, with an emphasis on prior choice and computation.
Moorea Brega, Sr. Director, Data Science, Premise Data
Moorea oversees Premise’s large-scale, automated machine learning and data science models for product and business applications. Prior to joining Premise, she spent five years at The Climate Corporation leading critical data science research efforts including parametric crop insurance development, nutrient recommendation systems, and science strategy. Moorea holds BS and MS degrees in Applied Mathematics from the University of Colorado at Boulder and received her PhD in Statistics from the University of California, Berkeley. In her free time Moorea can be found hiking and running in the beautiful Bay Area, and digging into books about the history of science.