Fernando Galindo-Rueda leads the science, technology, and innovation indicators unit in the Economic Analysis and Statistics Division of OECD’s Directorate for Science, Technology, and Industry. He is responsible for supporting the work of the OECD Working Party of National Experts on Science and Technology Indicators where he coordinates the upkeep and further development of the Frascati family of internationally adopted statistical standards for the measurement of research and development, innovation, and technology and the publication of OECD publications in this area. Prior to joining the OECD, he was deputy director of business economics at the United Kingdom’s Department of Economic Analysis. Previously, he also held positions in the UK Office for National Statistics and at the London School of Economics. He has a Ph.D. in economics from University College, London.
Christopher T. Hill is a professor in the School of Public Policy at George Mason University. His primary interests are in the history, design, evaluation, and politics of federal policies and programs intended to stimulate technological innovation in the commercial marketplace. He previously served as vice provost for research at George Mason University and held senior positions at the RAND Corporation, the National Research Council, the Congressional Research Service, the Massachusetts Institute of Technology, and the U.S. Office of Technology Assessment.
Joel L. Horowitz is the Charles E. and Emma H. Morrison professor of economics in the Department of Economics at Northwestern University. Prior to this position, he was on the faculty of the Department of Economics at the University of Iowa and a senior operations research analyst for the U.S. Environmental Protection Agency. He is a fellow of the Econometric Society and of the American Statistical Association and an elected member of the International Statistical Institute. His areas of interest are nonparametric and semiparametric methods. He has a B.S. in physics from Stanford University and a Ph.D. in physics from Cornell University.
David Newman is an associate research faculty member in the Department of Computer Science of the University of California, Irvine. His research interests are in machine learning, topic modeling, and text mining. He has received a Google Research Award for his work in topic mapping. He has a Ph.D. from Princeton University.
Stephanie S. Shipp is a research staff member at the Science and Technology Policy Institute at the Institute for Defense Analyses in Washington, DC. She specializes in the assessment of science and technology projects, programs, and portfolios. Her research involves innovation and competive-