Richard M. Golden
Professor
Program Head, Cognitive Science BS and Applied Cognition and Neuroscience MS
Research Interests
Computational Psychometrics, Statistical Machine Learning, Computational Cognitive Science
Curriculum Vitae
Contact
Email: [email protected]
Phone: 972-883-2423
Office:
GR_4.814
Campus Mail Code: GR41
Website: Cognitive Informatics and Statistics Lab and Golden Personal Web Page
Biography
Dr. Richard Golden’s current research focus lies in the multidisciplinary field of Computational Psychometrics. Computational Psychometrics integrates methods of psychometrics, learning science, usability engineering, mathematical statistics, and statistical machine learning. In particular, Dr. Golden is concerned with the development of new mathematical tools and models that quantitatively characterize what latent skills are learned by students and how those latent skills are learned. This research effort builds upon prior research threads summarized in his recently published text book Statistical Machine Learning: A unified framework.
Dr. Golden is currently a professor of cognitive science in the School of Behavioral and Brain Sciences and participating faculty member in electrical engineering in the Erik Jonsson School of Engineering and Computer Science. In addition, Dr. Golden is program head of the masters program in applied cognition and neuroscience and program head of the undergraduate program in cognitive science at The University of Texas at Dallas. In 1988, he received his M.S.E.E. in statistical pattern recognition and PhD in experimental psychology from Brown University. In 1987, Dr. Golden was an Andrew Mellon Fellow at the University of Pittsburgh Learning Research and Development Center. During the time period 1988-1990, Dr. Golden was an NIH postdoctoral scholar at Stanford University in the laboratory of Dr. David E. Rumelhart.
Recent and Selected Representative Publications
Books
Golden, Richard, M. (2020). Statistical Machine Learning: A unified framework. Texts in Statistical Sciences Series. CRC Press/Chapman-Hall. Book website: www.statisticalmachinelearning.com
Mathematical Methods for Neural Network Analysis and Design. Golden, R. M. (1996). MIT Press, Cambridge, MA. [419 pages].
Recent Peer-Refereed Book Chapters and Journal Articles
Golden, R. M.; Henley, Steven, S.; White, Halbert; Kashner, T. M. (2019). Consequences of model misspecification for maximum likelihood estimation with missing data. Econometrics, 7(3), 37.
Golden, Richard, M. (2018). Adaptive learning algorithm convergence in passive and reactive environments. Neural Computation, 30, no. 10, p. 2805-2832.
Golden, Richard M.; Henley, Steven S.; White, Halbert; Kashner, T. M. (2016). Generalized Information Matrix Tests for Detecting Model Misspecification. Econometrics 4, no. 4.