Richard Golden

Professor of Cognitive Science, School of Behavioral and Brain Sciences
Participating Faculty Member, Erik Jonsson School of Engineering and Computer Science
Program Head, Masters Program in Applied Cognition and Neuroscience
Program Head, Undergraduate Program in Cognitive Science
Tags: Cyberlearning Statistical Machine Learning Computational Cognitive Science Computational Psychometrics

Professional Preparation

National Institute of Health Postdoctoral Scholar - Computational Models of Human Comprehension
Stanford University - 1990
Andrew Mellon Postdoctoral Scholar - Computational Models of Human Comprehension
University of Pittsburgh LRDC - 1988
Ph.D. - Experimental Psychology (Computational Cognitive Science)
Brown University - 1987
M.S. - Electrical Engineering (Statistical Pattern Recognition)
Brown University - 1986
B.S. - Electrical Engineering (Communication Systems) and Psychology
University of California at San Diego - 1982

Research Areas

Computational Psychometrics
Dr. Golden's primary research focus is concerned with the development of new mathematical models that quantitatively characterize what latent skills are learned by students and how those latent skills are learned. This current research effort builds upon prior work concerned with selecting best-fitting models (Golden, 2000; Golden 2003; Golden et al. 2015; Golden, Nandy, and Patel, 2019; Golden, 2020, Chapter 16), prior work concerned with checking for flaws in a model's representation of reality (Golden et al. 2013Golden et al., 2016; Golden, 2020, Chapter 16), and the consequences of estimation and inference in possibly misspecified models with latent variables and missing data  (Golden et al., 2019).
Statistical Machine Learning
Dr. Golden's long-term research interests in this area have been concerned with the development of a formal unified probabilistic framework for interpreting inference and learning processes in machine learning algorithms (Golden, 1988a; Golden, 1988b; Golden, 1988c; Rumelhart et al., 1995; Golden 1996a; Golden, 1996b; Golden, 1997) as well as convergence analyses of machine learning algorithms (Golden, 1986; Golden, 1993; Golden, 2018).  Dr. Golden's new graduate computer science textbook Statistical Machine Learning: A unified framework (Golden, 2020) introduces and reviews such methods.
Computational Cognitive Science
Dr. Golden's long-term research interests in this area have focused upon the development and evaluation of formal models and theories of human comprehension.  Golden (1986) explored how bottom-up and top-down processing mechanisms might be learned from experience using a computational artificial neural network model. Golden and Rumelhart (1993), Golden et al. (1994), and Golden (1997) propose computational models of text recall and summarization. Golden (1994), Golden (1998), Jaynes and Golden (2003), and Goldman and Golden (2006) analyzed temporal structure in human story recall and summarization data using the Knowledge Digraph Contribution Analysis (KDC) analysis developed by Golden (1994) and Golden (1998). KDC analysis is based upon a psychometric model for generating recall and summarization data whose parameters represent the influence of different types of knowledge schemata represented as directed graphs. Durbin, Earwood, and Golden (2000) and Ghiasinejad and Golden (2013) developed and evaluated a hidden Markov model designed to interact with human coders to assist in the coding of free response data in text recall and text summarization experiments.

Publications

Making causal inferences about treatment effect sizes from observational datasets 2020 - Journal Article
Statistical modeling methods: challenges and strategies 2020 - Journal Article
Consequences of model misspecification for maximum likelihood estimation with missing data 2019 - Journal Article
Adaptive learning algorithm convergence in passive and reactive environments 2018 - Journal Article
Generalized information matrix tests for detecting model misspecification 2016 - Journal Article
Response to letter regarding "a systematic approach to subgroup analyses in a smoking cessation trial" 2016 - Journal Article
A systematic approach to subgroup analyses in a smoking cessation trial 2015 - Journal Article
Statistical Pattern Recognition 2015 - Book Chapter
Comparing clinical predictors of deep venous thrombosis versus pulmonary embolus after severe injury: A new paradigm for posttraumatic venous thromboembolism? 2013 - Journal Article
Modeling human coding of free response data 2013 - Journal Article

Appointments

Professor of Cognitive Science
University of Texas at Dallas [2004–Present]
Program Head, Applied Cognition and Neuroscience Graduate Program
University of Texas at Dallas [2000–Present]
Program Head, Undergraduate Cognitive Science Program
University of Texas at Dallas [2000–Present]
Participating Faculty Member in Electrical Engineering
University of Texas at Dallas [1999–Present]
Associate Professor of Cognitive Science
University of Texas at Dallas [1996–2004]
Assistant Professor of Cognitive Science
University of Texas at Dallas [1990–1996]

Awards

Invited Speaker for "Robotics, Artificial Intelligence, and Machine Learning: 2020 Challenges for Urological Research Symposium Series" - American Urological Association [2020]
Secretary-Treasurer Service Award (15 years) - Society for Mathematical Psychology [2017]
Invited Speaker for "New Strategies to Solve Analytic Challenges in Health Services Research" Workshop - Veteran's Administration Health Services Research and Development [2016]
Keynote Speaker - 34th Annual Meeting of the Society for Mathematical Psychology [2001]
IEEE Senior Member - Institute of Electrical and Electronics Engineers [1999]
Faculty Development Award to Study Econometrics with Professor Halbert L. White - University of Texas at Dallas [1998]
Keynote Speaker - 2nd Joint Mexico-US International Conference on Neural Networks and Neurocontrol [1997]

Projects

Supporting Classroom Learning Outcome Assessment using a Longitudinal Higher-Order Cognitive Diagnostic Model
2020/07 This project develops advanced statistical machine learning measurement methodologies specifically designed to support low-stakes classroom instruction by the typical classroom instructor for in person, hybrid, and online learning environments. A key feature of the proposed project is that the psychometric measurement tools estimate student mastery of instructor-specified course learning outcomes at various assessment time periods throughout the semester while simultaneously integrating information across all assessment time periods.

Additional Information

Editorial and Governing Boards
Editorial Board Member (Action Editor)Governing/Executive Board Member
Patents
  • Graphical User Interface for Automatic Coding of Free Response Data using Hidden Markov Model Methodology. Golden, R.M., Earwood, J., Durbin, M. A.  Patent No. US 7,188,064. Assignee: Board of Regents, The University of Texas System, Austin, TX.  April 12, 2002.
  • Adaptive Multiple Access Interference Suppression. Dowling, E. M., Jani, U. G., Wang, Z., Golden, R. M. U.S. Patent No. US 6,700,923 B1. Assignee: Board of Regents, The University of Texas System, Austin, TX. March 2, 2004.
  • Smart Antenna Multiuser Detector. Dowling, E. M., Jani, U., Golden, R. M., Wang, Z.U.S. Patent No. US 6,782,036 B1. Assignee: Board of Regents, The University of Texas System, Austin, TX. August 24, 2004.

Affiliations

Society for Mathematical Psychology
The Society for Mathematical Psychology is a multidisciplinary group of scientists from the fields of mathematics, psychology, neuroscience, cognitive neuroscience, artificial intelligence, mathematical statistics, and computational statistics with the common goal of using mathematical and computational methods to advance our understanding of the mind and brain.
Psychometric Society
The Psychometric Society is a multidisciplinary group of scientists from fields such as social science, behavioral science, education, mathematical statistics, and computational statistics with the common goal of using quantitative methods to advance quantitative measurement practices in the social, behavioral, and learning sciences.
Artificial Intelligence and Education
The International Artificial Intelligence in Education (AIED) Society is a multidisciplinary group of scientists from the fields of artificial intelligence, education, and psychology with the common goal of developing and evaluating new digital learning environments while advancing theory in the learning sciences, cognitive sciences, and Artificial Intelligence. Member of the International Alliance to Advance Learning in the Digital Era (IAALDE).

Funding

Assessing the Core Assumptions of Cognitive Diagnostic Knowledge Tracing
$10,000 - University of Texas at Dallas Office of Sponsored Projects [2019/06–2020/11]
The research proposed in this seed grant proposal will collect and analyze pilot data for testing core assumptions of the Cognitive Diagnostic Knowledge Tracing psychometric model.
Neuroscience-Inspired Unsupervised Deep Learning
$16,000 - Raytheon Technologies [2020/09–2020/12]
The purpose of this project is to research and develop a new unsupervised deep learning architecture inspired by neuroscience and evaluate the developed model(s) on moderately sized data sets.