National Institute of Health Postdoctoral Scholar - Computational Models of Human Comprehension
Stanford University - 1990
Richard Golden
Program Head, Cognitive Science BS and Applied Cognition and Neuroscience MS
Professor
Professor of Cognitive Science
Participating Faculty Member in Electrical Engineering and Computer Science
Research Interests: Computational psychometrics, learning sciences, and statistical machine learning.
972-883-2423
GR 4.814
COINS Lab
Personal Home Page
Curriculum Vitae
ORCID
Currently accepting undergraduate and graduate students
Professional Preparation
Andrew Mellon Postdoctoral Scholar - Computational Models of Human Comprehension
University of Pittsburgh LRDC - 1988
University of Pittsburgh LRDC - 1988
Ph.D. - Experimental Psychology (Computational Cognitive Science)
Brown University - 1987
Brown University - 1987
M.S. - Electrical Engineering (Statistical Pattern Recognition)
Brown University - 1986
Brown University - 1986
B.S. - Electrical Engineering (Communication Systems) and Psychology
University of California at San Diego - 1982
University of California at San Diego - 1982
Research Areas
Computational Psychometrics
The current research focus of Dr. Golden's lab is Computational Psychometrics. Computational psychometrics is a new multidisciplinary field which integrates methods from areas such as Artificial Intelligence, psychometrics, learning science, and cognitive science to develop improved methods for measuring psychological processes and representations.A fundamental component to the field of Psychology is "measurement" achieved through behavioral task measures (e.g., accuracy, response time, and eye tracking) and biometric measures (e.g., fMRI, EEG and fNIRS). Psychometric methods support such measurement methodologies by developing explicit mathematical models of not only mental representations and processes but additionally by developing explicit mathematical models of the measurement process. Computational Psychometrics extends classic psychometric methodologies by incorporating advanced methods from the fields of machine learning, artificial intelligence, and mathematical statistics. In addition, Computational Psychometrics has an important applied component which supports technology-enhanced personalized educational and clinical assessment and feedback.
Currently, Dr. Golden's Computational Psychometrics lab (https://labs.utdallas.edu/coinslab) is concerned with the development, evaluation, and scaling of Probabilistic Graphical Diagnostic Classification Models (for a review of this area see the National Council on Measurement in Education (NCME) presentation by Golden et al., 2025) for the purpose of developing quantitative models of both human knowledge and quantitative models of knowledge assessment tasks.
Statistical Machine Learning and Artificial Intelligence
Dr. Golden's long-term research interests in Artificial Neural Networks and Statistical Machine Learning 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.Estimation and Inference in the Possible Presence of Model Misspecification and Parameter Redundancy
In many science and engineering fields, semantically interpretable models with interpretable parameter values play a critical role. However, in reality no model is perfect and so methods are required so that inferences drawn from such models are robust in the presence of possible model misspecification. Dr. Golden's research in this area includes the development of new statistical test methodologies for comparing competing and possibly non-nested probability models in the presence of possible misspecification (e.g., Golden, 2003) and new methodologies for the detection of model misspecification (e.g., Golden et al., 2019). In addition, if some parameter redundancy is present, then methods for properly handling the effects of parameter redundancy are required for the purpose of understanding how to properly interpret the parameter values. Dr. Golden is current working on methods for supporting maximum likelihood estimation and inference in the possible presence of parameter redundancy and model misspecification (Golden, 2024).Publications
Making causal inferences about treatment effect sizes from observational datasets 2020 - Journal Article
Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data 2019 - Journal Article
Consequences of model misspecification for maximum likelihood estimation with missing data 2019 - Journal Article
Making causal inferences about treatment effect sizes from observational datasets 2019 - Journal Article
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]
Appointments
Professor of Cognitive Science
University of Texas at Dallas [2004–Present]
University of Texas at Dallas [2004–Present]
Program Head, Applied Cognition and Neuroscience Graduate Program
University of Texas at Dallas [2000–Present]
University of Texas at Dallas [2000–Present]
Program Head, Undergraduate Cognitive Science Program
University of Texas at Dallas [2000–Present]
University of Texas at Dallas [2000–Present]
Participating Faculty Member in Electrical Engineering
University of Texas at Dallas [1999–Present]
University of Texas at Dallas [1999–Present]
Associate Professor of Cognitive Science
University of Texas at Dallas [1996–2004]
University of Texas at Dallas [1996–2004]
Assistant Professor of Cognitive Science
University of Texas at Dallas [1990–1996]
University of Texas at Dallas [1990–1996]
Additional Information
Editorial and Governing Boards
Editorial Board Member (Action Editor)- 1996-2011, Journal of Mathematical Psychology
- 1995-2006, Neural Networks
- 2001-2004, International Journal of Applied Intelligence
- 1999-2004, Neural Processing Letters.
- 2002-2017 Society for Mathematical Psychology (Secretary-Treasurer)
- 1996-2004 Society for Text and Discourse which is a member of the International Alliance to Advance Learning in the Digital Era (IAALE)
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.