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Department Faculty

Woo-Young Ahn

Woo-Young (Young) Ahn earned his B.S. in materials science & engineering in 2002 from Seoul National University and then went to Harvard University as a doctoral candidate for applied physics and received his S.M. in applied physics in 2003. Due to his interests in the human mind, he decided to change his major to clinical psychology so that he could study the human mind from multiple perspectives. He continued on to receive his M.A. in clinical psychology from Seoul National University in 2006, and his Ph.D. in clinical psychology from Indiana University, Bloomington in August 2012. He completed his clinical psychology internship at the University of Illinois at Chicago (UIC) in June 2012. He worked then as a postdoc for two years at Virginia Tech Carilion Research Institute (VTCRI) and for a year at Virginia Commonwealth University.

In August 2015, he joined the faculty in the Department of Psychology at the Ohio State University as an assistant professor.

Research interests

The mission of the Computational Clinical Science (CCS) Laboratory is to develop cost-effective markers of psychiatric disorders, especially addictive disorders, which can be readily translated into clinical practice. We believe developing such objective and affordable markers will innovate prevention and treatment programs and will have enormous implications for mental health and clinical practice. To accomplish this mission, We seek to identify the risk/protective factors of the psychiatric conditions, to understand their underlying brain mechanisms, and to use cutting-edge statistical algorithms to make accurate predictions based on these findings.

Specifically, we use decision neuroscience as a framework to understand both normative and abnormal behavior, computational modeling to delineate the cognitive processes responsible for decision-making deficits, and neuroimaging (fMRI and EEG) methods to probe their neural substrates. In collaboration with our colleagues, we aim to develop a quick and standardized battery that can predict clinical outcomes using machine-learning algorithms. The battery will combine behavioral, brain (functional and structural), clinical, and genetic measures.

We are currently recruiting graduate students and a post-doc. Please visit the lab’s website ( and/or email Dr. Ahn ( for more information.

Selected Publications

Ahn, W.-Y. & Busemeyer, J. R. (2016) Challenges and promises for translating computational tools into clinical practice. Current Opinion in Behavioral Sciences. 11, 1-7.

Ahn, W.-Y., Haines, N., & Zhang, L. (2016). Revealing neuro-computational mechanisms of reinforcement learning and decision-making with the hBayesDM package. bioRxiv.

Ahn, W.-Y. & Vassileva, J. (2016) Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence. Drug and Alcohol Dependence, 161 (1), 247–257.

Ahn, W.-Y.*, Ramesh*, D., Moeller, F. G., & Vassileva, J. (2016) Utility of machine learning approaches to identify behavioral markers for substance use disorders: Impulsivity dimensions as predictors of current cocaine dependence. Frontiers in Psychiatry, 7: 34.

Ahn, W.-Y., Dai, J., Vassileva, J., Busemeyer, J. R., & Stout, J. C. (2016) Computational modeling for addiction medicine: From cognitive models to clinical applications, 224, 53–65. In Ekhtiari, H. & Paulus, M. (Eds.), Progress in Brain Research: Neuroscience for Addiction Medicine: From Prevention to Rehabilitation. Elsevier. [Link]

Rass, O., Ahn, W.-Y., O’Donnell, B. F. (2016) Resting-state EEG, Impulsiveness, and Personality in Daily and Nondaily Smokers. Clinical Neurophysiology, 127(1), 409–418.

Ahn, W.-Y., Kishida, K. T., Gu, X., Lohrenz, T., Harvey, A. H., Alford, J. R., Smith, K. B., Yaffe, G., Hibbing, J. R., Dayan, P., & Montague, P. R. (2014) Nonpolitical images evoke neural predictors of political ideology. Current Biology, 24(22), 2693-2599.

Ahn, W.-Y., Vasilev, G., Lee, S., Busemeyer, J. R., Kruschke, J. K., Bechara A., & Vassileva, J. (2014) Decision-making in stimulant and opiate addicts in protracted abstinence: evidence from computational modeling with pure users. Frontiers in Decision Neuroscience, 5:849.

Ahn, W.-Y., Rass, O., Fridberg, D. F., Bishara, A. J., Forsyth, J. K., Breier, A., Busemeyer, J. R., Hetrick, W. P., Bolbecker, A. R., & O’Donnell, B. F. (2011) Temporal discounting of rewards in patients with bipolar disorder and schizophrenia. Journal of Abnormal Psychology, 120(4), 911-921.

Ahn, W.-Y., Krawitz, A., Kim, W., Busemeyer, J. R., & Brown, J. W. (2011) A model-based fMRI analysis with hierarchical Bayesian parameter estimation. Journal of Neuroscience, Psychology, and Economics, 4(2), 95-110.

Ahn, W.-Y., Busemeyer, J. R., Wagenmakers, E.-J., & Stout, J. C. (2008) Comparison of decision learning models using the generalization criterion method. Cognitive Science, 32(8), 1376-1402.