brandon-turner_000Brandon M. Turner is an Associate Professor in the Psychology Department at The Ohio State University. He received a B.S. from Missouri State University in mathematics and psychology in 2008, a MAS in statistics from The Ohio State University in 2010, and a Ph.D. from The Ohio State University in 2011. He then spent one year as a postdoctoral researcher at University of California, Irvine, and two years as a postdoctoral fellow at Stanford University. His research interests include dynamic models of cognition and perceptual decision making, efficient methods for performing likelihood-free and likelihood-informed Bayesian inference, and unifying behavioral and neural explanations of cognition. His current focus is on understanding how external factors such as the environment, and internal factors such as working memory interact to shape an observer’s perception of the world, and ultimately how this perception drives their decisions. Vita [@ 1/20/2020]


Emily Weichart is a postdoctoral researcher. She received a B.S. in Psychology in 2013 and an M.A. in Cognitive Psychology in 2017 from The Ohio State University. She completed her Ph.D. at the University of Virginia in 2020, working in the Computational Memory Lab. She has a background in model-based approaches to cognitive assessment and investigating the cognitive effects of clinical treatments. Her recent work has explored time-varying mechanisms of decision making during perceptual tasks, with the broader goal of understanding the complex dynamics that connect visual attention, cognitive control, and decision making processes.

Joyce_photoWenjia Joyce Zhao is a postdoctoral researcher in the Psychology Department. Together with Ian Krajbich and Brandon Turner, she uses computational modeling and process-tracing methods to understand cognitive processes underlying decision making. She received a B.Sc. in Psychology and Economics from Tsinghua University, an M.Sc. in Psychology from the University of Oxford, and an M.A. in Statistics from the University of Pennsylvania. She completed her Ph.D in the computational behavioral science lab at the University of Pennsylvania. Vita [@ 12.14.2020]


Giwon Bahg is a fourth-year doctoral student. He received a B.A. in Psychology and Philosophy in 2013, and an M.A. in Psychology in 2015 from Seoul National University. He is interested in computational modeling, Bayesian methods, and temporal dynamics of human cognition, particularly in the context of thinking processes (e.g., categorization, reasoning, decision-making). His current work aims to implement adaptive design optimization for fMRI experiments using a joint modeling approach. He is also investigating dynamics of internal representations in decision-making, as well as its neural and computational bases.

IMG_5883 2Inhan Kang is a third-year doctoral student in the Quantitative Psychology program. He graduated from Seoul National University with a B.A. in Psychology and a B.S. in Statistics in 2014 and an M.A. in Quantitative Psychology in 2016. His research interests include the generalized latent variable modeling and its extension to cognitive modeling and neuroscience, mathematical modeling, Bayesian analysis, and stochastic processes. His current focus is on joint modeling of different data modalities and statistical methods to achieve a parsimonious explanation of complex brain-behavior connections.


Matthew Galdo is a second-year graduate student. He graduated from Ohio State University in 2018 with a B.S. in Neuroscience. Now, he is working toward a Ph.D. in Cognitive Psychology and a MAS degree in Statistics. His research interests include: the dynamics of decision making, in particular how abnormal patterns of decision behavior (e.g. addiction) and their underlying mechanisms evolve over time; merging cognitive and psychiatric theory; Bayesian statistical methodology; and the link between neural data and cognition. Currently, his main focus is exploring how connectome data can interface with joint models of neural and behavioral data.


Daniel G. Evans is a first year doctoral student in the Cognitive Neuroscience program at The Ohio State University. He received a B.A. in Psychology in 2016 from Butler University. After graduating, he spent four years as a research assistant at George Washington University and The Ohio State University. His research background combines a breadth of topics (including attention, memory, linguistics, cognitive control, and multiple sclerosis) with behavioral, eye-tracking, and fMRI methods. The overarching theme of his research is to investigate the neural basis of how attention interacts with other cognitive processes (e.g., decision-making, learning, object tracking). Currently, he is using simultaneous eye-tracking and fMRI to develop computational models of hierarchical category learning. In his spare time, Dan enjoys playing hockey, video games, and brewing beer.

255398846_1089844095085878_3718662101473808554_nNicole C. King is a first-year doctoral student at The Ohio State University. She received her B.A. from Rutgers University – New Brunswick majoring in Psychology and minoring in Statistics. She is now working toward a Ph.D. in Cognitive Neuroscience and a MAS degree in Statistics. Her research background includes prior knowledge in the form of scripted events, episodic memory, agency, intentional binding, decision making, and attention. Currently she is investigating the structure and relation of memory in category learning tasks. In her free time, she loves to read, play music, explore amusement parks, and practice yoga.

IMG_3823Ryan is a first-year graduate student in the cognitive neuroscience program working towards a PhD and a MAS. He graduated from Ohio State University in 2020 with a B.S. in psychology, and spent a year as a research assistant before starting graduate school in 2021. His research interests include computational modeling, Bayesian methods, reinforcement learning, and artificial intelligence. His current research focuses on how to incorporate the world of neuroscience into the field of AI using realistic models of the human brain.