Hayoung Song, Ph.D., has completed her post-doctoral fellowship through the Center for Theoretical & Computational Neuroscience (CTCN) at Washington University, and accepted a faculty position at the University of Texas at Austin beginning in August 2026.
This fall, as an assistant professor of psychology, her focus will be on starting her own research lab and teaching research methods and statistics to psychology majors. She is looking forward to both building on research from her time with CTCN, and teaching undergraduate students – especially since it was a neuroscience course that initially inspired her love for the field.
“I would love to have the chance to teach cognitive neuroscience,” Song said. “I was so fascinated that the brain—a mushy organ underneath the skull—is what generates our intricate mind and complex behavior. I would feel very privileged if my classes sparked a similar fascination in someone.”
Song joined the CTCN two years ago to work with ShiNung Ching, Ph.D., Chair and Professor of Electrical & Systems Engineering, Zachariah Reagh, Ph.D., Associate Professor of Psychological & Brain Sciences, and Jeffrey Zacks, Ph.D., Chair and Professor of Psychological & Brain Sciences, to better understand how neural dynamics give rise to cognitive experience.
“CTCN fellows are expected to operate more independently and broadly than a conventional postdoc,” said Geoffrey Goodhill, Ph.D., Professor of Neuroscience and Developmental Biology and Director of the CTCN. “By having at least two mentors spanning disciplinary interfaces, fellows receive broader training and can explore a broader set of scientific perspectives than is typically possible within one laboratory. Hayoung has exemplified this mission, and we’re very proud of what she has achieved as a CTCN Fellow”
Song’s mentors are highly complimentary of her skillset, and believe her greatest assets moving into her role as faculty will be her curiosity, propensity to ask good questions, and attention to detail.
“She is a fiercely curious person, and she has a real drive to know more about any topic that interests her, which makes brainstorming sessions with her a real treat,” said Reagh. “And – she’s incredibly driven to do what needs to be done, and to do it properly. She doesn’t cut corners. She’s all about doing the best possible work that she can to arrive at the most confident answer possible.”
Her post-doctoral work focused on building neural network models to exhibit human-like behavior and reverse-engineering them to better understand how the human brain works. Specifically, her work on episodic memory found that models could recall context congruent and casually related past events in a similar way that people could, despite the model having no specific training to do this.
“This suggests that causal inference and contextual learning—both hallmarks of intelligent behavior—can emerge from the episodic memory process,” Song said.
She plans to build on this work in her lab at UT Austin. She said her time as a fellow with CTCN provided her with the skillset that allowed her to not only understand what the human brain does during memory retrieval, but also how this process takes place. Looking forward, Song is especially excited to learn how to best incorporate AI tools to continue to reverse engineer human intelligence.
“CTCN took a chance on my proposal and secured me the independence and mentorship to pursue research directions I was passionate about,” Song said. “My mentors have all been incredibly supportive.”