I am a brand-new Assistant Professor at Sungkyunkwan University (SKKU) in South Korea, studying how the brain generates complex and intelligent behaviors. I am affiliated with the Institute for Basic Science (IBS) - Center for Neuroscience Imaging Research and the Department of Biomedical Engineering.
Previously, I was a postdoctoral associate/research scientist at MIT, working with Mehrdad Jazayeri, and at Yale, working with Daeyeol Lee. I obtained my Ph.D. in neuroscience from Seoul National University, mentored by Sang-hun Lee, and my master’s/undergrad from KAIST, mentored by Jaeseung Jeong.
My area of research is cognitive and systems neuroscience. I have been investigating how the brain measures and processes time using multiple approaches: behavioral experiments, computational modeling (e.g., Bayesian theory), human neuroimaging (EEG/fMRI), and electrophysiology in non-human primates. In my new lab, I will combine these techniques to study how the prefrontal and posterior parietal cortices process information about magnitude (time, number, and space).
In my spare time (if I have any!), I enjoy spending time with my daughters outdoors (camping,skiing) and would love to adopt a dog.
February 2023: I start my own lab at Sungkyunkwan University (SKKU), Department of Biomedical Engineering & Institute for Basic Science - Center for Neuroscience Imaging Research
January 2023: Manuel & Nico’s work titled “Parametric control of flexible timing through low-dimensional neural manifolds”, which I am a part of, is published in Neuron
November 2022: Reza & Andrew’s work titled “A large-scale neural network training framework for generalized estimation of single-trial population dynamics”, which I am a part of, is published in Nature Methods
October 2022: Jason’s work that I mentored is accepted as an oral presentation in NeurIPS workshop
October 2021: My review paper with Devika titled “Neural implementations of Bayesian inference” is published in Current Opinion in Neurobiology
June 2021: My work titled “Validating model-based Bayesian integration using prior–cost metamers” is published in PNAS