Center for Relativistic Laser Science

Cocoan lab (Computational Cognitive Affective Neuroscience Laboratory)

Introduction

The mission of our lab is to understand pain and emotions in the perspective of Computational, Cognitive, and Affective Neuroscience. We also aim to develop clinically useful neuroimaging models and tools that can be used and shared across different research groups and clinical settings.

Our main research tools include functional Magnetic Resonance Imaging (fMRI), psychophysiology measures (skin conductance, pupilometry, electrocardiogram, respiration), electroencephalogram (EEG), and other behavioral measures such as face recording camera, eye-tracker, etc. Most importantly, we use computational tools to model and understand our affective, cognitive, and behavioral responses.

Selected Recent Publication

Neurophotonics Lab

Introduction

We use light as a tool to understand and manipulate living biological system, aiming to address pressing problems in neuroscience. Our research theme includes but is not limited to intravital imaging techniques, optical neuromodulation, and bio-integrated photonics. We take multidisciplinary approaches integrating optics, engineering, and biomedicine.

Selected Recent Publications

1. Choi M, Choi JW, Kim S, Nizamoglu S, Hahn SK, Yun SH, "Light-guiding hydrogels for cell-based sensing and optogenetic synthesis in vivo", Nature Photonics 7(12): 987-994, 2013 (featured in Nature Photonics, Nature Methods, Nature Review of Endocrinology, Thomson Reuter, etc.).

2. Choi M, Ku T, Chung K, Yoon J, Choi C, "Minimally invasive molecular delivery into the brain using optical modulation of vascular permeability", PNAS 108(22): 9256-9261, 2011.

3. Kim JK*, Lee WM*, Kim P*, Choi M*, Jung K, Kim S, Yun SH, "Fabrication and operation of GRIN probes for in vivo fluorescence cellular imaging of internal organs in small animals",  Nature Protocols 7: 1456-1469, 2012 (*co-first author; cover article).

4. Choi M, Humar M, Kim S, Yun SH, "Step-index optical fiber made of biocompatible hydrogels",  Advanced Materials 27: 4081-4086, 2015.

5. Choi M, Lee WM, Yun SH, "Intravital microscopic interrogation of peripheral taste sensation",  Scientific Reports 5: 8661, 2015.

Sensorimotor Cognition Lab

Introduction

My lab is interested in how attention and cognition modulate sensory neural representation and transmission of the neural information to the downstream motor areas in the brain. I use in-vivo neurophysiological recording techniques, computational modeling, and sophisticated behavioral control for understanding neural codes in the cortical/subcortical regions using NHP as an animal model. The lab also pursues to address the same questions using brain imaging techniques, including EEG and fMRI, through collaboration with others in the center.

Selected Recent Publications

1. Joonyeol Lee and Stephen G. Lisberger, "Gamma synchrony predicts neuron–neuron correlations and correlations with motor behavior in extrastriate visual area MT", Journal of Neuroscience 33: 19677-19688, 2013. 

2. Joonyeol Lee, Mati Joshua, Javier F. Medina, and Stephen G. Lisberger, "Signal, Noise, and Variation in Neural and Sensory-Motor Latency", Neuron 90: 1-2, 2016.

3. Jin Yang*, Joonyeol Lee*, and Stephen G. Lisberger, "The interaction of Bayesian priors and sensory data and its neural circuit implementation in visually guided movement", Journal of Neuroscience 32: 17632–17645, 2012 *Equal contribution.

4. Joonyeol Lee and John H. R. Maunsell, "Attentional modulation of MT neurons with single or multiple stimuli in their receptive fields", Journal of Neuroscience, 30: 3058-3066, 2010.

5. Joonyeol Lee and John H. R. Maunsell, "A normalization model of attentional modulation of single unit responses", PLoS ONE, 4(2): e4651, 2009.

Lab Name:  Protein Design & Protein Materials Lab

Introduction

Our laboratory focuses on design and structural characterization of supramolecular protein assemblies that can be toward to make cellular and molecular therapies effective and practical approaches eventually to treat disease. Protein-based biomaterials designed by utilizing the tools of De Novo protein design (rational and computational designs) are used to study the mechanisms by which chemical or mechanical signals are sensed by cells and alter cell function. These biomaterials that interface with nanoscience are used to deliver drugs safely and efficiently; to prevent, detect, and treat disease; to assist the body as it heals; and to engineer functional tissues outside of the body for organ replacement. Our biomaterials are now designed rationally or computationally with controlled assembly structure and dynamic functionality to integrate with biological complexity and perform tailored, high-level functions in the body. Design of new biomaterials provides desirable cues in a variety of tissue engineering, immunotherapy and drug delivery to promote the regeneration or targeted destruction of tissues and organs in the body.

Selected Recent Publications

1. Yong Ho Kim, Jason E. Donald, Gevorg Grigoryan, George P. Leser , Alexander Y. Fadeev, Robert A. Lamb, and William F. DeGrado, “Capture and Imaging of the Pre-hairpin Intermediate in Viral Membrane Fusion of the Paramyxovirus PIV5”, Proceedings of the National Academy of Sciences 108: 20992-20997, 2011.

2. Gevorg Grigoryan*, Yong Ho Kim*, Rudresh Acharya, Kevin Axelrod, Rishabh M. Jain, Lauren Willis, Marija Drndic, James M. Kikkawa, and William F. DeGrado, “Computational Design of Virus-like Protein Assemblies on Carbon Nanotube Surfaces”, Science 332: 1071-1076, 2011 *- Authors contributed equally.

3. Ivan V. Korendovych, Yong Ho Kim, Andrew H. Ryan, James D. Lear, William F. DeGrado, and Scott J. Shandler, “Computational Design of a Self-Assembling β-Peptide Oligomer”, Organic Letters 12: 5142, 2010. 

4. Ivan V. Korendovych, Alessandro Senes, Yong Ho Kim, James D. Lear, H. Christopher Fry, Michael J. Therien, J. Kent Blasie, F. Ann Walker, and William F. DeGrado, “De Novo Design and Molecular Assembly of a Transmembrane Diporphyrin-Binding Protein Complex”, Journal of the American Chemical Society 132: 15516, 2010.

Lab Name: Medical Image Processing Lab

Introdution

Our lab focuses on developing novel data processing algorithms for neuroimaging. We are particularly interested in image registration, segmentation, and feature extraction for various medical imaging modalities. Neuroimaging data contain millions of voxels and thus robust algorithmic considerations are required to properly explore such high-dimensional data. We are witnessing exponential growth in accumulated data with advances in neuroimaging technology. Thus, the role of data post-processing will be an integral part of advanced neuroimaging research. We also have following research interests; 1) data mining for neuroimaging, 2) medical image analysis for age modeling and neurological disease, 3) medical image analysis for cancer management.

Selected Recent Publications

1. B. Park and H.Park, “Connectivity differences between adult male and female patients with attention deficit hyperactivity disorder according to resting-state fMRI”, Neural Regeneration Research, 2015.

2. B. Park, J. Seo, J. Yi, and H.Park, “Structural and functional brain connectivity of people with obesity and prediction of body mass index using connectivity”, PLoS ONE 10(11): e0141376. doi:10.1371/journal.pone.0141376, 2015.

3. S.-J. Choi*, J.-H. Kim*, J. Seo, H.-S. Kim, J.-M. Lee, and H.Park, “Parametric Response Mapping of Dynamic CT for Predicting Intrahepatic Recurrence of Hepatocellular Carcinoma after Conventional Transcatheter Arterial Chemoembolization”, European Radiology 26(1): 225-234, 2016. (* equal contribution)

4. H.Park, D. Wood, H. Hussain, C. Meyer, R. Shah, T. Johnson, T. Chenevert, and M. Piert, “Introducing Parametric PET/MR Fusion Imaging of Primary Prostate Cancer”, Journal of Nuclear Medicine 53: 546-551, 2012.

5. H.Park, P. H. Bland, and C. R. Meyer, “Construction of an Abdominal Probabilistic Atlas and its application in Segmentation", IEEE Transactions on medical imaging, 22: 483-492, 2003.

Lab Name: Visual Cognitive Neuroscience Lab

Introduction

Visual Cognitive Neuroscience Lab @ SKKU is a research lab investigating psychological and brain processes involved in perception, memory and cognitive control by measuring eye movements and EEGs with solid psychophysics. Currently, we study  dynamics of perceptual bistability, contextual memory retrieval, and motor inhibition.

Selected Recent Publications

1. Kang, M.-S., & Choi, J, "Retrieval-induced inhibition in short-term memory", Psychological Science, 26(7): 1014-1025, 2015.

2. Kang, M.-S., Hong, S. W., Blake, R., & Woodman, G.F, "Visual working memory contaminates perception", Psychonomic Bulletin & Review 18: 860-869, 2011.

3. Kang, M.-S., Blake, R., & Woodman, G.F, "Semantic analysis does not occur in the absence of awareness induced by interocular suppression", Journal of Neuroscience 31: 13535-13545, 2011.

4. Kang, M.-S., & Blake, R, "What causes alternations in dominance during binocular rivalry?"
, Attention, Perception & Psychophysics 72(1): 179-186, 2010.

5. Kang, M.-S., "Size matters: A study of the binocular rivalry dynamics", Journal of Vision 9(1): 17, 1-17, 2009.

Perceptual and Cognitive Neuroscience Lab (Shim Lab)
 

Introduction

The goal of our research is to understand how the human brain gives rise to perception and cognition, and specifically how top-down or feedback processing contributes to this process. Research focuses on how top-down processing serves to gate the entry of information into attention and memory, alter fundamental information about object location and identity, create new representations at early stages of processing where no feedforward information exists, and integrate information from multiple sensory modalities. In order to gain a comprehensive understanding of the cognitive and neural mechanisms that underlie human mental processes, including perception, attention, and memory we combine techniques from neuroimaging (encoding and decoding), vision sciences, and cognitive psychology. This allows us to explore how the brain represents and processes a range of perceptual and cognitive information.

Selected Recent Publication

1. Yu, Q., & Shim, W.  M. (2016). Modulating foveal representation can influence visual discrimination in the periphery. Journal of Vision, 16(3):15, 1-12.

2. Chong, E., Familiar, A., & Shim, W.  M. (2015). Reconstructing representation of dynamic visual objects in early visual cortex. PNAS, 113, 1453-1458.

3. Uddenberg, S., & Shim, W. M. (2015). Seeing the world through target-tinted glasses: Positive mood broadens perceptual tuning. Emotion, 15, 319-328.

4. Shim, W. M., Jiang, Y. V., & Kanwisher, N. (2013). Redundancy gains in retinotopic cortex. Journal of Neurophysiology, 110, 2227-2235.

5. Shim, W. M., Alvarez, G. A., Vickery, T. J., & Jiang, Y. V. (2010). The number of attentional foci and their precision are dissociated in the posterior parietal cortex. Cerebral Cortex, 20, 1342-1349.

Detail

High-resolution fMRI Lab

Introduction

My lab is developing acquisition and analysis methods for high-resolution fMRI in humans. In particular, we are trying to image mesoscopic human brain function using MRI at 7 Tesla. To that end, novel fMRI sequences are developed and tested, analysis pipeline developed, and physiological model of ascending vein effects are applied to the data to remove spatial bias in the fMRI signal. In addition, we are interested in quantitative MRI approaches at 7 Tesla to study subcortical brain organization and cortical brain parcellation in both healthy subjects and patients. Finally, the lab pursues modeling brain connectivity using physiological principles and advanced computational approaches.

Selected Recent Publication

1. J. Polimeni, K. Uludağ. Neuroimaging with Ultra-High Field MRI: Present and Future. NeuroImage 168, 1-532 (http://www.journals.elsevier.com/neuroimage/call-for-papers/neuroimaging-with-ultra-high-field-mri-present-and-future/, 2018, Publisher: Elsevier).

2. K. Uludağ, K. Ugurbil, L. Berliner. Functional MRI: From Nuclear Spins to Brain Function. (http://www.springer.com/gp/book/9781489975904, 2015, Publisher: Springer).

3. I. Marquardt, M. Schneider, O.F. Gulban, D. Ivanov, K. Uludağ. Cortical depth profiles of luminance contrast responses in human V1 and V2 using 7 T fMRI. Human Brain Mapping 39, 2812-27 (2018).

4. S. Kashyap, D. Ivanov, M. Havlicek, B. A. Poser, K. Uludağ. Impact of acquisition and analysis strategies on cortical depth-dependent fMRI. NeuroImage 168, 332-344 (2018).

5. K. Uludağ, P. Blinder. Linking brain vascular physiology to hemodynamic response in ultra-high field MRI. NeuroImage 168, 279-295 (2018).

6. M. Havlicek, A. Roebroeck, K. J. Friston, A. Gardumi, D. Ivanov, K. Uludağ. Physiologically informed dynamic causal models for fMRI. NeuroImage 122, 355-372 (2015).

Computational Brain Imaging and Network Modeling Lab
(COMBINE LAB)

Introduction

We are the research group of Computational Brain Imaging and Network Modeling (COMBINE) at IBS Center for Neuroscience Imaging Research (CNIR) and Sungkyunkwan University (SKKU) in South Korea. “COMBINE” is not a simply eye-catching acronym for the lab title but represents the main research perspective we are pursuing. Using diverse neuroimaging and computational modeling approaches, our research aims at identifying system-level principles for large-scale organization of the brain and its neurodynamics in both typical and atypcial conditions. In performing the research, we are seeking to combine multi-method (connectomics, computational modeling), multi-modal (structure and function), and multi-scale (circuit-level, large-scale network and behhaviors) analytical approaches to understand brain working principles and capture individual variations in complex behavioral and clinical outcomes. Based on these research tools, ultimately we are targeting to develop effective imaging-based biomarkers for normal cognition and clinical diagnosis.

Selected Recent Publications

1. Hong SJ, Vogelstein J, Gozzi A, Bernhardt BC, Yeo B.T.T, Milham MP, Di Martino A, Towards Neurosubtypes in Autism. Biological Psychiatry 2020 

2. Hong SJ, Vos de Wael R, Bethlehem R, Lariviere R, Paquola C, Valk SL, Di Martino A, Milham MP, Smallwood J, Margulies D, Bernhardt BC. Atypical functional connectome hierarchy in autism. Nature Communications. 2019, 10 (1):1022

3. Hong SJ, Lee HM, Gill RS, Bernhardt BC, Bernasconi N, Bernasconi A. A connectome-based mechanistic model of epileptogenic focal cortical developmental malformations. Brain. 2019, 142 (3):688-699

Nature-inspired Biomateria Lab

Introduction

Our main research scope is to design nature-inspired adhesive materials via catechol or gallol redox chemistry, potential application of which is cardiovascular and neural system. In detail, we have focused on developing a variety of adhesive biomedical formulations (i.e., hydrogels, particulates) exhibiting neuron repair, hemostatic effect, minimally invasive, hemostatic medical devices, adhesion/affinity-based drug-delivery carriers as well as 3D printable inks based on mussel-inspired catechol/its derivatives chemistry for wet-resistant adhesion. The ultimate goal of our research is to design a new generation of biomaterial-based practical medical tools capable of diagnosing and treating actual patients.​

Selected Recent Publications

1. Mikyung Shin et al. Nature Materials 2017, 16,147-152

HumAN Lab (Human Affective Neuroscience Laboratory)

Introduction

The overarching goal of our research is to understand the psychological and neurobiological mechanisms that underpin how we experience our own emotions and evaluate the emotions of others. Our lab examines how different aspects of affective information are encoded, manipulated, and integrated in the brain. We also investigate individual differences in such processes on both behavioral and neural levels, and their implications for mental health. We combine experimental psychology, multimodal neuroimaging (fMRI, dMRI), and computational tools to answer research questions pertaining to affective science.

Selected Recent Publications

1. Kim, M. J., Mattek, A. M., & Shin, J. (2020). Amygdalostriatal coupling underpins positive but not negative coloring of ambiguous affect. Cognitive, Affective, and Behavioral Neuroscience, 20, 949-960.

2. ​Kim, M. J., Farber, M. J., Knodt, A. R., & Hariri, A. R. (2019). Corticolimbic circuit structure moderates an association between early life stress and later trait anxiety. Neuroimage: Clinical, 24, 102050.

3. Kim, M. J., Mattek, A. M., Bennett, R. H., Solomon, K. M., Shin, J., & Whalen, P.J. (2017). Human amygdala tracks a feature-based valence signal embedded within the facial expression of surprise. Journal of Neuroscience, 37, 9510-9518.

4. Kim, M. J., Shin, J., Taylor, J. M., Mattek, A. M., Chavez, S. J., & Whalen, P. J. (2017). Intolerance of uncertainty predicts increased striatal volume. Emotion, 17, 895-899.

5. Kim, M. J., Gee, D. G., Loucks, R. A., Davis, F. C., & Whalen, P. J. (2011). Anxiety dissociates dorsal and ventral medial prefrontal cortex functional connectivity with the amygdala at rest. Cerebral Cortex, 21, 1667-1673.

6. Kim, M. J., & Whalen, P. J. (2009). The structural integrity of an amygdala-prefrontal pathway predicts trait anxiety. Journal of Neuroscience, 29, 11614-11618.

D.SON Lab

Introduction

Selected Recent Publications

1. "Multifunctional wearable devices for diagnosis and therapy of movement disorders"
Donghee Son+ and Dae-Hyeong Kim* et al. Nature Nanotechnology 9, 397 (2014)

2. "Wearable multiplexed array of silicon nonvolatile memory using nanocrystal charge confinement"
Donghee Son+ and Dae-Hyeong Kim* et al. Science Advances 2, e1501101 (2016)

3. "An integrated self-healable electronic skin system fabricated via dynamic reconstruction of nanostructured conducting network" Donghee Son+ and Zhenan Bao*et al. Nature Nanotechnology 13, 1057 (2018)

4. "Strain-sensitive stretchable self-healable semiconducting film for multiplexed skin-like sensor array"
Donghee Son+ and Zhenan Bao*et al. Science Advances 5, eaav3097 (2019)

5. "Adaptive self-healing electronic epineurium for chronic bidirectional neural interfaces"
Donghee Son*et al. Nature Communications 11, Article number: 4195 (2020)

Introduction

Neural Basis of Continuous Behavior (NBCB) lab aims to understand humans and animals' internal processes while making continuous and interactive decisions between multiple agents. We use human psychophysics, animal electrophysiology, and computational models to address our scientific question.

Specifically, we want to address normative behavior and neural dynamics of:

• prediction and planning

   

• information factorization and generalization across context

   

• social inference and learning

  by using the real-time navigation/foraging/hunting task paradigm.
  

We are open to incorporate methods from various fields, including artificial neural networks and computational ethology (but not limited to).

Selected Recent Publications

1. Yoo, S.B.M., Hayden, B.Y., and Pearson, J.M. (2021). Continuous decisions. Philosophical Transactions Royal Soc B 376, 20190664.

2. Yoo, S. B. M., Tu, J. C., & Hayden, B. Y. Multicentric tracking of multiple agents by anterior cingulate cortex during pursuit and evasion. Nature Communication (2021, accepted)

3. Yoo, S.B.M., Tu, J.C., Piantadosi, S.T., and Hayden, B.Y. (2020). The neural basis of predictive pursuit. Nature Neurosci 23, 252–259.

4. Yoo, S.B.M., and Hayden, B.Y. (2020). The Transition from Evaluation to Selection Involves Neural Subspace Reorganization in Core Reward Regions. Neuron 105, 712-724.e4.

5. Yoo, S.B.M., and Hayden, B.Y. (2018). Economic Choice as an Untangling of Options into Actions. Neuron 99, 434–447.

Neural Reinforcement Learning Lab (NeuRLab)

Introduction

Living in an uncertain environment, we desire to pursue good things and to avoid bad things. We are interested in how the brain recognizes different situations and learns to make better decisions. Related questions are: How does the brain represent reward or punishment? How does the brain remember something good and pursue it? How does the brain choose one action out of multiple options? What makes one animal more intelligent than another animal? What can we learn about how the brain works from artificial intelligence?

Reinforcement learning (RL) theory provides theoretical and computational frameworks to these problems. Interestingly, it has been shown that dopamine activity in the brain resembles the teaching signal in one of reinforcement learning theories, temporal difference (TD) learning. However, the detailed neural mechanisms of adaptive behaviors remain elusive. We perform experiments using animals and analyze data using computational models derived from artificial intelligence (AI) to understand the biological mechanisms of reinforcement learning.

Selected Recent Publications

1. Kim HR*, Malik AM*, Mikhael JG, Bech P, Tsutsui-Kimura I, Sun F, Zhang Y, Li Y, Watabe-Uchida M, Gershman SJ, Uchida N (2020) A unified framework for dopamine signals across timescales. Cell (lead author)

2. Kim HR, Angelaki DE, DeAngelis GC (2017) Gain Modulation as a Mechanism for Coding Depth from Motion Parallax in Macaque Area MT. Journal of Neuroscience 37 (34), 8180-8197

3. Kim HR, Angelaki DE, DeAngelis GC (2015) A novel role for visual perspective cues in the neural computation of depth. Nature Neuroscience 18(1), 129-137.

Computational Learning & Memory Neurosciece Lab

(CLMN Lab)

Research interest

·  Computational modeling of human movement control, learning, and memory

·  Neuroscientific approach to modulating human learning & memory with non-invasive brain stimulation

·  Brain inspired artificial intelligence (Reverse engineering the brain to understand learning and memory)

·  Cognitive and neural mechanisms underlying decision making in the framework of reinforcement learning

Selected Recent Publications

1.     Choi Y, Shin EY, Kim S*. Spatiotemporal dissociation of fMRI activity in the caudate nucleus underlies human de novo motor skill learning. Proceedings of National Academy of Sciences U. S. A., Vol. 117, Issue 38, 2020

2.   Kim S, Nilakantan AS, Hermiller MS, Palumbo R, VanHaerents SA, Voss JL*. Selective and coherent activity increases due to stimulation indicate functional distinctions between episodic memory networks. Science Advances, Vol. 4, Issue 8, 2018

3.   Kim S, Ogawa K, Lv J, Schweighofer N*, Imamizu H. Neural substrates related to motor memory with multiple time scales in sensorimotor adaptation. PLoS Biology, Vol. 13, Issue 12, 2015 

4.     Kim S, Callier T, Tabot GA, Gaunt RA, Tenore FV, Bensmaia SJ*. Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex. Proceedings of National Academy of Sciences U. S. A., Vol. 112, Issue 49, 2015

Detail

CAMIN lab (Computational Analysis for Multimodal Integrative Neuroimaging)

Introduction

We analyze multimodal brain MR imaging data using advanced connectomics and machine learning techniques to establish novel frameworks assessing multiscale brain organization. In particular, we aim to assess large-scale brain organization as well as structure-function coupling during typical and atypical development and young adults. Leveraging histology and imaging-genetics approaches, we provide biological underpinnings to the imaging findings. Methodologically, we study data mining, multimodal integration, and classification/prediction for big data.

Selected Recent Publications

1. B.-y. Park et. al., “Topographic divergence of atypical cortical asymmetry and atrophy patterns in temporal lobe epilepsy”, Brain, 2021.

2. B.-y. Park, H. Park, F. Morys, M. Kim, K. Byeon, H. Lee, S.-H. Kim, S. Valk, A. Dagher, B. C. Bernhardt, “Inter-individual body mass variations relate to fractionated functional brain hierarchies”, Communications Biology, 4:735, 2021.

3. B.-y. Park, S.-J. Hong, S. Valk, C. Paquola, O. Benkarim, R. A. I. Bethlehem, A. Di Martino, M. Milham, A. Gozzi, B. T. T. Yeo, J. Smallwood, and B. C. Bernhardt, “Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism”, Nature Communications, 12:2225, 2021.

4. B.-y. Park, R. A. I. Bethlehem, C. Paquola, S. Larivière, R. Rodríguez-Cruces, R. Vos de Wael, E. T. Bullmore, B. C. Bernhardt, “An expanding manifold in transmodal regions characterizes adolescent reconfiguration of structural connectome organization”, eLife, 10:e64694, 2021.

5. B.-y. Park, R. Vos de Wael, C. Paquola, S. Larivière, O. Benkarim, J. Royer, S. Tavakol, R. Rodríguez-Cruces, Q. Li, S. L. Valk, D. S. Margulies, B. Mišić, D. Bzdok, J. Smallwood, and B. C. Bernhardt, “Signal diffusion along connectome gradients and inter-hub routing differentially contribute to dynamic human brain function”, NeuroImage, 224:117429, 2021.

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.

Recent Updates

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