Introduction

Dr. Lau earned his doctoral degree from Oxford University, and was a professor at Columbia University and the University of California, Los Angeles (UCLA). In 2021 he joined as the team leader of the RIKEN Center for Brain Science in Japan.

In his academic book ‘In Conscious We Trust’, published in 2022, Dr. Lau presented an original theory of consciousness perception through empirical research and theoretical cooperation. In recent years, studies such as the interpretation of whether machines such as artificial intelligence (AI) can be conscious like humans (‘17, Science) and the presentation of computational methodologies to evaluate metacognitive abilities have attracted attention. Based on these achievements, he won major awards in psychological science, including the William James Award in 2005 and the Janet Taylor Spence Award in 2012.

Dr. Lau joined IBS in September 2024, and set a goal to find fundamental reasons why the way humans experience the world is different from other animals. Specifically, to reveal why the development of the prefrontal cortex, one of the unknown regions of the brain, is particularly noticeable in humans compared to other animals and how its function contributes to perception, he plans to study a combination of non-invasive experimental methods that are safe for humans and advanced technologies that can be used in animal models such as rodents.

Selected Recent Publication

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.

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.

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