Core AI | 서울대학교AI연구원(AIIS)

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RESEARCH

Core AI

AIIS (Artificial Intelligence Institute at Seoul National University) is an intercollegiate research institute
committed to integrating and supporting AI-related research. As a hub of AI research both in Core AI
and X+AI areas, researchers of diverse disciplines collaborate through AIIS.

Brain & Mind

AIIS is building bridges to the next-generation of AI by adopting cognitive science research methodologies.
Our engineers and brain scientists work together to measure, interpret and modulate the activity patterns of neural networks
in the hippocampus and prefrontal cortex for learning and memory.

Cha, Jiwook Department of Psychology

  • Research Lab SNU Connectome Lab
  • Research Area (Core AI)Learning & Reasoning, Brain & Mind
  • Research Area (X+AI)Neuroscience, Bio, Humanities/Social Sciences, Medicine,Brain

대표논문

"The sexual brain, genes, and cognition: A machine-predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children." Human Brain Mapping
"Association of Genome-wide Polygenic Scores for Multiple Psychiatric and Common Traits Identify Preadolescent Youth with Risk for Suicide." JAMA Network Open
"Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth." Human Brain Mapping
"Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data." NPJ Digital Medicine
"Diagnosis and prognosis of Alzheimer's disease using brain morphometry and white matter connectomes." Neuroimage-Clinical
"Associations between brain structure and connectivity in infants and exposure to selective serotonin reuptake inhibitors during pregnancy. " JAMA pediatrics
"The Effects of Obstructive Sleep Apnea Syndrome on the Dentate Gyrus and Learning and Memory in Children.'" The Journal of Neuroscience
"Clinically anxious individuals show disrupted feedback between inferior frontal gyrus and prefrontal-limbic control circuit."  Journal of Neuroscience
"Neural correlates of aggression in medication-naive children with ADHD: multivariate analysis of morphometry and tractography."  Neuropsychopharmacology
"Hyper-reactive human ventral tegmental area and aberrant mesocorticolimbic connectivity in overgeneralization of fear in generalized anxiety disorder."  Journal of Neuroscience
"Circuit-wide structural and functional measures predict ventromedial prefrontal cortex fear generalization: implications for generalized anxiety disorder."  Journal of Neuroscience

문태섭 Department of Electrical and Computer Engineering

  • Research Lab M.IN.D (Machine INtelligence and Data science) Lab
  • Research Area (Core AI)Learning & Reasoning, Vision & Perception, Data Intelligence, Brain & Mind, AI Law & Ethics
  • Research Area (X+AI)Brain, Bio, Energy

대표논문

Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization. Sangwon Jung, Taeeon Park, Sanghyuk Chun, and Taesup Moon. The 11th International Conference on Learning Representations (ICLR), May 2023

Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks. Hongjoon Ahn, Youngyi Yang, Quan Gan, David Wipf, and Taesup Moon. Neural Information Processing Systems (NeurIPS), December 2022

GRIT-VLP: Grouped Mini-batch Sampling for Efficient Vision and Language Pre-training. Jaeseok Byun, Taebaek Hwang, Jianlong Fu, and Taesup Moon. European Conference on Computer Vision (ECCV), October 2022

Learning Fair Classifiers with Partially Annotated Group Labels. Sangwon Jung, Sanghyuk Chun, and Taesup Moon. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022

SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning. Sungmin Cha, Beomyoung Kim, Youngjoon Yoo, and Taesup Moon. Neural Information Processing Systems (NeurIPS), December 2021

SS-IL: Separated Softmax for Incremental Learning. Hongjoon Ahn, Jihwan Kwak, Subin Lim, Hyeonsu Bang, Hyojun Kim, and Taesup Moon. International Conference on Computer Vision (ICCV), October 2021

Fair Feature Distillation for Visual Recognition. Sangwon Jung, Donggyu Lee, Taeeon Park, and Taesup Moon. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021

FBI-Denoiser: Fast Blind Image Denoiser for Poisson-Gaussian Noise. Jaeseok Byun, Sungmin Cha, and Taesup Moon. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021

Continual Learning with Node-Importance based Adaptive Group Sparse Regularization. Sangwon Jung, Hongjoon Ahn, Sungmin Cha, and Taesup Moon. Neural Information Processing Systems (NeurIPS), December 2020

Uncertainty-based continual learning with adaptive regularization. Hongjoon Ahn, Sungmin Cha, Donggyu Lee and Taesup Moon. Proceedings of Neural Information Processing Systems (NeurIPS), December 2019

Fooling neural network interpretations via adversarial model manipulation. Juyeon Heo, Sunghwan Joo, and Taesup Moon. Proceedings of Neural Information Processing Systems (NeurIPS), December 2019
  • Research Lab 바이오인텔리전스 랩
  • Research Area (Core AI)Learning & Reasoning,Brain & Mind,Language & Cognition,Language & Cognition
  • Research Area (X+AI)Humanities/Social Sciences,Bio,Brain

대표논문

Answerer in questioner's mind: Information theoretic approach to goal-oriented visual dialog, S.-W. Lee, Y.-J. Heo, B.-T. Zhang, The 32nd Annual Conference on Neural Information Processing Systems (NIPS 2018), (Spotlight)
Bilinear attention networks, J.-H. Kim, J. Jun, B.-T. Zhang, The 32nd Annual Conference on Neural Information Processing Systems (NIPS 2018)
Overcoming catastrophic forgetting by incremental moment matching, S.-W. Lee, J.-H. Kim, J. Jun, J.-W. Ha, and B.-T. Zhang, The 31st Annual Conference on Neural Information Processing Systems (NIPS 2017)
Multimodal residual learning for visual QA, J.-H. Kim, S.-W. Lee, D.-H. Kwak, M.-O. Heo, J. Kim, J.-W. Ha, and B.-T. Zhang, In The 30th Annual Conference on Neural Information Processing Systems (NIPS 2016)

Lee, Inah Department of Brain and Cognitive Sciences

  • Research Lab Laboratory for Behavioral Neurophysiology of Memory
  • Research Area (Core AI)Learning & Reasoning, Brain & Mind
  • Research Area (X+AI)

대표논문

Ahn JR, Lee HW, and Lee I (2019). Rhythmic pruning of perceptual noise for object representation in the hippocampus and perirhinal cortex in rats. Cell Reports 26
Jung MW, Lee H, Jeong Y, Lee JW, Lee I (2018). Remembering rewarding futures: A simulation‐selection model of the hippocampus. Hippocampus 28
Ahn JR, Lee I (2017) Neural correlates of both perception and memory for objects in the rodent perirhinal cortex. Cerebral Cortex 27
Lee I, Lee CH (2013) Contextual behavior and neural circuits. Frontiers in Neural Circuits 7
Lee I, Yoganarasimha D, Rao G, Knierim J (2004) Comparison of population coherence of place cells in hippocampal subfields CA1 and CA3. Nature 430