Jia Liu Ph.D.
Tsinghua University Chair Professor of Sciences, Chief Scientist of Beijing Academy of Artificial Intelligence
In recent years, fascinating progresses have been made in utilizing artificial intelligence to solve a broad range of problems. AI systems today can match and even outperform human performance in certain challenging tasks. Many recent AI advances have been largely inspired by neuroscience research into biological brain, guided by architectural and algorithmic constrains from biological neural networks. However, artificial neural networks remain to be “black boxes,” where the internal representations and computations of network components are poorly understood.
In my lab, we advocate the potential for cognitive neuroscience to further benefit AI. Specifically, research techniques and approaches available in cognitive neuroscience, including single-unit recording, neuroimaging, cognitive, and lesion techniques, can serve as a repertoire of tools for unveiling the black boxes of AI, illuminating the computations and representations inside AI networks. Further, research findings from cognitive neuroscience can provide inspirations to develop next generation of AI, by build-in a priori architectures, algorithms, and knowledge. The purpose of our research is to bring together research efforts from AI and cognitive neuroscience, seeking to integrate AI and cognitive neuroscience toward a new field: AI of Brain and Cognition (ABC).
Department of Brain and Cognitive Sciences, MIT, PhD, 2003
Department of Psychology, Peking University, B.S. & M.S., 1995/1997
Department of Psychology & Tsinghua Laboratory of Brain and Intelligence,Tsinghua University,Professor,2020- Now
National Key Laboratory of Cognitive Neuroscience and Learning/School of Psychology/Faculty of Psychology,Beijing Normal University,Professor,2006–2020
Department of Brain & Cognitive Sciences,Massachusetts Institute of Technology,Fulbright Visiting Scholar,2009–2010
National Key Laboratory of Brain & Cognitive Sciences,Institute of Biophysics,Chinese Academy of Sciences,Associate Professor/Professor,2003–2006
Department of Brain & Cognitive Sciences, Massachusetts Institute of Technology,Postdoc,2002–2003
Selected Publications (2011-2020)
1.Tian X., Wang, R., Zhao, Y., Zhen, Z., Song, Y., & Liu, J. (2020). Multi-Item Discriminability Pattern to Faces in Developmental Prosopagnosia Reveals Distinct Mechanisms of Face Processing. Cerebral Cortex 30(5):2986-2996.
2.Huang, T., Chen, X., Jiang, J., Zhen, Z., & Liu, J. (2019). A probabilistic atlas of the human motion complex built from large-scale functional localizer data. Human Brain Mapping 40(12):3475-3487
3.Zhao, Y., Zhen, Z., Liu, X., Song, Y., & Liu, J. (2018). The neural network for face recognition: insights from an fMRI study on developmental prosopagnosia. NeuroImage 169: 151-161.
4.Kong, X. Z., Song, Y., Zhen, Z., & Liu, J. (2017). Genetic variation in S100B modulates neural processing of visual scenes in Han Chinese. Cerebral Cortex 27(2): 1326-1336.
5.Wang, X., Zhen, Z., Song, Y., Huang, L., Kong, X., & Liu, J. (2016). The Hierarchical Structure of the Face Network Revealed by Its Functional Connectivity Pattern. The Journal of Neuroscience, 36(3): 890-900.
6.Song, Y., Lu, H., Hu, S., Xu, M., Li, X., & Liu, J. (2015) Regulating emotion to improve physical health through the amygdala. Social, Cognitive, and Affective Neuroscience 10(4): 523-530.
7.Huang, L., Song, Y., Li, J., Zhen, Z., Yang, Z., & Liu, J. (2014). Individual differences in cortical face selectivity predict behavioral performance in face recognition. Frontiers in Human Neuroscience 8(483): 1-10.
8.Song, Y., Luo, Y., Li, X., Xu, M., & Liu, J. (2013). Representation of Contextually Related Multiple Objects in the Human Ventral Visual Pathway. Journal of Cognitive Neuroscience 25(8):1261-1269.
9.Wang, R., Li, J., Fang, H., Tian, M., & Liu, J. (2012). Individual differences in holistic processing predict face-recognition ability. Psychological Science 23: 169-177.
10.Zhu, Q., Zhang, J., Luo, Y., Dilks, D., & Liu, J. (2011). Resting-state neural activity across face-selective cortical regions is behaviorally relevant. The Journal of Neuroscience 31(28):10323-10330.
Office: RM 909, Tsinghua Laboratory of Brain and Intelligence, Tsinghua University