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Dr. Gehua Ma obtained his PhD in Artificial Intelligence at College of Computer Science and Technology-CCNT Lab, Zhejiang University.

His recent research primarily focuses on Causal Inference and Spectrum Analyses in Recommender Systems, Associative Memory, and Spatiotemporal Modeling. Previously, he has devised AI4Science solutions aimed at Cognitive Computational Neuroscience, NeuroAI, and XAI.

Selected Honors

Selected Publications and Preprints

  • [2024] [Dissertation] Research on A Spiking Generative Model of Spatiotemporal Memory Construction and Computation. Available at link

  • [2023] G. Ma, R. J., R. Y., et al. Temporal Conditioning Spiking Latent Variable Models of the Neural Response to Natural Visual Scenes.

    NeurIPS (Thirty-seventh Conference on Neural Information Processing Systems). Available at link.

  • [2022] G. Ma, R. Y., and H. T.. Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks.

    Cell Press, Patterns. Available at link.

  • [2021] G. Ma, R. J., L. W., et al. Dual memory model for experience-once task-incremental lifelong learning.

    Elsevier, Neural Networks. Available at link.

  • [2020] G. Ma, H. W., J. Z., et al. Successive POI Recommendation via Brain-inspired Spatiotemporal Aware Representation.

    AAAI (Thirty-Eighth AAAI Conference on Artificial Intelligence). Available at link.

  • [2020] Y. W., G. Ma (Co-first Author), G. G., et al. Bioimaging of Dissolvable Microneedle Arrays: Challenges and Opportunities.

    Science/AAAS, Research. Available at link.

Service

  • Served for top-tier tracks and journals including NeurIPS, ICLR, IJCAI, ICASSP, Proc. of IEEE, IEEE TPAMI, IEEE TNNLS, Neural Networks, etc.

Employment and Internship

Exchange Experience

  • The Chinese University of Hong Kong, HK, PRC. 2016. Visiting Student

  • Technische Universität München, München, Germany. 2014. Summer Campus

  • New York University, NY, USA. 2012. Summer Campus

Patents

  • [2021] H. T., Gehua Ma, and R. Y. Point of Interest Recommendation Method and System based on Brain-inspired Spatiotemporal Perceptual Representation. PCT (International): WO/2023/015658, CN202110930940.0 (granted).

  • [2019] S. X., C. T., Gehua Ma, and B. Wang. A deep learning based vein visualization method and device. CN201911365761.6