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Xiangxiang Cui

A PhD student at BNU focuses on designing intelligent computing models.

About me

Aiming to design Scalable & Efficient & Trustworthy AI Models by studying neural networks through brain, computer, math and physics, while actively pursuing innovations in Next-generation AI paradigms.

  • 🪧Seeking a Postdoc Position for Fall 2026: Generative AI & AI Efficiency & AI Explainability & NeuroAI.

    • Systems → How to engineer better AI (efficiency, scalability, robustness)
    • Algorithms → How to design smarter AI (learning, adaptation, generalization)
    • Mechanisms → How to understand deeper AI (principles, explainability, theory)

    My training mirrors this stack—B.E. Computer Science , M.E. Deep Learning, and Ph.D. Cognitive Neuroscience—enabling me to tackle challenges across all three layers with deep expertise. Looking forward to contributing to your team.

  • 🧠Ph.D., 2022~Now, majored in Cognitive Neuroscience. State Key Laboratory of Cognitive Neuroscience and Learning (IDG/McGovern Institute for Brain Research) at Beijing Normal University. Advised by Prof. Jing Sui & Prof. Vince D. Calhoun (TReNDS). Research on AI↔Brain.

  • 👨‍⚕️M.E., 2019~2022, majored in Software Engineering. Advised by Prof. Zhongyu li. Research on Deep Learning. 🤖B.E., 2014~2018, majored in Software Engineering. Research on Embedded Software & Robot.

InterestsEducation
🧠NeuroAI
🎯Generative AI
📈Continual Learning
🌍‪Multimodal Learning
🎓Ph.D. in Beijing Normal University, 2022-Present
State Key Laboratory of Cognitive Neuroscience and Learning
🎓M.E. in Xi’an JiaoTong University, 2019-2022
Faculty of Electronic and Information Engineering
I am dedicated to designing intelligent computing models, focusing on developing meta-methods for reasoning, learning, security, and efficiency. My interests span Multimodal AI, Physics AI, World Simulation, AI Games, Continual Learning, Reinforcement Learning, Twin Brains, Neuro-symbolic AI, Neural Decoding, Neural Coding and Robotics. If you are interested in any of these topics, please feel free to contact me. I am open to cooperation.

News

  • ⌛️ 2025/07 A patent is in the application process.
  • 🎉 2025/07 A paper has been Accepted by Neurocomputing (JCR Q1, IF=6.5).
  • 🎉 2025/07 A paper has been Accepted by Physics in Medicine and Biology (JCR Q1).
  • 🎉 2025/01 A patent (No. 202410125896.X) for the application of artificial intelligence in radiotherapy.
  • 🎉 2024/08 A paper has been Accepted by Medical physics 2024 (JCR Q1, Top 3 Medical Physics journals)
  • 🎉 2024/04 A paper about eliminating site effects will be Accepted to EMBC 2024.
  • 🎉 2023/12 An abstract of neuroimage classification was Accepted to OHBM2024.
  • 🎉 2023/10 Join in the National key research and development program.

Selected Publications

​ # Equal contributions

  • For a complete list (including works under review), please contact me via email.
  • Model Design
    • Xiangxiang Cui#, Min Zhao#, et al. “A Cross-Feature Mutual Learning Framework Integrating Multiple Features for Brain Disorder Diagnosis”. In Neurocomputing 2025 (JCR Q1)
    • Xiangxiang Cui#, Xueying Yang#, et al. “A StarGAN and Transformer-Based Hybrid Classification-Regression Model for Multi-institution VMAT Patient-Specific Quality Assurance.” In Medical Physics 2024 (JCR Q1).
    • Xiangxiang Cui, Dongmei Zhi, Weizheng Yan, et al. “CGDM-GAN: An Adversarial Network Approach with Self-supervised Learning for Site Effect Removal. “ In EMBC 2024, CAAI B.
    • Xiangxiang Cui, et al. “Multi-Scale Analysis Framework Integrating Brain Functional Connectivity and Activity” In the Annual Meeting of the organization for Human Brain Mapping (OHBM) 2024.
    • Xiangxiang Cui, et al. “CGDM-GAN: An Adversarial Network Approach with Self-Supervised for Removing Site Effects” In the Annual Meeting of the organization for Human Brain Mapping (OHBM) 2023.
    • Xiangxiang Cui, et al. “Variable-frame CNNLSTM for Breast Nodule Classification using Ultrasound Videos” Preprint 2023.
    • Xiangxiang Cui, et al. “DEAttack: A differential evolution based attack method for the robustness evaluation of medical image segmentation.” In Neurocomputing 2021, JCR Q1, IF:6.5.
    • Xueying Yang#, Xiangxiang Cui#, et al. “Uncertainty quantification-guided patient-specific quality assurance using Bayesian neural networks based on field complexity features and fluence maps”. In Physics in Medicine and Biology 2025 (JCR Q1)
    • Yichen Wang, Zhongyu Li, Xiangxiang Cui, et al. “Key-frame Guided Network for Thyroid Nodule Recognition using Ultrasound Videos” In MICCAI 2022.
  • Model Explainability
    • Multimodal
    • Cognitive Coding

Under submission

  • Xiangxiang Cui, Min Zhao, et al. “BRIEF: BRain-Inspired network connection search with Extensive temporal feature Fusion enhances disease classification”
  • Yi Cui, Xiangxiang Cui, et al. “Information transmission through light-induced scattering in photorefractive crystal”

Patents

  • 一种容积调强放射治疗的检测方法及相关设备, CN Patent
  • 一种基于贝叶斯模型不确定性量化的 VMAT 计划患者剂量验证预测方法及系统, CN Patent

Services

Conference Reviewers: ICLR