CV

Current Position

Project Researcher, International Research Center for Neurointelligence (IRCN), The University of Tokyo
May 2026 - Present

Research on brain-inspired AI, active vision, predictive coding, cognitive feelings, and multimodal learning.

Education

• Ph.D. in Information Science and Technology, The University of Tokyo, 2023-2026
  Department of Mechano-Informatics, Graduate School of Information Science and Technology
  Dissertation: Human-Inspired, Affect Sensitive Hard Attention Model Yields Human-Like Gaze in Image Classification.

• M.S. in Information Science and Technology, The University of Tokyo, 2021-2023
  Department of Mechano-Informatics, Graduate School of Information Science and Technology
  Thesis: Abstraction and conceptualization of spatial information through task-specific active perception.

• B.Eng. in Mechanical and Electronic Engineering, Jiangnan University, 2015-2019

• Exchange Student / Research Student, Tohoku University, 2018-2019
  School of Engineering, Department of Mechanical and Aerospace Engineering

Experience

• Project Researcher, IRCN, The University of Tokyo, 2026-Present
  Research on brain-inspired AI, active vision, predictive coding, cognitive feelings, and multimodal learning.

• Machine Learning Engineer Intern, Corpy & Co., Inc., 2023-2024
  Implemented and evaluated machine learning models, analyzed experimental results, and built literature information extraction pipelines using the Semantic Scholar API and OpenAI API.

• Social Robot Research Intern, Tata Consultancy Services Japan, 2022
  Worked on multimodal human-robot interaction using facial expression recognition, robot vision, dialogue generation, and generative AI tools.

• Research Intern, Chulalongkorn University, Feb-Mar 2023
  Studied model interpretability for audio recognition using YAMNet and Grad-CAM visualization.

Selected Publications

• Pan Pengcheng, Shogo Yonekura, Yasuo Kuniyoshi. “Debiasing Central Fixation Confounds Reveals a Peripheral ‘Sweet Spot’ for Human-like Scanpaths in Hard-Attention Vision.” Annual Meeting of the Cognitive Science Society (CogSci 2026), Poster, Rio de Janeiro, Brazil, July 22-25, 2026.
• Pan Pengcheng, Shogo Yonekura, Yasuo Kuniyoshi. “Emergence of Fixational and Saccadic Movements in a Multi-Level Recurrent Attention Model for Vision.” 32nd International Conference on Neural Information Processing (ICONIP 2025), Oral, Okinawa, Japan, November 20-24, 2025.
• Pan Pengcheng, Shogo Yonekura, Yasuo Kuniyoshi. “Bayesian Glimpse Control Boosts Active Vision RL on Atari Pong.” 6th International Workshop on Active Inference (IWAI 2025), Poster & Spotlight, Montreal, Canada, October 28-30, 2025.
• Pan Pengcheng, Shogo Yonekura, Yasuo Kuniyoshi. “Connecting Dense and Sparse Networks in Recurrent Independent Mechanisms.” Robotics Society of Japan Annual Conference (RSJ 2022), Oral, Tokyo, Japan, October 28-30, 2022.
• Pengcheng Pan, Shogo Yonekura, Yasuo Kuniyoshi. “Human-in-the-Loop Hard Attention for Privacy-Aware Active Vision.” The 40th Annual Conference of the Japanese Society for Artificial Intelligence (JSAI 2026), Oral, Gunma, Japan, June 8-12, 2026.
• Pan Pengcheng, Noritaka Yusa. “Numerical Analysis for Pipe Wall Thinning Detection using Electrical Impedance Tomography.” 17th International Conference on Flow Dynamics (ICFD 2020), Oral, Sendai, Japan, October 28-30, 2020.

Skills

Research areas: computer vision, active vision, hard attention, human-centered AI, cognitive science, neuroscience-inspired AI, multimodal AI, reinforcement learning.

Machine learning: deep learning, CNNs, RNNs, REINFORCE, visual attention models, image classification, representation learning, fundamentals of Transformers and vision-language models.

Implementation: Python, PyTorch, NumPy, Pandas, scikit-learn, Matplotlib, Git, Docker, Linux, Jupyter, CUDA-based deep learning experiments.

Evaluation and analysis: classification accuracy, scanpath analysis, DTW, ScanMatch, NSS, AUC, GCS, PCA-based feature analysis, fixation distribution analysis, error analysis, visualization.