Team

Ryota Ishikawa

PhD Student, Humanics
JSPS Research Fellow (DC2)
MA in Behavioral Sciences, University of Tsukuba

Ryota joined HEBB’S Lab in 2018. His research examines how bodily self-consciousness emerges and changes through motor learning, particularly in virtual-reality environments. He currently investigates how altered bodily self-consciousness influences perceptual processing, including pain, and develops computational models based on Bayesian inference. His long-term goal is to establish new methods for modulating sensory perception using noninvasive brain stimulation such as tACS and tDCS.

• iScience
• Press release
• Cognition
• Press release
• PAIN Reports

Jiaxing Tian

PhD Student, Empowerment Informatics
MS in Information Science, Nara Institute of Science and Technology

Jiaxing joined HEBB’S Lab in 2022 and entered the PhD Program in Empowerment Informatics. He has studied computational models of exploration and exploitation in reward-based human decision-making. His current research focuses on mathematical models of motor learning and motor memory based on metacognitive theory. He ultimately aims to use principles of human brain function to develop more powerful artificial-intelligence models and algorithms.

Meng-Hung Lee

PhD Student, Humanics
JST SPRING
MS in Brain Science, National Yang Ming Chiao Tung University

Meng-Hung joined HEBB’S Lab in 2022 and entered the PhD Program in Humanics. During his master’s studies, he used deep learning to generate music from EEG signals with the aim of reducing pain. His current research examines how sleep influences motor behavior and motor learning. He hopes to apply these findings to improve motor rehabilitation and healthcare for older adults.

Towa Arai

PhD Student, Degree Program in Intelligent and Mechanical Interaction Systems
MS in Engineering, University of Tsukuba

Towa joined HEBB’S Lab in 2022. He uses artificial neural networks to investigate the mechanisms of adaptive motor learning under external perturbations. Through simulations using biologically plausible models inspired by brain architecture, he aims to clarify the mechanisms that support the flexibility and speed of human learning. His work contributes both to computational neuroscience and to potential engineering applications.

Yuto Fukuda

PhD Student, Empowerment Informatics
JST BOOST
MS in Engineering, University of Tsukuba

Yuto joined HEBB’S Lab in 2022 and entered the PhD Program in Empowerment Informatics in 2025. He is interested in human cognition, particularly the role of decision-making in motor control. His research seeks to identify the computational principles of decision-making and apply them to sport and neurorehabilitation.

Rei Taomote

PhD Student, Degree Program in Intelligent and Mechanical Interaction Systems
JST SPRING
MS in Engineering, University of Tsukuba

Rei joined HEBB’S Lab in 2023. His earlier work examined how painful stimulation alters the frequency characteristics and temporal dynamics of EEG signals. He currently investigates how VR-induced changes in bodily self-consciousness influence pain perception and brain activity. His long-term goal is to develop noninvasive VR-based interventions for chronic pain.

Huiting Ma

PhD Student, Humanics
MS in Mechanical Engineering, Dongguan University of Technology

Huiting joined HEBB’S Lab in 2025 and entered the PhD Program in Humanics. During her master’s studies, she investigated continuous human-movement intention using surface electromyography and muscle-synergy analysis. Her current work examines neural activity elicited by external stimulation. She aims to develop robust computational models for interpreting bioelectrical data and advancing brain–machine interaction.

Kevin Jaehwa Lee

PhD Student, Degree Program in Intelligent and Mechanical Interaction Systems
JST SPRING
MS in Computer Science (Artificial Intelligence), University of Southern California

Kevin joined HEBB’S Lab in 2025. His research focuses on computational modeling of motor recovery after stroke, combining neuroscience and machine learning to explain how patients transition from suboptimal to more effective movement strategies during rehabilitation. He develops biologically grounded two-timescale models that couple action selection with motor adaptation and reproduce clinical phenomena such as the valley of recovery and learned nonuse. His long-term goal is to develop AI-driven personalized rehabilitation systems.

Srikireddy Dhanunjay Reddy

PhD Student, Indian Institute of Technology Roorkee
JST LOTUS Fellow

Dhanunjay joined HEBB’S Lab in 2026 through the JST LOTUS Fellowship. He is pursuing his PhD at the Indian Institute of Technology Roorkee and previously completed his master’s degree at the National Institute of Technology Silchar. His doctoral work applies manifold-learning techniques to the identification of neurological disorders using EEG. Within the LOTUS program, he investigates meta-learning approaches for improving spike-sorting algorithms and aims to develop a robust manifold-based analysis pipeline for spike sorting.

Lucas Dal'Bello, PhD

Researcher, Laboratory of Neuromotor Physiology, Fondazione Santa Lucia
PhD in Human Informatics, University of Tsukuba
MEXT Scholarship Recipient and Meikeikai Award Recipient

Lucas joined HEBB’S Lab in 2017 and entered the PhD Program in Empowerment Informatics in 2018. He graduated from the Molecular Sciences Program at the University of São Paulo, where he gained experience in EMG, linear-systems modeling, and computational neuroscience. His doctoral research used experiments and mathematical modeling to investigate how information related to motor control is processed in the human brain.

• Current affiliation
• Neural Networks
• Neural Networks
• Press release

Yihao Wu

Kyocera Corporation
PhD in Human Informatics, University of Tsukuba
Award of the Dean of the School of Integrative and Global Majors

Yihao joined HEBB’S Lab in 2018 and entered the PhD Program in Empowerment Informatics in 2019. His research examined reward-based human learning and decision-making from an engineering perspective. He developed computational models of regret-based reinforcement learning and internal-model switching.

• Regret reinforcement learning
• Neural Networks

Taisei Sugiyama, PhD

Researcher, Fujita Health University
PhD in Human Informatics, University of Tsukuba
President’s Award Recipient

Taisei joined HEBB’S Lab in 2016 and entered the PhD Program in Empowerment Informatics in 2017. He studied Cognitive Science at UCLA. His research examined how external factors such as reward and training context influence motor behavior, motor learning, and meta-learning. He also has experience in TMS, tDCS, neuroimaging, and computational neuroscience.

• Nature Communications
• European Journal of Physical and Rehabilitation Medicine
• PNAS