Assistant Professor / Applied AI Researcher

Building AI systems that understand handwriting, documents, and visual structure.

I am an Assistant Professor at the WACOM-TUAT Joint Research Lab, Tokyo University of Agriculture and Technology. My work focuses on machine learning, computer vision, handwriting recognition, automatic scoring, image/text understanding, recommendation-oriented applications, and deployable data-driven AI systems.

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Handwriting & document AI

Recognition and structural analysis of handwritten mathematical expressions, Japanese/English handwriting, chemical formulas, and document-like input.

Graph-based reasoning

Graph neural networks for modeling symbols, spatial relations, candidate edges, and structural patterns in handwritten mathematical expressions.

Applied AI systems

End-to-end model development, REST API deployment, computer vision systems, social media analytics, recommendation systems, and real-world AI product prototypes.

Current research directions

Graph-based HME structure recognition

I develop models that represent handwritten mathematical expressions as structured graphs of symbols and spatial relations, with edge-aware attention and relation prediction.

PyTorch GeometricGATCROHMEInkML

Multimodal handwriting recognition

I combine online pen trajectories, stroke order, and timestamps with offline visual representations to build more robust handwriting and mathematical expression recognition systems.

Online signalsOffline imagesMultimodal AI

Automatic scoring of handwritten answers

I work on recognition, answer similarity, confidence estimation, rejection mechanisms, and human-in-the-loop verification for educational assessment.

Educational AIScoringHuman-in-the-loop

Geometry drawing assessment

I study process-aware scoring of geometric construction tasks using digital ink from an electronic drawing compass, stroke-level clustering, step-level segmentation, primitive extraction, and geometric reasoning.

Digital inkGeometryStep-wise scoring

Handwriting input systems for LMS

I develop Moodle-based handwriting input and recognition systems for math, chemistry, Japanese, and English questions while preserving handwriting traces for analysis.

MoodleREST APIMathJaxEducational assessment

Featured projects

Edge-aware GAT for HME

Relation prediction between handwritten mathematical symbols using edge-aware attention and relation decoding.

HAIMR Moodle Plugin

Handwritten answer input, management, and recognition for Japanese, English, mathematical expressions, and chemical formulas.

E-compass scoring

Process-aware scoring framework for geometry construction problems using arcs, lines, intersections, and step evidence.

Personal interests

Outside research, I enjoy staying active and documenting everyday moments.

Football Badminton Running Photography Cycling

Research vision

My long-term goal is to develop AI systems that can understand human-generated visual and multimodal information, including handwriting, documents, diagrams, and learning processes. I am particularly interested in combining computer vision, graph-based structural reasoning, online/offline handwriting recognition, and multimodal learning to build reliable and interpretable AI systems. My research aims to bridge academic advances in pattern recognition with practical applications in education, document intelligence, and human-centered AI.

I aim to bridge academic advances in pattern recognition with practical applications in education, document intelligence, and human-centered AI.