RESEARCH AREAS
Research areas
Grounded in pattern recognition and machine learning, we explore generalizable and trustworthy methods across real-world settings.
Pattern Recognition & Machine Learning
Machine learning is a core branch of artificial intelligence. It enables computers to accomplish prediction or decision‑making tasks without explicit programming by automatically learning rules and patterns from data. Its primary approaches include s
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Document Intelligence
Scene text analysis is a key branch of document recognition and analysis. It focuses on detecting, recognizing and understanding text embedded in natural‑scene images. Major challenges include complex background noise, multi‑scale font variations, un
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Handwritten Signature Verification
Handwritten signature verification consists of three phases: feature extraction, feature generation and verification analysis. First, backbone networks extract static and dynamic features from signature images, including geometric structures, stroke
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Medical Image Analysis
Medical‑image segmentation and classification are core tasks in medical‑image processing. Segmentation aims to precisely localize anatomical structures or lesion regions, while classification identifies corresponding pathological types. Conventional
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Data Mining & Bioinformatics
Within bioinformatics, Graph‑Neural‑Network (GNN)‑based protein‑structure prediction models proteins as graphs: nodes represent amino‑acid residues and edges denote interactions or spatial adjacency between residues. Combined with data‑mining techniq
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Remote Sensing
Remote‑sensing‑image object detection is one core task of intelligent remote‑sensing interpretation. It automatically identifies and geolocates specific ground objects (buildings, vehicles, ships, aircraft, etc.) within high‑resolution remote‑sensing
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Speech & Language
Multilingual speech recognition is an important branch of speech‑recognition technology. It enables accurate recognition and cross‑lingual processing for diverse languages, dialects and mixed‑language utterances. Major challenges stem from acoustic‑f
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Privacy & Security
Federated Learning is a distributed machine‑learning paradigm that enables collaborative multi‑party model training while preserving data privacy. Its core principle is keeping raw data stored locally. Each participant trains models locally and uploa
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