researcher // founder // phd

Yunusa Haruna

Computer Vision · Efficient Architectures · Edge AI · Medical Imaging

01

About

Yunusa Haruna
📍 Beijing, China
GitHub

Yunusa is the founder of NewraLab, an AI research and development startup based in Suzhou, China, focused on building intelligent systems for autonomous technologies. With a PhD in Pattern Recognition and Intelligent Systems from Beihang University, he brings deep expertise in computer vision, generative models, and natural language processing.

He has published in and reviewed for top-tier venues including ACL, COLING, ACCV, ICMV, and ICCV, actively contributing to the global AI research community through peer review and collaborative research.

NewraLab is driven by his passion for advancing AI through rigorous research and translating breakthroughs into real-world solutions. Outside of work, Yunusa enjoys football and boxing — bringing the same discipline to his startup journey.

current_focus.sh
$postdocAgentic Retinal Fundus Diagnosis · Long-Range Reasoning
$labNewraLab — Edge-Ready AI for Emerging Regions
$archSSM / Mamba · Hybrid CNN-ViT · Spatiotemporal
$statusopen to collaboration
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News

Mar. 2025

Our work on the synergies of hybrid Vision Transformers and CNNs was published in Engineering Applications of Artificial Intelligence. [link →]

Dec. 2024

MambaForGCN accepted at COLING 2025 — enhancing long-range dependency with SSM and Kolmogorov-Arnold Networks for ABSA. [link →]

Oct. 2024

KonvLiNA presented at ICMV 2024, Edinburgh — integrating KAN with Linear Nyström Attention for crop field detection. [link →]

Dec. 2023

Presented iiANET at ACCV Workshop — Computer Vision for Developing Countries, Hanoi, Vietnam. [slides →]

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Publications

Journal
Exploring the Synergies of Hybrid CNNs and ViTs Architectures for Computer Vision: A Survey
Conf.
MambaForGCN: Enhancing Long-Range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-Based Sentiment Analysis
Journal
Multi-Scale Dual-Stream Visual Feature Extraction and Graph Reasoning for Visual Question Answering
Journal
SaRPFF: Self-Attention with Register-Based Pyramid Feature Fusion for Enhanced Rice Leaf Disease Detection