Entreprenuer and Computer Vision Researcher
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 โ blending academic research with practical innovation. Yunusa has published in, and reviewed for, top-tier conferences such as ACL, COLING, ACCV, ICMV, and ICCV. He actively contributes to the global AI community through peer reviews 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 โ bringing the same energy and discipline to his startup journey.
Our work on the synergies of hybrid Vision Transformers and CNN was published in the Engineering Applications of Artificial Intelligence. Link
We presented iiANET in ACCV workshop, Computer Vision for Developing Countries, Hanoi, Vietnam Link
๐ Excited to announce that our work, "MambaForGCN: Enhancing Long-Range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-Based Sentiment Analysisโ has been accepted at COLING 2025! ๐. Link
๐๐พ Excited to share our work on KonvLiNA: Integrating Kolmogorov-Arnold Network with Linear Nystrรถm Attention for Feature Fusion in Crop Field Detection, presented at the 2024 17th International Conference on Machine Vision (ICMV) in Edinburgh, UK. Link
Exploring the Synergies of Hybrid CNNs and ViTs Architectures for Computer Vision: A survey. | Engineering Applications of Artificial Intelligence, Elsevier (2025)
MambaForGCN: Enhancing Long-Range Dependency with State Space Model and Kolmogorov-Arnold Networks for Aspect-Based Sentiment Analysis. | 31st ACL COLING, Abu Dhabi, UAE (2025)
Multi-scale dual-stream visual feature extraction and graph reasoning for visual question answering, SaRPFF: A self-attention with register-based pyramid feature fusion module for enhanced rice leaf disease (RLD) detection. | Applied Intelligence, Elsevier (2025)
SaRPFF: A self-attention with register-based pyramid feature fusion module for enhanced rice leaf disease (RLD) detection. | Multimed Tools Application (2025), Springer (2025)