Jinyang Guo | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Jinyang Guo | Artificial Intelligence | Best Researcher Award

Assist. Prof. Dr. Jinyang Guo, Beihang University, China.

Dr. Jinyang Guo is an Assistant Professor at Beihang University, China, specializing in efficient AI computing 🖥️. A recipient of China’s prestigious National Youth Talent Program 🇨🇳, he has published 40+ papers in top-tier venues like ICML and CVPR 📚. . His research excellence is complemented by patents, IEEE awards 🏅, and active roles in global AI conferences 🌐.

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🎓 Early Academic Pursuits

Dr. Jinyang Guo began his academic journey in Australia, earning a Bachelor of Engineering (Honors) in Electrical Engineering from The University of New South Wales with First Class Honours and placement on the Dean’s Award List (top 5%) from 2014 to 2017. He pursued a Ph.D. in Electrical and Information Engineering at The University of Sydney, focusing on efficient and scalable machine learning, where he was supported by prestigious scholarships including the USydIS and ARC-backed fellowships.

💼 Professional Endeavors

Currently serving as an Assistant Professor at the School of Artificial Intelligence, Beihang University, China, Dr. Guo specializes in efficient AI computing. He has emerged as a lead researcher in several national and industrial projects, overseeing more than 10 competitive grants and spearheading initiatives in AI deployment for UAVs, model compression, and human-machine hybrid systems. He also plays active roles in international academic circles through workshops, guest editorships, and conference organizations.

🔬 Contributions and Research Focus On Artificial Intelligence

Dr. Guo’s research focuses on model compression, AI efficiency, sparsity, and scalable learning, addressing real-world challenges in deploying AI on edge and embedded systems. His pioneering work includes multidimensional pruning frameworks, 3D action recognition, and language model compression, bridging fundamental AI theory with impactful applications. His contributions also span human-machine alignment, video diffusion, and efficient large language models.

🌍 Impact and Influence

With over 40 high-impact publications in IEEE Transactions and elite venues such as ICML, CVPR, AAAI, and NeurIPS, Dr. Guo has gained global recognition in AI and machine learning. His research has influenced both academic and industrial practices, contributing significantly to robust model design and resource-constrained AI deployment. His awards include the ICCV Doctoral Consortium Award and 2nd Place in the IEEE Autonomous UAV Challenge 2023, affirming his standing as a rising leader in the field.

🧠 Research Skills

Dr. Guo possesses robust skills in model pruning, quantization, and neural network optimization. He is adept at developing scalable solutions for real-world AI applications, such as UAVs and embedded systems. His expertise also spans human-machine alignment, point cloud processing, and transformer-based 3D object detection.

🏅 Awards and Honors

He is a recipient of the ICCV Doctoral Consortium Award, 2nd Place in the 2023 IEEE Autonomous UAV Chase Challenge, and multiple prestigious scholarships, including the University of Sydney International Scholarship, Postgraduate Research Supplementary Scholarship, and Engineering and IT Research Scholarship, amounting to over $147,000 USD in academic suppo

Haojin Tang | Artificial Intelligence | Innovative Research Award

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Dr. Haojin Tang | Artificial Intelligence | Innovative Research Award

Dr. Haojin Tang, Guangzhou University, China.

🧑‍🔬 Dr. Haojin Tang is a Lecturer at Guangzhou University, specializing in 🌐 Artificial Intelligence, Deep Learning, and 🛰️ Hyperspectral Image Processing. He holds a Ph.D. in Information and Communication Engineering and has published 20+ top-tier papers, secured national patents 🧾, and led major research projects. As an inspiring mentor, he guides students to achieve excellence in intelligent manufacturing and environmental sensing. His work is shaping the future of smart technologies and remote sensing innovation. 🚀📡

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🎓 Early Academic Pursuits

Dr. Haojin Tang’s academic excellence began at Shenzhen University, where he pursued a B.S. in Electronic Information Engineering (2014–2018) and was recommended for postgraduate study without examination. He later earned both his M.S. (2018–2020) and Ph.D. (2020–2023) in Information and Communication Engineering, supported by the National Scholarship and recognized among the Top 10 Doctoral Dissertations. His solid academic foundation laid the groundwork for a promising research career in artificial intelligence and remote sensing. 🎓📘

💼 Professional Endeavors

Since July 2023, Dr. Tang has served as a Lecturer at the School of Electronic and Communication Engineering, Guangzhou University. He actively mentors undergraduate and graduate students, encouraging them to explore cutting-edge AI techniques in agricultural, forestry, and intelligent manufacturing applications. Under his supervision, students have secured high-impact publications and received numerous provincial and university-level gold awards. 🏅📚

🔬 Contributions and Research Focus On Artificial Intelligence

Dr. Tang’s research is rooted in the integration of Artificial Intelligence, Deep Learning, and Hyperspectral Image Processing, with special attention to industrial fault detection and few-shot learning. His contributions include:

  • Publishing over 20 papers in top-tier journals (JCR Q1, CAS TOP) and CCF Class A conferences.

  • Developing innovative algorithms for hyperspectral image classification and zero-shot learning.

  • Leading projects on cross-domain image classification using large language models. 🧠🛰️

🌍 Impact and Influence

Dr. Tang’s influence extends across academia and industry:

  • He has been invited to review for top journals including IEEE TGRS, Remote Sensing, and J-STARS.

  • His interdisciplinary research addresses real-world challenges in environmental monitoring and intelligent manufacturing.

  • His work has contributed to the advancement of UAV-based hyperspectral sensing and fault detection systems. 📡🌱

🧠 Research Skills

Dr. Tang is adept at designing and implementing deep learning architectures for low-shot learning tasks, developing cross-domain classification algorithms, and leveraging large language models for image interpretation. His skills extend to UAV-based remote sensing systems, software development for big data analysis, and interdisciplinary innovation, making him a versatile researcher and practitioner. 🤖💻

🏅 Awards and Honors

  • National Scholarship (Master’s & Ph.D.)

  • Top 10 Doctoral Dissertations at Shenzhen University

  • Student mentees under his guidance have won numerous provincial and institutional gold medals for research excellence.
    These accolades underscore his academic distinction and mentorship capabilities. 🎖️🌟

🏛️ Legacy and Future Contributions

Dr. Tang is on a trajectory to become a leading innovator in AI-driven remote sensing and industrial diagnostics. His upcoming work on Large Language Model-driven image classification signals a bold move toward integrating generative AI into remote sensing. As a mentor and researcher, he is nurturing future scientists while paving the way for interpretable and scalable AI models in hyperspectral imaging and intelligent manufacturing. 🚀🌐

Publications Top Notes

  • 🛰️ A Spatial–Spectral Prototypical Network for Hyperspectral Remote Sensing Image
    Journal: IEEE Geoscience and Remote Sensing Letters
    Citations: 64
    Year: 2019
    ✨ Pioneer in spatial-spectral modeling for remote sensing

  • 🔍 Multidimensional Local Binary Pattern for Hyperspectral Image Classification
    Journal: IEEE Transactions on Geoscience and Remote Sensing
    Citations: 37
    Year: 2021
    🔬 Robust feature extraction in HSI

  • 🧠 Fusion of Multidimensional CNN and Handcrafted Features for Small-Sample Hyperspectral Image Classification
    Journal: Remote Sensing
    Citations: 13
    Year: 2022
    🤖 Hybrid deep learning for limited data

  • 📊 A Multiscale Spatial–Spectral Prototypical Network for Hyperspectral Image Few-Shot Classification
    Journal: IEEE Geoscience and Remote Sensing Letters
    Citations: 13
    Year: 2022
    🔁 Improved generalization with few-shot learning

  • ⚙️ HFC-SST: Improved Spatial-Spectral Transformer for Hyperspectral Few-Shot Classification
    Journal: Journal of Applied Remote Sensing
    Citations: 12
    Year: 2023
    🧭 Enhanced transformer model in HSI

  • 🛠️ Multi-Label Zero-Shot Learning for Industrial Fault Diagnosis
    Conference: 6th Int’l Conf. on Information Communication and Signal Processing
    Citations: 7
    Year: 2023
    🏭 AI for smart industry diagnostics

  • 🛰️ Multi-Scale Attention Adaptive Network for Object Detection in Remote Sensing Images
    Conference: 5th Int’l Conf. on Information Communication and Signal Processing
    Citations: 4
    Year: 2022
    🎯 Precision object detection framework

  • 🧠 Global-Local Attention-Aware Zero-Shot Learning for Industrial Fault Diagnosis
    Journal: IEEE Transactions on Instrumentation and Measurement
    Citations: 2
    Year: 2025
    💡 Breakthrough in industrial ZSL

  • 📐 TSSLBP: Tensor-Based Spatial–Spectral Local Binary Pattern
    Journal: Journal of Applied Remote Sensing
    Citations: 2
    Year: 2020
    🧮 Tensor-based HSI analysis

  • 🧬 AMHFN: Aggregation Multi-Hierarchical Feature Network for Hyperspectral Image Classification
    Journal: Remote Sensing
    Citations: 1
    Year: 2024
    🔗 Deep feature aggregation strategy

  • 🎯 Dense Convolution Siamese Network for Hyperspectral Image Target Detection
    Conference: 5th Int’l Conf. on Information Communication and Signal Processing
    Citations: 1
    Year: 2022
    🛸 High-precision target detection