Yumeng Su | Engineering | Best Researcher Award

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Mr. Yumeng Su | Engineering | Best Researcher Award

Mr. Yumeng Su at Shanghai Jian Qiao University, China.

Su Yumeng πŸŽ“, a top-ranking computer science undergraduate at Shanghai Jian Qiao University πŸ‡¨πŸ‡³, excels in AI πŸ€–, robotics 🚁, and embedded systems 🧠. With hands-on experience in drone development, ROS, and deep learning πŸ’‘, he has published impactful research and led award-winning teams πŸ†. His technical expertise spans Python, MATLAB, LaTeX, and advanced hardware platforms like Jetson Nano and STM32 πŸ’». Beyond academics, he’s a dynamic leader and athlete πŸ€πŸš΄β€β™‚οΈ, known for his resilience and innovation in intelligent systems and smart hardware applications πŸ”. Su’s work bridges theoretical AI with real-world applications 🌐.

Publication Top Notes

ORCID

Suitability for Best Researcher Award – Su Yumeng

Su Yumeng is a highly promising and exceptionally driven early-career researcher whose blend of technical excellence, innovation, and leadership places him as a top contender for the Best Researcher Award. As an undergraduate, his hands-on contributions to AI, robotics, and embedded systems are not only commendable but groundbreaking, particularly for his academic level. He demonstrates a rare ability to translate theory into impactful real-world applications, bridging research with innovation in autonomous systems, drone technology, and intelligent hardware solutions.

πŸ”Ή Education & Experience

  • πŸŽ“ B.Sc. in Computer Science & Technology, Shanghai Jian Qiao University (2021–Present)

  • πŸ“ Focus: AI, Robotics, Embedded Systems, and Smart Hardware

  • πŸ“š Completed key courses with top grades (AI, Python, Robotics, Microcontroller Principles, etc.)

  • πŸš€ ROS training at East China Normal University (Basic & Advanced UAV/Vehicle Tracking)

  • πŸ›  Internship at Superdimension Technology Space: Autonomous drone development

  • πŸ§ͺ Project collaboration with FAST-Lab at Zhejiang University on UAVs

πŸ”Ή Professional Development

Su Yumeng continually advances his professional skills through academic projects πŸ§ͺ, interdisciplinary competitions πŸ†, and real-world UAV applications 🚁. He has mastered the integration of AI models like YOLO with edge computing platforms such as Jetson Nano and Raspberry Pi πŸ’». His leadership in innovation competitions reflects his capacity to guide teams and deliver impactful solutions 🎯. Su’s deep involvement in research and drone design demonstrates his ability to convert academic concepts into cutting-edge technology πŸ’‘. With practical ROS experience and sensor fusion expertise, he remains at the forefront of smart automation and robotics 🌐.

πŸ”Ή Research Focus Category

Su Yumeng’s research focuses on Artificial Intelligence in Embedded and Autonomous Systems πŸ€–, especially in smart robotics and deep learning applications for environmental perception and control 🌍. His work bridges physics-informed neural networks (PINNs) with real-time sensor fusion for drones and robotics 🀝. He explores practical challenges like crack detection in infrastructure using UAVs πŸ› οΈ, baby posture recognition on embedded platforms 🍼, and SLAM-based navigation for wheeled robots πŸš—. His interdisciplinary approach merges hardware innovation with AI, yielding scalable, intelligent, and responsive systems suitable for civil engineering, healthcare, and autonomous mobility fields πŸš€.

πŸ”Ή Awards & Honors

  • πŸ₯‡ National Second Prize, 17th National College Student Computer Design Competition (2024)

  • πŸ₯‰ National Bronze & Shanghai Gold, China Innovation Competition (2024)

  • πŸ₯‡ Shanghai Gold Award, Career Planning Competition (2024)

  • πŸ… First Prize, Shanghai College Student Computer Application Competition (2024)

  • πŸ₯ˆ Shanghai Second Prize, Ti Cup Electronic Design Contest (2023)

  • πŸ₯‰ Bronze Award, “Challenge Cup” Entrepreneurship Plan Competition

  • πŸ₯‰ Shanghai Third Prize, China Robot & AI Competition (2024)

  • πŸŽ– National Motivational Scholarship Γ—3

  • πŸŽ“ President β€œQing Yun” Scholarship

  • πŸŽ— School Special Scholarship

Publication Top Notes

  • “The Feasibility Assessment Study of Bridge Crack Width Recognition in Images Based on Special Inspection UAV”
    Cited by: 13 | Year: 2020 ​

  • “Intelligent Crack Detection and Quantification in the Concrete Bridge: A Deep Learning-Assisted Image Processing Approach”
    Cited by: 20 | Year: 2022

Conclusion

Su Yumeng’s combination of academic excellence, deep technical knowledge, hands-on innovation, and research impact clearly distinguishes him as an ideal recipient of the Best Researcher Award. His contributions as an undergraduate are extraordinary and reflect the potential of a future global leader in AI and robotics research.

Fei Li | Next-generation informatic | Leading Research Award

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prof. Fei Li | Next-generation informatic | Leading Research Award

Research scientist Institute of Grassland Research, Chinese Academy of Agricultural Sciences China

Dr. Fei Li is a prominent research professor at the Institute of Grassland Research, Chinese Academy of Agricultural Sciences (CAAS). Specializing in the remote sensing of grassland ecology and big data, Dr. Li leverages satellite and UAV remote sensing along with AI algorithms to advance ecological research. With over 40 academic papers published in top-tier journals, Dr. Li’s contributions have significantly impacted the field of ecological and biological sciences.

 

Profile

Scopus

Education πŸŽ“

Dr. Li earned his Ph.D. in Cartography and Geographic Information Systems from the University of Chinese Academy of Sciences in 2014. Prior to this, he completed his M.S. in Cartography and Geographic Information Systems and his B.S. in Geographic Information Systems from Northwest Normal University in 2009 and 2006, respectively.

Experience πŸ†

Dr. Li’s extensive experience includes his current role as a research professor at CAAS since 2020. He previously served as a research assistant at the University of Tennessee, Michigan State University, and the Chinese Academy of Sciences. His diverse experience in both academic and research institutions has equipped him with a robust understanding of ecological processes and remote sensing technologies.

Research Interests πŸ”¬

Dr. Li’s research interests lie in integrating ecological process models with remote sensing observations and machine learning approaches. He focuses on simulating global-regional carbon-water cycles and investigating their response mechanisms. Additionally, he is dedicated to utilizing big data from remote sensing for effective grassland resource monitoring and management.

Awards πŸ…

Dr. Li has been recognized with numerous awards and grants from prestigious organizations such as NSF, NASA, DOE, NSA, and ESA. His groundbreaking work in remote sensing and ecological modeling has earned him accolades and funding for various high-impact projects.

Publications Top Notes πŸ“š

Dr. Li has an impressive portfolio of publications, including:

Li, H., Li, F.*, Xiao, J., Chen, J., Lin, K., Bao, G., … & Wei, G. (2024). A machine learning scheme for estimating fine-resolution grassland aboveground biomass over China with Sentinel-1/2 satellite images. Remote Sensing of Environment, 311, 114317. Cited by 10 articles.

Read here

Li, F., Xiao, J., Chen, J., Ballantyne, A., Jin, K., Li, B., … & John, R. (2023). Global water use efficiency saturation due to increased vapor pressure deficit. Science, 381(6658), 672-677. Cited by 25 articles.

Read here

Yan, H., Li, F.*, & Liu, G. (2023). Diminishing influence of negative relationship between species richness and evenness on the modeling of grassland Ξ±-diversity metrics. Frontiers in Ecology and Evolution, 11, 154. Cited by 15 articles.

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Ouyang, Z., Sciusco, P., Jiao, T., Feron, S., Lei, C., Li, F., … & Chen, J. (2022). Albedo changes caused by future urbanization contribute to global warming. Nature Communications, 13(1), 3800. Cited by 30 articles.

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Brabazon, H., DeBruyn, J. M., Lenaghan, S. C., Li, F., Mundorff, A. Z., Steadman, D. W., & Stewart Jr, C. N. (2020). Plants to Remotely Detect Human Decomposition?. Trends in Plant Science, 25(10), 947-949. Cited by 20 articles.

Read here