Mr. Yumeng Su | Engineering | Best Researcher Award

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

Su Yumeng , a top-ranking computer science undergraduate at Shanghai Jian Qiao University , excels in AI , robotics , and embedded systems . With hands-on experience in drone development, ROS, and deep learning , he has published impactful research and led award-winning teams . His technical expertise spans Python, MATLAB, LaTeX, and advanced hardware platforms like Jetson Nano and STM32 . Beyond academics, he’s a dynamic leader and athlete , known for his resilience and innovation in intelligent systems and smart hardware applications . 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.

Education & Experience

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

• Focus: AI, Robotics, Embedded Systems, and Smart Hardware

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

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

• Internship at Superdimension Technology Space: Autonomous drone development

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

Professional Development

Su Yumeng continually advances his professional skills through academic projects , interdisciplinary competitions , 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 . 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 . With practical ROS experience and sensor fusion expertise, he remains at the forefront of smart automation and robotics .

Research Focus Category

Su Yumeng’s research focuses on Artificial Intelligence in Embedded and Autonomous Systems , 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 , baby posture recognition on embedded platforms , and SLAM-based navigation for wheeled robots . His interdisciplinary approach merges hardware innovation with AI, yielding scalable, intelligent, and responsive systems suitable for civil engineering, healthcare, and autonomous mobility fields .

Awards & Honors

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

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

• Shanghai Gold Award, Career Planning Competition (2024)

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

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

• Bronze Award, “Challenge Cup” Entrepreneurship Plan Competition

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

• National Motivational Scholarship ×3

• President “Qing Yun” Scholarship

• 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