Xingjia Li | Robotics | Best Researcher Award

Dr. Xingjia Li | Robotics | Best Researcher Award

Senior Engineer at Shanghai Liangxin Electrical Co., Ltd., China.

Dr. Xingjia Li πŸŽ“ is a dynamic early-career researcher specializing in robotics πŸ€– and electrical systems ⚑. He earned his Ph.D. in Mechanical Engineering from Jiangsu University, China πŸ‡¨πŸ‡³, in 2023. Currently, he is a Postdoctoral Associate at Shanghai Liangxin Electrical Co., Ltd. 🏒, where he explores advanced sensor data processing integrated with machine learning πŸ€–πŸ“Š. His work is focused on creating intelligent and secure systems for human-centered applications πŸ§ πŸ”’. Passionate about innovation and industrial collaboration, Dr. Li aims to bridge the gap between research and real-world impact πŸŒπŸ’Ό.

Professional Profile:

ORCID

Suitability for Best Researcher Award – Dr. Xingjia Li

Dr. Xingjia Li is a highly promising early-career researcher whose work stands at the intersection of robotics, electrical systems, and machine learning. While he is still in the early stages of his academic journey, his innovative research direction, strong industrial collaboration, and interdisciplinary approach make him a strong contender for the Best Researcher Award in the early-career or emerging researcher category. His focus on human-centered intelligent systems showcases a commitment to solving real-world problems using advanced technologies, a quality that aligns perfectly with the spirit of this award.

πŸ”Ή Education & Experience

πŸŽ“ Ph.D. in Mechanical Engineering – Jiangsu University, Zhenjiang, China (2023)
πŸ§ͺ Postdoctoral Associate – Shanghai Liangxin Electrical Co., Ltd., Postdoctoral Workstation, Shanghai, China (2023–Present)
πŸ”¬ Research Interests – Robotics πŸ€–, Electrical Systems ⚑, Sensor Data Processing with Machine Learning πŸ“ˆ
πŸ”— Application Areas – Human-centered Systems πŸ§β€β™‚οΈπŸ›‘οΈ, Automation 🀝, Smart Devices πŸ“²

πŸ”Ή Professional Development

Dr. Li is committed to continuous professional growth through industrial collaboration and advanced research 🀝πŸ§ͺ. At Shanghai Liangxin Electrical Co., Ltd., he participates in practical projects focused on secure and intelligent automation systems πŸ”’πŸ€–. He actively engages in interdisciplinary learning, integrating machine learning, AI, and electrical systems to enhance innovation πŸ“šπŸ’‘. His exposure to both academic and industrial environments enables him to develop real-world applications that solve current technological challenges πŸŒπŸ› οΈ. By staying updated through research networks, technical seminars, and collaboration, Dr. Li positions himself as a forward-thinking researcher πŸ“–πŸŒ.

πŸ”Ή Research Focus CategoryΒ 

Dr. Xingjia Li’s research falls under AI-driven robotics and smart electrical systems πŸ€–βš‘. He specializes in applying machine learning techniques to process sensor data πŸ“ŠπŸ§ , enabling systems to become more intelligent, secure, and adaptive to human needs. His focus includes cyber-physical systems, human-machine interfaces, and automation technologies πŸ’»πŸ”§. These technologies have broad applications in healthcare, industrial automation, and smart homes πŸ₯🏭🏠. Dr. Li’s interdisciplinary approach combines mechanical engineering, computer science, and electrical design to create next-generation human-centric innovations 🌐🀝.

πŸ”Ή Awards & Honors

As a recent Ph.D. graduate and emerging researcher, Dr. Xingjia Li πŸŽ“ is in the early stages of building his academic and professional recognition profile. While there are currently no publicly documented awards or honors πŸ…, his active involvement in cutting-edge research projects and collaboration with industry through Shanghai Liangxin Electrical Co., Ltd. 🏒 positions him well for future accolades. With continued publication of impactful research, participation in international conferences 🌐, and contributions to innovation in robotics and machine learning πŸ€–πŸ“Š, Dr. Li is poised to earn distinctions such as best paper awards, patents, or young researcher honors in the near future πŸŒŸπŸ“ˆ.

Publication Top Notes

1. Optimization of Piezoelectric Energy Harvester Using Equilibrium Optimizer Algorithm

  • Conference: 16th Symposium on Piezoelectricity, Acoustic Waves, and Device Applications (SPAWDA)

  • Date: October 11, 2022

  • DOI: 10.1109/spawda56268.2022.10046019

  • Contributors: Jian Sun, X. J. Li, J. N. Gu, M. L. Pu, H. Chen

  • Summary: This paper presents a novel approach to enhance the efficiency of piezoelectric energy harvesters by applying the Equilibrium Optimizer algorithm, a nature-inspired metaheuristic, for optimal parameter tuning. The method improves energy output and system stability.

2. Tuning ANFIS Using a Simplified Sparrow Search Algorithm

  • Journal: Advances in Transdisciplinary Engineering

  • Date: February 10, 2022

  • DOI: 10.3233/ATDE220091

  • Contributor: Xingjia Li

  • Summary: This study applies a simplified version of the Sparrow Search Algorithm to optimize the parameters of the Adaptive Neuro-Fuzzy Inference System (ANFIS), enhancing its performance in complex engineering problems.

3. A Numerical Approach for Flexoelectric Energy Harvester Modeling Using COMSOL Multiphysics

  • Conference: 15th Symposium on Piezoelectricity, Acoustic Waves and Device Applications (SPAWDA)

  • Date: June 4, 2021

  • DOI: 10.1109/spawda51471.2021.9445427

  • Contributor: Xingjia Li

  • Summary: This paper proposes a numerical model of a flexoelectric energy harvester using COMSOL Multiphysics, addressing the coupling of mechanical and electrical domains to predict device performance accurately.

4. A Fusion Parameter Method for Classifying Freshness of Fish Based on Electrochemical Impedance Spectroscopy

  • Journal: Journal of Food Quality

  • Date: March 10, 2021

  • DOI: 10.1155/2021/6664291

  • Contributors: Jian Sun, Yuhao Liu, Gangshan Wu, Yecheng Zhang, Rongbiao Zhang, X. J. Li, Daniel Cozzolino

  • Summary: The research introduces a fusion parameter technique combining electrochemical impedance spectroscopy data to accurately classify fish freshness, demonstrating potential for food quality control applications.

5. Research on the Actuation Performance of 2D-Orthotropic Piezoelectric Composite Materials Linear Phased Array Transducer

  • Journal: Journal of Nanoscience and Nanotechnology

  • Date: August 1, 2019

  • DOI: 10.1166/jnn.2019.16814

  • Contributor: Xingjia Li

  • Summary: This article investigates the actuation performance of a 2D-orthotropic piezoelectric composite used in linear phased array transducers, highlighting the material’s anisotropic effects on acoustic wave propagation.

6. Design and Optimization for Double-Sided Interdigital Transducer with Piezoelectric Substrate

  • Conference: 13th Symposium on Piezoelectricity, Acoustic Waves and Device Applications (SPAWDA)

  • Date: January 11, 2019

  • Contributor: Xingjia Li

  • Summary: The paper focuses on the design and optimization of double-sided interdigital transducers on piezoelectric substrates to improve device efficiency and sensitivity for acoustic applications.

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Conclusion

While Dr. Li is at the beginning of his research career, his exceptional potential, innovation-driven mindset, and strong research focus make him a suitable candidate for the Best Researcher Award (Emerging Researcher Category). His contributions already demonstrate the capacity to shape the future of robotics and intelligent systems. With continued research output and growing industrial impact, Dr. Li is on a clear path to becoming a leading figure in his field.

Weichen Zhang | Deep Learning | Best Researcher Award

Dr. Weichen Zhang | Deep Learning | Best Researcher Award

Postdoc Researcher at University of Sydney, Australia.

Dr. Weichen Zhang πŸŽ“ is a passionate AI Researcher and Engineer specializing in 2D/3D visual applications, currently working as a Postdoctoral Researcher at the University of Sydney πŸ‡¦πŸ‡Ί. With a strong academic background and over 8 years of hands-on experience, he has contributed significantly to deep learning, point cloud analysis, and 3D human body reconstruction πŸ€–. Formerly the R&D Team Lead at Bodymapp Pty Ltd, he led innovations in AI, SLAM, and AWS MLOps pipelines ☁️. His work is well-recognized with publications in top-tier venues like CVPR, T-PAMI, and T-IP, and two U.S. patents for AI technologies πŸ†.

Professional Profile:

ORCID

Google Scholar

Suitability for Best Researcher Award – Dr. Weichen Zhang

Dr. Weichen Zhang is an outstanding candidate for the Best Researcher Award due to his groundbreaking contributions to the fields of AI, 2D/3D computer vision, and deep learning. With over 8 years of rich academic and industrial experience, he has demonstrated a rare combination of technical depth, publication excellence, and innovation in applied AI. His research has made substantial impact in areas such as point cloud analysis, 3D human body reconstruction, SLAM, and domain adaptation, positioning him as a rising star in AI and computer vision.

πŸ“˜ Education

  • πŸŽ“ Ph.D. in Deep Transfer Learning, University of Sydney, Australia (2017–2021)

  • πŸŽ“ B.I.T. (Hons Class I), University of Sydney, Australia (2013–2016)

  • πŸŽ“ High School, Affiliated to Nanjing Normal University, China (2009–2012)

πŸ’Ό Experience

  • 🧠 Postdoctoral Research Associate, University of Sydney (Sep 2024–Present)

  • πŸ”¬ R&D Team Lead & Research Engineer, Bodymapp Pty Ltd (May 2021–Aug 2024)

  • πŸ‘¨β€πŸ« Academic Tutor, University of Sydney (Jul 2019–Dec 2019)

πŸ› οΈ Professional DevelopmentΒ 

Dr. Zhang continually evolves his professional skillset through advanced research collaborations 🀝, cross-disciplinary project leadership, and active participation in academic publishing πŸ“š. His professional growth is marked by practical application of machine learning, neural networks, DevOps, and 3D vision technologies across academia and industry. With a strong command of programming languages like Python, C++, and MATLAB, he has built scalable AI systems and AWS MLOps pipelines ☁️. He also contributes to academic instruction, tutoring deep learning and transfer learning concepts. Zhang’s engagement with top research institutions and conferences keeps him at the cutting edge of AI innovation πŸš€.

πŸ§ͺ Research Focus Area

Dr. Weichen Zhang’s research is focused on 2D/3D visual understanding, including point cloud analysis, 3D mesh reconstruction, SLAM, and deep learning model generalization πŸ”. His projects span cross-domain recognition, domain adaptation, and privacy-preserving avatar reconstruction technologies πŸ‘€. He integrates supervised and unsupervised learning methods to solve complex problems in autonomous driving πŸš—, human body tracking, and forestry analytics 🌳. Zhang’s contributions are reflected in CORE A* journals and conferences, showing his commitment to pushing the boundaries of scalable, multimodal, and multi-sensor visual systems in real-world applications πŸ“Š.

πŸ… Awards and Honours

  • πŸ₯‡ Research Training Program (RTP) Scholarship, University of Sydney (2017–2021)

  • πŸŽ–οΈ Norman I. Price Scholarship, University of Sydney (2018–2019)

  • 🌟 CVPR 2018 Paper Spotlight, Salt Lake City, USA (2018)

  • πŸ† USYD-CSIRO Summer Research Scholarship, University of Sydney (2016–2017)

Publication Top Notes

πŸ“˜ 1. Collaborative and Adversarial Network for Unsupervised Domain Adaptation

  • Authors: W. Zhang, W. Ouyang, W. Li, D. Xu

  • Published in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • Year: 2018

  • Cited by: 628

  • Summary: This paper presents a novel framework combining collaborative learning and adversarial training to improve performance in unsupervised domain adaptation. It effectively aligns feature distributions between source and target domains.

πŸ“˜ 2. Model Compression using Progressive Channel Pruning

  • Authors: J. Guo, W. Zhang, W. Ouyang, D. Xu

  • Published in: IEEE Transactions on Circuits and Systems for Video Technology

  • Year: 2020

  • Cited by: 74

  • Summary: The authors propose a progressive pruning method that reduces the number of channels in convolutional neural networks, enabling model compression with minimal accuracy loss. The method is data-driven and layer-wise.

πŸ“˜ 3. SRDAN: Scale-aware and Range-aware Domain Adaptation Network for Cross-dataset 3D Object Detection

  • Authors: W. Zhang, W. Li, D. Xu

  • Published in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

  • Year: 2021

  • Cited by: 54

  • Summary: SRDAN is a domain adaptation network designed for 3D object detection. It introduces scale-aware and range-aware components to address distribution shifts between datasets, improving detection across varying sensor configurations.

πŸ“˜ 4. Progressive Modality Cooperation for Multi-modality Domain Adaptation

  • Authors: W. Zhang, D. Xu, J. Zhang, W. Ouyang

  • Published in: IEEE Transactions on Image Processing, Vol. 30, pp. 3293–3306

  • Year: 2021

  • Cited by: 24

  • Summary: This work proposes a progressive learning scheme where different modalities (e.g., RGB and depth) are learned in stages to enhance domain adaptation. The model progressively integrates information, improving generalization.

πŸ“˜ 5. 3D Hand Pose Estimation with Disentangled Cross-Modal Latent Space

  • Authors: J. Gu, Z. Wang, W. Ouyang, W. Zhang, J. Li, L. Zhuo

  • Published in: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 391–400

  • Year: 2020

  • Cited by: 24

  • Summary: This paper introduces a cross-modal learning framework that disentangles modality-specific and shared features, enabling accurate 3D hand pose estimation from multi-view data.

Conclusion

Dr. Zhang’s sustained contributions to cutting-edge AI research, proven innovation, and recognized leadership in both academia and industry make him an ideal recipient of the Best Researcher Award. His visionary work continues to push the boundaries of what is possible in machine learning and computer vision, with real-world impact and academic distinction.