Dr. Shengyu Lu | Artificial Intelligence | Young Scientist Award
Tenure-Track Associate Professor, Harbin Engineering University
Shengyu Lu is a Tenure-Track Associate Professor at Harbin Engineering University, specializing in deep learning and computer vision. He obtained his Ph.D. in Computer Science from the University of Southampton (2020-2024) and has a strong background in software engineering and information security. With experience as a lecturer and teaching assistant, he has mentored students in machine learning, image processing, and programming. His research focuses on medical image analysis, particularly using deep learning for fracture discrimination and volumetric texture classification. He has collaborated with leading clinical experts and published extensively in high-impact journals. Shengyu has also served as a reviewer for prestigious journals, including Pattern Recognition and IEEE Transactions on Industrial Informatics. Recognized for his contributions, he has received multiple scholarships and awards, such as the Best Abstract Award in Rheumatology and the Best Oral Presentation Award at the WCSE Conference.
Profile
🎓 Education
Ph.D. in Computer Science (2020-2024) – University of Southampton, specializing in deep learning and medical image analysis under the supervision of Sasan Mahmoodi and Mahesan Niranjan. Master’s in Software Engineering (2017-2020) – Xiamen University, focusing on machine learning, AI, and software development. Bachelor’s in Information Security (2013-2017) – Nanchang University, with expertise in cybersecurity and data protection. Throughout his academic journey, Shengyu Lu has developed advanced AI-driven solutions for medical imaging, object detection, and computational intelligence. His research integrates deep learning with real-world applications, particularly in healthcare and security. He has actively participated in conferences and received multiple scholarships, including the National Merit Scholarship and the China Scholarship Council (CSC) Scholarship. His educational background reflects a strong foundation in AI, computer vision, and software engineering, preparing him for impactful research and teaching.
💼 Work Experience
Lecturer (2024-Present) – Harbin Engineering University, delivering lectures, mentoring students, and conducting AI research. Teaching Assistant (2020-2024) – University of Southampton, guiding master’s students in machine learning, image processing, and Python programming. Mentor (2021-2023) – University of Southampton, assisting master’s students in academic and career development. Teaching Assistant (2018-2019) – Xiamen University, instructing undergraduate students in software engineering and C programming. Shengyu Lu has a strong teaching portfolio, mentoring students in AI-related disciplines and conducting impactful research. His role as a lecturer involves fostering academic excellence while advancing research in deep learning and medical image analysis. With experience in both undergraduate and postgraduate education, he has developed expertise in curriculum development, student mentorship, and academic service.
🏆 Awards and Honors
2022 – Best Abstract Award, Rheumatology 2020 – Best Oral Presentation Award, WCSE Conference 2020 – China Scholarship Council (CSC) Scholarship 2020 – Outstanding Graduate, Xiamen University m2019 – National Merit Scholarship 2019 – “Xuangong Wu” Research Scholarship 2018 – “Huawei” Scholarship 2017 – Outstanding Graduate, Nanchang University 2016-2014 – Multiple Excellent Student and Cadre Awards Recognized for his academic excellence and research contributions, Shengyu Lu has received numerous awards and scholarships. His outstanding performance in AI and deep learning has earned him national and international accolades. His achievements highlight his dedication to cutting-edge research and academic leadership.
🔬 Research Focus
Shengyu Lu’s research centers on deep learning and computer vision, with a particular emphasis on medical image analysis. His work includes designing deep neural networks for fracture discrimination using high-resolution peripheral quantitative computed tomography (HR-pQCT) images. He develops AI-driven volumetric texture analysis techniques to assess cortical and trabecular compartments in bone structure. Collaborating with clinical experts from the MRC Lifecourse Epidemiology Centre and University Hospital, he integrates AI with medical diagnostics. His research extends to object detection, machine learning for protein-protein interactions, and real-time AI applications. He has published extensively in SCI-indexed journals such as Bone, IEEE Access, and Computers & Electrical Engineering. His innovative contributions improve automated medical diagnostics, reducing human error in clinical assessments. His work advances AI’s role in healthcare, bridging the gap between technology and medicine to enhance early disease detection and predictive analytics.
🔍 Conclusion
Shengyu Lu is a strong candidate for the Young Scientist Award, given his impressive academic achievements, impactful research contributions in deep learning for medical imaging, and international recognition. To further strengthen his candidacy, he could focus on securing independent research funding, leading high-impact clinical studies, and increasing first-author publications in top-tier journals.
Publication
Author: S Lu, B Wang, H Wang, L Chen, M Linjian, X Zhang
Title: A real-time object detection algorithm for video
Year: 2019
Citations: 145
Author: S Lu, B Wang, H Wang, Q Hong
Title: A hybrid collaborative filtering algorithm based on KNN and gradient boosting
Year: 2018
Citations: 20
Author: S Lu, H Chen, XZ Zhou, B Wang, H Wang, Q Hong
Title: Graph‐Based Collaborative Filtering with MLP
Year: 2018
Citations: 19
Author: L Chen, B Xu, J Chen, K Bi, C Li, S Lu, G Hu, Y Lin
Title: Ensemble-machine-learning-based correlation analysis of internal and band characteristics of thermoelectric materials
Year: 2020
Citations: 18
Author: S Lu, NR Fuggle, LD Westbury, MÓ Breasail, G Bevilacqua, KA Ward, …
Title: Machine learning applied to HR-pQCT images improves fracture discrimination provided by DXA and clinical risk factors
Year: 2023
Citations: 16
Author: S Lu, Q Hong, B Wang, H Wang
Title: Efficient resnet model to predict protein-protein interactions with GPU computing
Year: 2020
Citations: 15
Author: S Lu, B Wang
Title: An image retrieval algorithm based on improved color histogram
Year: 2019
Citations: 8
Author: NR Fuggle, S Lu, MÓ Breasail, LD Westbury, KA Ward, E Dennison, …
Title: OA22 Machine learning and computer vision of bone microarchitecture can improve the fracture risk prediction provided by DXA and clinical risk factors
Year: 2022
Citations: 5
Author: S Lu, S Mahmoodi, M Niranjan
Title: Robust 3D rotation invariant local binary pattern for volumetric texture classification
Year: 2022
Citations: 4
Author: S Lu, H Chen, L Peng, B Wang, H Wang, X Zhou
Title: A compression algorithm of FASTQ file based on distribution characteristics analysis
Year: 2018
Citations: 1