Shuxian Lun | Image Classification | Excellence in Computer Vision Award

Posted on by Computer Vision Awards

Prof. Shuxian Lun | Image Classification | Excellence in Computer Vision Award

Dean, School of Control Science and Engineering at Bohai University, China

Dr. Shuxian Lun is a distinguished researcher and academic affiliated with the College of Control Science and Engineering at Bohai University, China. His work spans several cutting-edge domains including artificial intelligence, image processing, fault detection, and new energy power generation technologies. With an impressive portfolio of over 90 SCI and EI-indexed publications, 22 authorized invention patents, and six published books, he has made substantial contributions to the fields of intelligent systems and automation. Dr. Lun has led four general projects and participated in a key project funded by the National Natural Science Foundation of China, demonstrating his leadership and national-level recognition. He also collaborates with researchers globally and is actively involved in consultancy and industry-linked research. As a member of IEEE and Elsevier’s academic networks, Dr. Lun maintains a strong presence in the global scientific community. His innovative mindset and multidisciplinary approach mark him as a leading figure in applied and theoretical research.

Professional Profile 

Education🎓

Dr. Shuxian Lun has built a strong educational foundation that supports his interdisciplinary research in artificial intelligence and computer vision. Although specific degree titles and universities are not detailed, his academic background has clearly equipped him with a deep understanding of control science, electrical engineering, and computational technologies. The breadth and depth of his research outputs—spanning artificial intelligence, fault detection, energy systems, and image processing—suggest rigorous graduate and postgraduate training in science and engineering. His extensive publication record, leadership in national-level projects, and innovation in applied technologies underscore a comprehensive educational journey that bridges theoretical knowledge and practical implementation. Furthermore, his successful authorship of six academic books and his role in mentoring complex R&D projects reflect his solid pedagogical foundation and academic maturity. Dr. Lun’s educational background, though not exhaustively specified, is evidently rooted in strong technical training and a commitment to continuous learning and innovation.

Professional Experience📝

Dr. Shuxian Lun has a prolific professional career as a professor and researcher at the College of Control Science and Engineering, Bohai University, China. His professional experience is marked by a strong record of academic leadership and innovation, particularly in the domains of artificial intelligence, image processing, and new energy systems. He has completed over 90 funded research projects, with two currently ongoing, and has led four general projects under the prestigious National Natural Science Foundation of China. Dr. Lun has also participated in a major key national research project and served as a consultant on five industry-oriented initiatives. His professional role involves supervising multidisciplinary research teams, developing novel technologies, and authoring books and patents. His work has culminated in the development of award-winning smart grid control systems and other technologies of national significance. These accomplishments highlight his capacity for high-impact applied research, academic mentoring, and industry collaboration.

Research Interest🔎

Dr. Shuxian Lun’s research interests lie at the intersection of artificial intelligence, image processing, fault detection, and new energy power generation technologies. He is particularly engaged in applying AI to intelligent control systems and computer vision problems, contributing to real-time monitoring, optimization, and safety in distributed energy networks. His work explores both theoretical algorithms and practical applications, including convolutional neural networks, rapid image recognition techniques, and fault-tolerant systems for smart grids. Dr. Lun also investigates the integration of AI with control engineering to enhance efficiency and reliability in power distribution systems. Furthermore, his involvement in over 90 research projects demonstrates a dynamic interest in advancing both the scientific and practical frontiers of his fields. His interdisciplinary approach enables the seamless integration of machine learning with fault diagnostics, safety assurance, and intelligent automation—areas that are pivotal for next-generation smart technologies and sustainable energy solutions.

Award and Honor🏆

Dr. Shuxian Lun has received multiple prestigious recognitions for his outstanding research and innovation. Notably, he was awarded the First Prize for Scientific and Technological Progress by the China Automation Society for the development of a “complete and practical active distribution network source network load optimization control equipment.” This award underscores the societal and industrial impact of his work in control systems and smart grids. Over the course of his career, he has presided over four general research projects and contributed to a major key project funded by the National Natural Science Foundation of China, showcasing his national-level research leadership. His innovations are further validated by the authorization of 22 invention patents and publication of 6 books. These accolades, combined with his active roles in consultancy and collaboration, reflect his influence not only within academic circles but also in shaping future-ready technologies across energy and automation sectors.

Research Skill🔬

Dr. Shuxian Lun possesses a robust and diverse set of research skills that underpin his excellence in engineering and computer science. He is proficient in the design and implementation of advanced artificial intelligence models, with a focus on computer vision, fault detection, and intelligent control systems. His technical expertise includes developing and optimizing deep learning architectures, processing high-dimensional image data, and engineering fault-tolerant systems for smart grids. He has a strong command of simulation tools, experimental design, and real-time system integration, which are crucial for applied research in control and automation. Dr. Lun also excels in academic writing, having published over 90 SCI/EI papers and 6 books, and in patent development, with 22 inventions to his name. His leadership in over 90 research projects and consultancy engagements illustrates his capacity to translate theoretical concepts into practical, impactful solutions. These capabilities make him a highly versatile and innovative researcher in multidisciplinary engineering domains.

Conclusion💡

Dr. Shuxian Lun is highly suitable for the Best Researcher Award, especially under the Computer Vision Excellence category. His research depth, innovation, national-level project leadership, and significant patent portfolio strongly reflect a top-tier research profile. With a sharper emphasis on core computer vision outcomes and citation impact in future applications, his candidacy would be even more compelling on an international stage.

Publications Top Noted✍

  • Adaptive Echo State Network with a Recursive Inverse‑Free Weight Update Algorithm
    Authors: Bowen Wang; Shuxian Lun; Ming Li; Xiaodong Lu; Tianping Tao
    Year: 2023
    Citations: 7 

  • A New Explicit I–V Model of a Silicon Solar Cell Based on Chebyshev Polynomials
    Authors: Shu‑xian Lun; Ting‑ting Guo; Cun‑jiao Du
    Year: 2015

  • A Mahalanobis Hyperellipsoidal Learning Machine Class Incremental Learning Algorithm
    Authors: Yuping Qin; Hamid Reza Karimi; Dan Li; Shuxian Lun; Aihua Zhang
    Year: 2014

  • Preparation and Characterization of CdIn₂S₄ Wedgelike Thin Films
    Authors: Lina Zhang; Wei Zhang; Xiaodong Lu; Qiushi Wang; Xibao Yang; Libin Shi; Shuxian Lun
    Year: 2013

  • Preparation and Characterization of Cu₂ZnSnS₄ Thin Films by Solvothermal Method
    Authors: Wei Zhang; Lina Zhang; Xiaodong Lu; Qiushi Wang; Xibao Yang; Libin Shi; Shuxian Lun
    Year: 2013

  • Thermal Evaporation Synthesis and Properties of ZnO Nano/Microstructures Using Sn Reducing Agents
    Authors: Hang Lv; Xibao Yang; Xiaodong Lu; Boxin Li; Qiushi Wang; Lina Zhang; Wei Zhang; Shuxian Lun; Fan Zhang; Hongdong Li
    Year: 2013

  • Amorphous Silicon‑Assisted Self‑Catalytic Growth of FeSi Nanowires in Arc Plasma
    Authors: Qiushi Wang; Xiaodong Lu; Lina Zhang; Lv Hang; Wei Zhang; Yue Wang; Shuxian Lun
    Year: 2013

  • Design of GaAs Solar Cell Front Surface Anti‑Reflection Coating
    Authors: Tao Zhou; Xiaodong Lu; Shuxian Lun; Yuan Li; Ming Zhang; Chunxi Lu
    Year: 2013

  • Reflecting Filters Based on One Dimensional Photonic Crystal with Large Lattice Constant
    Authors: Xiaodong Lu; Shuxian Lun; Tao Zhou; Yuan Li; Chunxi Lu; Ming Zhang
    Year: 2013

 

Dr. Zhou Zhang | Computer Vision | Best Researcher Award

Posted on by Computer Vision Awards

Dr. Zhou Zhang | Computer Vision | Best Researcher Award

Doctorate at SUNY Farmingdale State College, United States

👨‍🎓 Profiles

Scopus

Orcid

Google Scholar

🎓 Early Academic Pursuits

Dr. Zhou (Joe) Zhang embarked on his academic journey with a strong foundation in mechanical and electrical engineering. His early education culminated in a Ph.D. in Mechanical Engineering from Stevens Institute of Technology, where he was awarded the prestigious James Harry Potter Award in 2018 for outstanding doctoral performance. During his doctoral studies, Dr. Zhang explored virtual reality applications in engineering education, including camera pose tracking, data fusion, and the development of virtual laboratories—an area that would become a cornerstone of his future research.

🏫 Professional Endeavors

Dr. Zhang’s academic career is marked by progressive teaching and research roles. He currently serves as an Assistant Professor at SUNY Farmingdale State College, where he teaches Tool Design and Electronics Packaging. Previously, he held key positions including Associate Professor at Middle Tennessee State University and Assistant Professor at CUNY’s New York City College of Technology, where he played a central role in launching and coordinating the Robotics Concentration. His professional journey also includes roles as a Visiting Research Scholar at NYU, a Research Associate at Southeast University, and an Electrical Engineer at CRRC Nanjing Puzhen Co., Ltd, as well as a Mechanical Engineer at CETC’s 14th Research Institute.

🔬 Contributions and Research Focus

Dr. Zhang’s research bridges academic theory and practical implementation. His major contributions span virtual reality (VR) and augmented reality (AR) for engineering education, AI and machine learning applications in robotics, force-feedback robotics, and bio-inspired virtual assembly systems. His work has been funded by institutions such as CUNY GRTI and CUNY Research Awards, including notable projects like the AI and Machine Learning in Co-Robotics and the Virtual Assembly Platform for Engineering Education. Earlier in his career, he was also involved in state-funded research in China, including a $5 million smart controller project backed by the State Grid Corporation of China.

🌍 Impact and Influence

Dr. Zhang has made a tangible impact on student development, workforce readiness, and interdisciplinary education. His initiatives include establishing co-op and internship collaborations with industry, mentoring undergraduate research, and leading programs like the Virtual Reality and Artificial Intelligence Club. He also contributed to maintaining ABET accreditation, aligning curriculum development with institutional and industry standards. His mentorship has supported student participation in key events such as the Brooklyn Navy Yard Competition, Maker Faire, and the CUNY Black Male Initiative Conference.

📚 Academic Citations & Publications

Dr. Zhang’s scholarly work is extensively cited in the domains of VR-based education, 3D reconstruction, force-feedback robotics, and embedded systems. His contributions have not only advanced academic research but also enriched applied engineering education. As one of the main investigators in several NSF-funded projects, his research continues to influence both academic curricula and practical engineering tools.

💻 Technical Skills

Dr. Zhang is proficient in a variety of engineering and programming tools, including virtual reality system design, computer-aided engineering, middleware integration, finite element methods (FEM), and AI/machine learning applications in robotics. His skills encompass real-time 3D reconstruction, electromagnetic field simulation, and embedded systems design, with applications extending to DSP, ARM-based controls, and semiconductor converters.

🧑‍🏫 Teaching Experience

With over two decades of teaching experience, Dr. Zhang has taught a wide array of courses across institutions like SUNY Farmingdale, CUNY, NJIT, and Middle Tennessee State University. His teaching portfolio includes Mechanical Measurement, Stress Analysis, Rapid Prototyping, Programmable Logic Controllers, and AI-integrated robotics courses. He has served in diverse capacities—course designer, club advisor, curriculum developer, and research mentor—demonstrating his commitment to academic excellence and student engagement.

🏆 Awards and Honors

Dr. Zhang has received multiple accolades for his dedication to academic and research excellence. In addition to the James Harry Potter Award, he earned graduate travel grants from Stevens Institute of Technology, recognizing his contributions to engineering research and academic dissemination.

🚀 Legacy and Future Contributions

Dr. Zhang’s legacy lies in his ability to blend innovative research with effective teaching, transforming traditional mechanical engineering education through technology. His future goals include advancing interdisciplinary robotics education, expanding virtual learning platforms, and fostering global academic-industry collaborations. With a career devoted to bridging theoretical knowledge and real-world applications, Dr. Zhang continues to inspire students and colleagues alike, shaping the future of engineering education and technological innovation.

 

Publications

The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection

  • Authors: Momina Liaqat Ali, Zhou Zhang
    Journal: Computers
    Year: 2024

Project-Based Courses for B.Tech. Program of Robotics in Mechanical Engineering Technology

  • Authors: Zhang Z., Zhang A.S., Zhang M., Esche S.
    Journal:     Computers in Education Journal
    Year:     2020

A Virtual laboratory system with biometric authentication and remote proctoring based on facial recognition

  • Authors: Zhang, Z.; Zhang, M.; Chang, Y.; Esche, S.K.; Chassapis, C.
    Journal: Computers in Education
    Year: 2016

Real-time 3D reconstruction for facilitating the development of Game-based virtual laboratories

  • Authors: Zhang, Z.; Zhang, M.; Chang, Y.; Esche, S.K.; Chassapis, C.
    Journal:     Computers in Education
    Year:     2016

Usability evaluation of a virtual educational laboratory platform

  • Authors: Chang, Y.; Aziz, E.-S.S.; Zhang, Z.; Zhang, M.; Esche, S.K.
    Journal: Computers in Education
    Year: 2016

Dr. Ghulam Murtaza | Image Processing | Best Academic Researcher Award

Posted on by Computer Vision Awards

Dr. Ghulam Murtaza | Image Processing | Best Academic Researcher Award

Doctorate at National University of Modern Languages, Pakistan

👨‍🎓 Profiles

Scopus

Orcid

📌 Summary

Dr. Ghulam Murtaza is an Assistant Professor in the Department of Mathematics at the National University of Modern Languages (NUML), Islamabad. His research focuses on developing new mathematical models in cryptography, particularly in elliptic curve and chaotic maps-based cryptosystems. With a passion for innovation, he mentors students and actively contributes to cutting-edge research in mathematical cryptography and machine learning-based cryptosystems.

🎓 Education

  • PhD in Mathematics (2019–2023) – Quaid-i-Azam University
    Dissertation: Image Cryptosystems Using Elliptic Curve Cryptography

  • MPhil in Mathematics (2015–2017) – Quaid-i-Azam University
    Dissertation: Learning From Data Using Algebraic Geometry

  • MSc in Mathematics (2013–2015) – Quaid-i-Azam University

  • BSc in Mathematics & Physics (2010–2012) – Bahauddin Zakariya University

👨‍🏫 Professional Experience

  • Assistant Professor – NUML, Islamabad (2023–Present)

  • Visiting Assistant Professor – Quaid-i-Azam University (2023)

  • Visiting Lecturer – Quaid-i-Azam University (2023–2024)

  • Lecturer – University of Lahore, Pakpattan Campus (2017–2019)

🏆 Awards & Honors

  • First-class academic record from Matric to MPhil

  • Mrs. Rehmat Shahbuddin Memorial Scholarship (MSc, 2013–2015)

  • Merit Scholarship – Quaid-i-Azam University

  • Shahbaz Sharif Youth Initiative Laptop Scheme (2012)

🔬 Research Interests

  • Elliptic Curve Cryptography

  • Chaotic Maps-Based Cryptography

  • Machine Learning for Cryptosystems

  • Dynamical Systems & Isogeny-Based Cryptography

 

Publications

Efficient Image Encryption Algorithm Based on ECC and Dynamic S-Box

  • Author: Ghulam Murtaza, Umar Hayat
    Journal: Journal of Information Security and Applications
    Year: 2025

Enumerating Discrete Resonant Rossby/Drift Wave Triads and Their Application in Information Security

  • Author: Umar Hayat, Ikram Ullah, Ghulam Murtaza, Naveed Ahmed Azam, Miguel D. Bustamante
    Journal: Mathematics
    Year: 2022

Designing an Efficient and Highly Dynamic Substitution-Box Generator for Block Ciphers Based on Finite Elliptic Curves

  • Author: Ghulam Murtaza, Naveed Ahmed Azam, Umar Hayat, Iqtadar Hussain
    Journal: Security and Communication Networks
    Year: 2021

Mr. Jiahao Nie | Image Processing | Best Researcher Award

Posted on by Computer Vision Awards

Mr. Jiahao Nie | Image Processing | Best Researcher Award

Hangzhou Dianzi University, China

👨‍🎓 Profiles

Scopus

Google Scholar

📌 Summary

Mr. Jiahao Nie is a dedicated Ph.D. candidate at Hangzhou Dianzi University (HDU) and Hanyang University (HYU), specializing in computer vision, 2D image processing, and 3D point cloud processing. Under the guidance of Prof. Zhiwei He and Assoc. Prof. Dong-Kyu Chae, he is actively engaged in cutting-edge research in autonomous driving and object tracking.

🎓 Education

  • Ph.D. in Electronic Science and Technology (HDU, 2022-2025)
  • Joint Ph.D. in Computer Science (HYU, 2024-2025)
  • B.Eng. in Electronic Information Engineering (HDU, 2020-2022)

🔬 Research Interests

His research is primarily focused on computer vision, including 2D image processing, 3D point cloud processing, and object tracking for autonomous driving.

🏆Honors & Awards

  • Ph.D. National Scholarship (Rank: 1/75) | Full Postgraduate Scholarship (2020-2025)
  • First-Class Academic Scholarship (Top 3%) | National Scholarship for Studying Abroad (2023)

📑 Academic Contributions

  • Reviewer for ICCV, CVPR, ICLR, ICML, ECCV, NeurIPS, AAAI, ACM MM
  • Presenter at ICLR (2024), IJCAI (2023), AAAI (2023)

 

Publications

TTSNet: state-of-charge estimation of Li-ion battery in electrical vehicles with temporal transformer-based sequence network

  • Authors: Zhengyi Bao, Jiahao Nie, Huipin Lin, Kejie Gao, Zhiwei He, Mingyu Gao
  • Journal: IEEE Transactions on Vehicular Technology
  • Year: 2024

A fine-grained feature decoupling based multi-source domain adaptation network for rotating machinery fault diagnosis

  • Authors: Xiaorong Zheng, Jiahao Nie, Zhiwei He, Ping Li, Zhekang Dong, Mingyu Gao
  • Journal: Reliability Engineering & System Safety
  • Year: 2024

A progressive multi-source domain adaptation method for bearing fault diagnosis

  • Authors: Xiaorong Zheng, Zhiwei He, Jiahao Nie, Ping Li, Zhekang Dong, Mingyu Gao
  • Journal: Applied Acoustics
  • Year: 2024

Dual-task learning for joint state-of-charge and state-of-energy estimation of lithium-ion battery in electric vehicle

  • Authors: Zhengyi Bao, Jiahao Nie, Huipin Lin, Zhi Li, Kejie Gao, Zhiwei He, Mingyu Gao
  • Journal: IEEE Transactions on Transportation Electrification
  • Year: 2024

TM2B: Transformer-Based Motion-to-Box Network for 3D Single Object Tracking on Point Clouds

  • Authors: Anqi Xu, Jiahao Nie*, Zhiwei He, Xudong Lv
  • Journal: IEEE Robotics and Automation Letters
  • Year: 2024

Dr. Hua Ren | Image Processing | Best Researcher Award

Posted on by Computer Vision Awards

Dr. Hua Ren | Image Processing | Best Researcher Award

Doctorate at Henan Normal University, China

👨‍🎓 Profiles

Scopus

Orcid

📌 Summary

Dr. Hua Ren is a dedicated researcher and lecturer specializing in image security, encryption, and data hiding. His expertise lies in visually secure encryption and authentication technologies, contributing significantly to high-impact journals and research projects in these domains.

🎓 Education

  • Ph.D. in Computer Science and Technology, Beijing University of Posts and Telecommunications (2019-2023)
  • Master’s in Computer Science and Technology, Henan Normal University (2016-2019)
  • Bachelor’s in Computer Science and Technology, Henan Normal University (2012-2016)

💼 Work & Research Experience

  • Lecturer (2023-Present) – School of Computer and Information Engineering, Henan Normal University
  • Principal Investigator – Henan Science and Technology Research Project on image reversible authentication (2025-2026)

🔬 Research Interests

  • Image Security & Encryption
  • Reversible Data Hiding
  • Visual Authentication & Cryptography
  • Digital Image Processing

 

Publications

A novel reversible data hiding method in encrypted images using efficient parametric binary tree labeling

  • Authors: Hua Ren, Zhen Yue, Feng Gu, Ming Li, Tongtong Chen, Guangrong Bai
  • Journal: Knowledge-Based Systems
  • Year: 2024

Multi-scale attention context-aware network for detection and localization of image splicing

  • Authors: Ruyong Ren, Shaozhang Niu, Junfeng Jin, Jiwei Zhang, Hua Ren, Xiaojie Zhao
  • Journal: Applied Intelligence
  • Year: 2023

ERINet: Efficient and robust identification network for image copy-move forgery detection and localization

  • Authors: Ruyong Ren, Shaozhang Niu, Junfeng Jin, Keyang Xiong, Hua Ren
  • Journal: Applied Intelligence
  • Year: 2023

ESRNet: Efficient Search and Recognition Network for Image Manipulation Detection

  • Authors: Ruyong Ren, Shaozhang Niu, Hua Ren, Shubin Zhang, Tengyue Han, Xiaohai Tong
  • Journal: ACM Transactions on Multimedia Computing, Communications, and Applications
  • Year: 2022

Joint encryption and authentication in hybrid domains with hidden double random-phase encoding

  • Authors: Hua Ren, Shaozhang Niu
  • Journal: Multimedia Tools and Applications
  • Year: 2022

Dr. Recai Yilmaz | Applications of Computer Vision | Best Researcher Award

Posted on by Computer Vision Awards

Dr. Recai Yilmaz | Applications of Computer Vision | Best Researcher Award

Doctorate at Children’s National Hospital, Washington, D.C, United States

👨‍🎓 Profiles

Scopus

Orcid

Google Scholar

🎓 Early Academic Pursuits

Dr. Recai Yilmaz’s academic journey began with a strong foundation in medicine, earning his M.D. from Istanbul Faculty of Medicine in 2017. His passion for medical innovation led him to pursue a Ph.D. in Experimental Surgery at McGill University, focusing on neurosurgical simulation and artificial intelligence. His early education at Private Anafen Gaye High School in Istanbul, where he was a full-scholarship student, demonstrated his academic excellence from a young age.

💼 Professional Endeavors

Dr. Yilmaz has amassed extensive experience at the intersection of medicine, artificial intelligence, and computer vision. As a Postdoctoral Research Fellow at Children’s National Medical Center, Washington, D.C., he applies computer vision and machine learning to intraoperative surgical video analysis, aiming to improve real-time surgical performance assessment. His tenure at MultiCIM Technologies Inc. (CareChain) further reflects his leadership in integrating AI into patient triage and clinical decision-making systems.

🔬 Contributions and Research Focus

Dr. Yilmaz’s research is centered on AI-driven surgical assessment, medical data organization, and neurosurgical simulation. At McGill University’s Neurosurgical Simulation and Artificial Intelligence Learning Centre, he developed virtual reality surgical simulation models, advanced AI-based assessment tools, and real-time feedback mechanisms for neurosurgical expertise evaluation. His research also includes cloud-based medical data management and optical flow analysis in surgical procedures.

🌍 Impact and Influence

His pioneering work has significantly influenced AI applications in surgery and clinical decision-making. By integrating computer vision and deep learning into medical practice, he has improved the efficiency and accuracy of surgical skill evaluation, patient triage, and clinical outcome prediction. His projects have not only enhanced surgical education but also contributed to safer and more effective surgical procedures worldwide.

📚 Academic Citations and Recognitions

Dr. Yilmaz has been recognized with numerous awards and grants, including the prestigious Innovator of the Year Award (2023) by the Congress of Neurological Surgeons and research funding from the Brain Tumour Foundation of Canada and the Royal College of Physicians and Surgeons of Canada. His work has been published in high-impact journals and conferences, advancing the field of AI in medicine.

💻 Technical Expertise

  • Artificial Intelligence & Machine Learning (Medical AI applications, Neural Networks)
  • Computer Vision & Image Processing (Surgical video analysis, Optical flow)
  • Programming Languages (Python, MATLAB, C++, IBM SPSS)
  • Statistical Analysis & Data Science (AI-driven performance assessment, Data modeling)

🎓 Teaching and Mentorship

Dr. Yilmaz has actively mentored graduate students, medical researchers, and undergraduate students in AI, neurosurgical simulation, and data analysis. His mentorship spans institutions such as McGill University and Marianopolis College, where he has guided students in machine learning applications, research methodologies, and clinical AI integration.

🌟 Legacy and Future Contributions

Dr. Yilmaz’s legacy lies in his commitment to bridging AI and medicine. His contributions to surgical performance evaluation, AI-driven triage systems, and neurosurgical education continue to shape the future of AI-assisted medical practice. Moving forward, he aims to expand AI integration in real-time surgical decision-making, enhance global accessibility to AI-driven surgical training, and pioneer intelligent healthcare solutions.

 

Publications

AI in surgical curriculum design and unintended outcomes for technical competencies in simulation training

  • Authors:Ali M Fazlollahi, Recai Yilmaz, Alexander Winkler-Schwartz, Nykan Mirchi, Nicole Ledwos, Mohamad Bakhaidar, Ahmad Alsayegh, Rolando F Del Maestro
  • Journal: JAMA network open
  • Year: 2023

Utilizing artificial intelligence and electroencephalography to assess expertise on a simulated neurosurgical task

  • Authors: Sharif Natheir, Sommer Christie, Recai Yilmaz, Alexander Winkler-Schwartz, Khalid Bajunaid, Abdulrahman J Sabbagh, Penny Werthner, Jawad Fares, Hamed Azarnoush, Rolando Del Maestro
  • Journal: Computers in Biology and Medicine
  • Year: 2023

O022 real-time artificial intelligence instructor vs expert instruction in teaching of expert level tumour resection skills–a randomized controlled trial

  • Authors: R Yilmaz, M Bakhaidar, A Alsayegh, R Del Maestro
  • Journal: British Journal of Surgery
  • Year: 2023

Effect of artificial intelligence tutoring vs expert instruction on learning simulated surgical skills among medical students: a randomized clinical trial

  • Authors: Ali M Fazlollahi, Mohamad Bakhaidar, Ahmad Alsayegh, Recai Yilmaz, Alexander Winkler-Schwartz, Nykan Mirchi, Ian Langleben, Nicole Ledwos, Abdulrahman J Sabbagh, Khalid Bajunaid, Jason M Harley, Rolando F Del Maestro
  • Journal: JAMA network open
  • Year: 2022

Assessment of learning curves on a simulated neurosurgical task using metrics selected by artificial intelligence

  • Authors: Nicole Ledwos, Nykan Mirchi, Recai Yilmaz, Alexander Winkler-Schwartz, Anika Sawni, Ali M Fazlollahi, Vincent Bissonnette, Khalid Bajunaid, Abdulrahman J Sabbagh, Rolando F Del Maestro
  • Journal: Journal of neurosurgery
  • Year: 2022

Prof. Nema Salem | Image Processing | Best Researcher Award

Posted on by Computer Vision Awards

Prof. Nema Salem | Image Processing | Best Researcher Award

Professor at Effat University, Saudi Arabia

👨‍🎓 Profiles

Scopus

Orcid

Google Scholar

🎓 Early Academic Pursuits

Prof. Nema Salem’s academic journey began with a strong foundation in engineering and medical imaging. She earned her B.Sc. with honors in 1987 and later obtained her M.Sc. in 1990 from Alexandria University (AU), Egypt, specializing in mitral valve diagnosis. She further pursued her Ph.D. in “Classification of Breast Tumors by Acutance Measure and Shape Factors” through a joint program between the University of Calgary, Canada, and AU in 1996. Her early research laid the groundwork for advancements in medical diagnostics, particularly in breast cancer detection, setting the stage for a distinguished academic and research career.

🏆 Professional Endeavors

Prof. Salem’s professional trajectory spans multiple prestigious institutions. Since 1987, she has held progressive academic roles at AU, the Asian Institute of Technology (AIT), Hadramout University in Yemen, and Effat University in Saudi Arabia, where she has been an Assistant Professor since 2008. She has also served as the Chair of the Electrical and Computer Engineering Department at Effat University, contributing to curriculum development and accreditation processes such as NCAAA and ABET. Her leadership extends beyond academia, as she has organized international competitions like the IET GCC Robotics Challenge and the World Robot Olympiad, promoting innovation among young engineers.

🔬 Contributions and Research Focus

Prof. Salem’s research portfolio is marked by interdisciplinary contributions in medical imaging, artificial intelligence, control systems, and renewable energy. She has pioneered AI-driven applications, including ECG analysis, skin lesion segmentation, and glaucoma detection, enhancing the accuracy of medical diagnostics. Additionally, she has played a crucial role in renewable energy advancements, optimizing solar power generation and thermoelectric systems. Her expertise in robotics and control engineering is evident in her work on PID and LQR controllers for performance enhancement in automation and energy-efficient designs.

🌍 Impact and Influence

Prof. Salem’s influence extends beyond her research, as she actively mentors students, supervises master’s and Ph.D. theses, and collaborates with international researchers. Her dedication to fostering innovation has resulted in students winning prestigious awards, including a bronze medal at the 49th International Exhibition in Geneva. She has also contributed significantly to the academic community through her editorial roles and peer-reviewing for high-impact journals. Her recognition includes the Queen Effat Award for Teaching Excellence (2019-2020, 2022-2023) and a UK Fellowship for teaching excellence, affirming her commitment to quality education and research.

📚 Academic Citations and Publications

Prof. Salem’s research is well-documented in reputable journals and conferences. She has published extensively in IEEE Transactions on Medical Imaging, PLOS ONE, Sensors, and IEEE Access, with a strong presence in high-impact publications. Her work is widely cited, reflecting its significance in medical imaging, artificial intelligence, and renewable energy. Her research contributions are accessible via Google Scholar and the AD Scientific Index 2024, demonstrating her academic reach and influence.

💡 Technical Skills and Expertise

Prof. Salem possesses a diverse technical skill set, encompassing AI-driven signal and image processing, robotics, logic design, and renewable energy optimization. She has expertise in developing machine learning models for medical diagnostics, implementing control strategies for automation, and designing CMOS-based circuits. Her ability to integrate interdisciplinary approaches has made her a sought-after researcher in multiple domains, from biomedical engineering to energy-efficient systems.

📖 Teaching and Mentorship

With over three decades of teaching experience, Prof. Salem has played a pivotal role in shaping the next generation of engineers. She has designed and delivered courses in signal processing, artificial intelligence, control systems, and electronics. Her student-centered approach has been recognized through multiple teaching awards. She actively engages in student mentorship, encouraging innovative research projects and guiding them to success in international competitions and academic publishing.

🔮 Legacy and Future Contributions

Prof. Salem’s legacy is defined by her relentless pursuit of innovation and knowledge dissemination. Her research continues to push the boundaries of technology, particularly in AI-driven healthcare and renewable energy systems. She remains committed to mentoring students, expanding research collaborations, and advancing engineering education. Through her leadership, she aims to drive impactful change in medical diagnostics, sustainable energy, and robotics, ensuring a lasting influence in academia and industry.

 

Publications

Artificially Intelligent Detection of Retinal Pigment Sign Using P3S-Net for Retinitis Pigmentosa Analysis

  • Authors: Syed Muhammad Ali Imran, Abida Hussain, Nema Salem, Muhammad Arsalan
    Journal: Results in Engineering
    Year: 2025

Causal Speech Enhancement Using Dynamical-Weighted Loss and Attention Encoder-Decoder Recurrent Neural Network

  • Authors: Fahad Khalil Peracha, Abdullah M. Mutawa, Muhammad Irfan Khattak, Nema Salem, Nasir Saleem
    Journal: PLOS ONE
    Year: 2023

Artificial Intelligence-Based Detection of Human Embryo Components for Assisted Reproduction by In Vitro Fertilization

  • Authors: Abeer Mushtaq, Maria Mumtaz, Ali Raza, Nema Salem, Muhammad Naveed Yasir
    Journal: Sensors
    Year: 2022

Automated Diagnosis of Leukemia: A Comprehensive Review

  • Authors: Afshan Shah, Syed Saud Naqvi, Khuram Naveed, Nema Salem, Mohammad A. U. Khan, Khurram S. Alimgeer
    Journal: IEEE Access
    Year: 2021

DAVS-NET: Dense Aggregation Vessel Segmentation Network for Retinal Vasculature Detection in Fundus Images

  • Authors: Mohsin Raza, Khuram Naveed, Awais Akram, Nema Salem, Amir Afaq, Hussain Ahmad Madni, Mohammad A. U. Khan, Mui-zzud-din
    Journal: PLOS ONE
    Year: 2021

Ms. Joy Shen | Applications of Computer Vision | Best Researcher Award

Posted on by Computer Vision Awards

Ms. Joy Shen | Applications of Computer Vision | Best Researcher Award

Joy Shen at University of Maryland at College Park, United States

Profiles

Scopus

Orcid

📚 Summary

Ms. Joy Shen is a Ph.D. candidate in Reliability Engineering at the University of Maryland, with expertise in probabilistic risk assessments (PRA) and reliability analysis, particularly for nuclear power systems. She currently works as a Reliability Engineer at NIST, where she develops Bayesian networks and conducts risk-based analyses to improve safety and operational efficiency in nuclear reactors.

Education

  • Ph.D. in Reliability Engineering (Expected May 2025), University of Maryland
  • MSc. in Reliability Engineering (May 2023), University of Maryland
  • B.Sc. in Mechanical Engineering with Nuclear Engineering Minor (Aug 2018), University of Maryland

👩‍🏫 Work Experience

  • Reliability Engineer | NIST, Gaithersburg, MD (Feb 2024 – Present)
    Developed Bayesian network models for safety-related systems in NIST’s research reactor. Analyzed degradation and assisted with relicensing efforts for long-term operations.
  • Mechanical Engineer | NIST, Gaithersburg, MD (Aug 2019 – Aug 2023)
    Performed CFD analysis and developed CAD models for the design of a new neutron source.

🔬 Research Experience

  • Graduate Research Assistant | University of Maryland, College Park, MD
    Conducted pioneering research in external flood PRAs using Monte Carlo augmented Bayesian networks. Investigated nuclear power plant risks and contributed to the U.S. NRC’s efforts to assess system vulnerabilities during external floods.
  • Associate Researcher | NIST, Gaithersburg, MD
    Conducted neutron energy spectrum measurements, proving the viability of Cf-250 as a calibration source for radiation instrumentation.

🛠 Skills and Certifications

  • CAD: Solidworks, Autodesk Inventor
  • Programming: MATLAB, Python, IGOR
  • Accident Analysis: MCNP, RELAP5, TRACE, SNAP
  • Probabilistic Tools: SAPHIRE, GeNIE, Minitab
  • Certifications: 10 CFR 50.59 Training, Procedure Professionals Association (PPA)

🔍 Research Interests

Ms. Joy’s research interests focus on probabilistic risk assessments (PRA), Bayesian networks, nuclear reactor safety, and external flood risk assessments. She is passionate about enhancing the reliability and safety of critical infrastructure through advanced analytical models.

 

Publications

A Monte Carlo augmented Bayesian network approach for external flood PRAs

  • Authors: Shen, J., Bensi, M., Modarres, M.
  • Year: 2025

A Hybrid, Bayesian Network-Based PRA Methodology for External Flood Probabilistic Risk Assessments at Nuclear Power Plants

  • Authors: Shen, J., Frantzis, C., Marandi, S., Bensi, M., Modarres, M.
  • Year: 2023

Synthesis of Insights Regarding Current PRA Technologies for Risk-Informed Decision Making

  • Authors: Shen, J., Marandi, S., Bensi, M., Modarres, M.
  • Year: 2023