Lipeng Jiao | Vegetation Disturbance Detection | Best Researcher Award

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Dr. Lipeng Jiao | Vegetation Disturbance Detection | Best Researcher Award

Lecturer at Henan Normal University | China

Lipeng Jiao is a dedicated researcher and academic specializing in deep learning-based remote sensing, with a strong focus on vegetation time-series modeling and disturbance detection. Currently serving as a lecturer at the School of Tourism, Henan Normal University, China, he has developed expertise in integrating advanced computational methods with environmental monitoring and ecological analysis. His career reflects a balance of theoretical knowledge and practical applications, demonstrated by his active role in large-scale national research projects and collaborations with international institutions. With publications in highly regarded journals such as IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing and GIScience & Remote Sensing, he has established himself as a promising scholar in his field. His research contributions address global environmental challenges, particularly in sustainable land use and ecological monitoring. Through his work, he continues to contribute to both academic advancement and societal well-being.

Professional Profiles 

Scopus Profile | ORCID Profile 

Education

Lipeng Jiao has pursued a strong educational foundation in surveying, mapping, and geographic information systems, building a career rooted in both technical depth and interdisciplinary applications. He earned his bachelor’s degree in surveying and mapping engineering from Shangqiu Normal University, which provided him with the fundamental skills for spatial data analysis and geoscience research. He further advanced his expertise with a master’s degree in surveying and mapping engineering from the China University of Mining and Technology in Beijing, where he specialized in advanced mapping technologies and environmental data interpretation. He then completed his doctoral studies in cartography and geographic information systems at Beijing Normal University, focusing on remote sensing and ecological monitoring. In addition to his domestic education, he broadened his academic perspective through an international visiting scholar program at Virginia Tech in the United States, where he collaborated on advanced research in vegetation dynamics and remote sensing applications.

Professional Experience

Lipeng Jiao is currently serving as a lecturer at the School of Tourism, Henan Normal University, where he is actively engaged in teaching, research, and mentoring students in areas related to remote sensing and environmental studies. His professional journey is marked by extensive involvement in major research initiatives, including participation in national key research and development programs in China. He has contributed to projects that focus on global remote sensing monitoring, land use change, and ecological simulations, establishing himself as an integral member of multidisciplinary research teams. His international exposure as a visiting scholar at Virginia Tech in the United States allowed him to collaborate with leading experts and enhance his research perspective. In addition to his teaching and research responsibilities, he actively contributes to the dissemination of knowledge through publications in recognized journals. His professional experience reflects a commitment to combining scientific innovation with practical applications in environmental sustainability.

Research Interest

Lipeng Jiao’s research interests are centered on the application of deep learning techniques in remote sensing, with a particular emphasis on vegetation time-series modeling and the detection of ecological disturbances. He is passionate about developing advanced computational methods that can improve the monitoring and interpretation of environmental changes across diverse ecosystems. His studies focus on vegetation disturbance detection, attribution of change agents, and mapping of ecological processes, which are critical for understanding the impacts of climate change and human activities on natural resources. He is also interested in synergizing multi-source satellite data to achieve near real-time monitoring of phenomena such as burned areas and vegetation degradation. By integrating cutting-edge artificial intelligence methods with remote sensing data, his research contributes to the improvement of global ecological monitoring systems. His interests extend toward practical applications, aiming to support sustainable resource management and policy-making for environmental conservation.

Research Skill

Lipeng Jiao possesses a diverse set of research skills that enable him to address complex challenges in remote sensing and environmental monitoring. He is proficient in applying deep learning algorithms to process and analyze large-scale vegetation time-series data, allowing for the detection and attribution of ecological disturbances with high accuracy. His expertise extends to multi-source satellite data integration, enhancing the capability to conduct near real-time environmental assessments. He is skilled in geographic information systems, cartography, and advanced data analysis methods that support spatial and temporal modeling. His contributions to national research projects highlight his ability to work within interdisciplinary teams, manage data-intensive tasks, and produce impactful outcomes. Additionally, his international research exposure has strengthened his adaptability to diverse scientific approaches and collaborative environments. These skills position him as a researcher capable of advancing both theoretical innovations and practical applications in ecological monitoring and sustainability science.

Publications Top Notes

Title: Robust Identification of Vegetation Change Using Shapelet-Based Temporal Segmentation of Landsat Time-Series Stacks: A Case Study in the Qilian Mountains
Authors: Lipeng Jiao; Randolph H. Wynne
Year: 2025

Title: Near real-time mapping of burned area by synergizing multiple satellites remote-sensing data
Authors: Lipeng Jiao; Yanchen Bo
Year: 2022

Conclusion

Lipeng Jiao is a deserving candidate for the Best Researcher Award due to his significant contributions in applying deep learning to vegetation remote sensing, advancing the understanding of ecological changes and land use impacts. His work on vegetation disturbance detection, participation in major research projects, and high-quality publications demonstrate both scientific excellence and societal relevance. With his strong research foundation, international experience, and potential for leadership in collaborative and innovative projects, he is well-positioned to continue making impactful contributions to his field and the broader research community.

 

 

Zahra Yahyaoui | Deep Learning | Women Researcher Award

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Dr. Zahra Yahyaoui | Deep Learning | Women Researcher Award

Teacher-Researcher at Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University | Tunisia

Dr. Zahra Yahyaoui is a dedicated researcher and educator with expertise in electronics, microelectronics, renewable energy systems, and artificial intelligence. She has established herself as an active contributor to the advancement of intelligent fault detection and diagnosis methods for photovoltaic and wind energy conversion systems. Her work bridges theory and practice, combining advanced machine learning techniques with embedded hardware implementation, ensuring her research is both academically rigorous and industrially relevant. Alongside her research activities, she has been deeply involved in teaching, supervision, and mentoring, helping to shape the academic and professional development of students in electronics and applied sciences. Her publications in high-impact journals and participation in international conferences highlight her growing recognition in the global research community. With technical versatility, adaptability, and strong teamwork skills, she continues to contribute to sustainable solutions in energy systems while promoting innovation, academic excellence, and interdisciplinary collaboration.

Professional Profiles 

Scopus Profile | ORCID Profile 

Education

Dr. Zahra Yahyaoui pursued her academic path in Tunisia, beginning with a bachelor’s degree in industrial computing with a specialization in embedded systems. She then advanced to a master’s research degree in nanomaterials and embedded electronics, where she specialized in embedded electronics and conducted important research on fault detection and diagnosis in wind energy systems using machine learning. Building on this foundation, she completed her doctoral studies in electronics and microelectronics at the Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University. Her PhD research focused on developing enhanced intelligent data-driven paradigms for fault detection and diagnosis in power systems, with practical applications on embedded architectures. She carried out her doctoral work within the Research Unit of Advanced Materials and Nanotechnologies, furthering her expertise at the intersection of artificial intelligence, renewable energy, and electronic systems. This strong academic background reflects her commitment to innovative, multidisciplinary research.

Professional Experience

Dr. Zahra Yahyaoui has built a solid academic and professional career through her teaching and research activities. She started as a part-time teacher at the Higher Institute of Applied Sciences and Technology of Kasserine, where she gained experience delivering courses and tutorials in electronics, microprocessor and microcontroller architectures, and embedded systems. Her role expanded to contractual teacher at the same institute under Kairouan University, where she was responsible for teaching system-on-chip design, combinational and sequential logic circuits, and analog signal processing, covering both theoretical and practical sessions. In addition to her teaching duties, she has co-supervised master’s theses on advanced topics such as interval-valued machine learning, deep learning for fault detection in renewable systems, and photovoltaic installation design. Through her academic contributions, she has combined teaching excellence with mentoring, ensuring students receive both theoretical knowledge and practical insights. Her professional journey highlights her commitment to education, innovation, and applied research.

Research Interest

Dr. Zahra Yahyaoui’s research interests lie at the intersection of electronics, artificial intelligence, and renewable energy systems. She focuses on developing intelligent data-driven approaches for fault detection and diagnosis, aiming to enhance the reliability and efficiency of power systems such as photovoltaic and wind energy converters. Her work emphasizes the use of advanced machine learning and deep learning techniques, including BiLSTM, GRU, and optimization algorithms, to address uncertainty in renewable energy conversion and monitoring. She is also interested in the implementation of these algorithms on embedded architectures, integrating software with hardware platforms like FPGA, Raspberry Pi, and microcontrollers for real-world applications. Beyond fault diagnosis, she explores forecasting methods for solar irradiance and power output, contributing to the broader field of sustainable energy management. By combining theoretical modeling, algorithm development, and embedded system integration, her research supports innovation in intelligent renewable energy technologies.

Research Skill

Dr. Zahra Yahyaoui has developed a diverse set of research skills that enable her to carry out impactful and interdisciplinary work. She is proficient in programming languages such as MATLAB and Python, which she uses extensively for data analysis, machine learning model development, and algorithm implementation. She is skilled in simulation tools like ISE and Simplorer, supporting her expertise in circuit and system design. Her hardware-related skills include working with Siemens S7-1200, FPGA boards, Raspberry Pi, and Arduino microcontrollers, allowing her to translate theoretical models into practical embedded system solutions. She has strong problem-solving abilities, adaptability, and teamwork skills, which contribute to successful research collaborations and academic projects. Her research methodology combines theoretical analysis with experimental validation, ensuring robust and application-oriented results. With certifications in artificial intelligence and embedded systems, she brings an advanced skillset for developing intelligent monitoring and diagnostic systems, particularly for renewable energy applications.

Publications Top Notes

Title: Fault detection and diagnosis in grid-connected PV systems under irradiance variations
Authors: Hajji, M.; Yahyaoui, Z.; Mansouri, M.; Nounou, H.; Nounou, M.
Year: 2023

Title: One-Class Machine Learning Classifiers-Based Multivariate Feature Extraction for Grid-Connected PV Systems Monitoring under Irradiance Variations
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Bouzrara, K.
Year: 2023

Title: Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Abodayeh, K.; Bouzrara, K.; Nounou, H.
Year: 2022

Title: Kernel PCA based BiLSTM for Fault Detection and Diagnosis for Wind Energy Converter Systems
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Bouzrara, K.; Nounou, H.; Nounou, M.
Year: 2022

Title: Efficient fault detection and diagnosis of wind energy converter systems
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Harkat, M.-F.; Kouadri, A.; Nounou, H.; Nounou, M.
Year: 2020

Conclusion

Dr. Zahra Yahyaoui is a deserving candidate for the Best Researcher Award due to her significant contributions in advancing intelligent data-driven techniques for renewable energy systems, fault detection, and embedded architectures. Her research has produced valuable publications in reputed international journals and conferences, with practical applications that support sustainable energy and technological innovation. Through her teaching, mentorship, and active participation in the academic community, she has demonstrated a strong commitment to knowledge sharing and capacity building. With her proven expertise, dedication, and potential for future leadership, she is well positioned to continue making impactful contributions to both research and society.

Sheilla Ann Pacheco | Machine Learning | Best Researcher Award

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Assist. Prof. Dr. Sheilla Ann Pacheco | Machine Learning | Best Researcher Award

Faculty at North Eastern Mindanao State University | Philippines

Dr. Sheilla Ann Pacheco is an accomplished academic and researcher with extensive experience in computer science, particularly in the fields of machine learning, image processing, and adversarial defense. With over nine years of academic service, she has established herself as a dedicated educator, mentor, and innovator who contributes significantly to both research and teaching. Her work spans practical and theoretical domains, addressing challenges in privacy-preserving AI, biometrics, and medical applications such as breast cancer prediction. Dr. Pacheco is actively involved in presenting her research at national and international conferences, where she has received recognition for her contributions. She is also engaged with professional organizations such as IEEE and ACM, which allows her to remain connected with global advancements in her field. Combining strong technical expertise, leadership in research, and dedication to academic growth, she continues to advance computer science while inspiring students and peers alike.

Professional Profiles 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Sheilla Ann Pacheco has pursued her academic journey with determination and excellence in the field of computer science. She earned her Bachelor of Science in Computer Science from Surigao del Sur State University, laying the foundation for her career in research and academia. She further advanced her studies by completing her Master of Science in Computer Science at the same institution, where she deepened her knowledge of programming, data processing, and research methodologies. To further enhance her expertise, she is currently completing her Doctor of Philosophy in Computer Science at the Technological Institute of the Philippines, focusing on advanced topics such as machine learning, adversarial defense, and computational intelligence. Her academic path highlights her continuous commitment to lifelong learning and growth in her field. Through her education, she has developed the strong theoretical and practical background that now underpins her teaching, supervision, and impactful research contributions.

Professional Experience

Dr. Sheilla Ann Pacheco has built a solid professional career as an academic and researcher in the field of information technology. She currently serves as an Assistant Professor at North Eastern Mindanao State University, where she teaches a variety of courses, supervises research, and contributes to the development of the academic community. Over the years, she has guided students in their research projects, emphasizing innovation and practical applications of computer science in areas such as artificial intelligence and data processing. Her experience is not only limited to classroom teaching but also extends to participation in academic conferences, workshops, and seminars where she presents her work and collaborates with other professionals. Her professional journey demonstrates a balance of academic leadership, technical expertise, and a commitment to advancing knowledge. Through her role, she continues to inspire students and colleagues while contributing to the university’s mission of research and innovation.

Research Interest

Dr. Sheilla Ann Pacheco’s research interests lie in the fields of machine learning, image processing, adversarial defense, and privacy-preserving artificial intelligence. She has a particular focus on developing intelligent solutions that enhance the security and accuracy of biometric recognition systems, as reflected in her work on SARGAN-based face recognition and hidden adversarial attacks on facial biometrics. In addition, she explores federated learning models that aim to protect user privacy while enabling effective AI applications. Her research also extends to healthcare, where she has contributed to studies such as breast cancer prediction using ensemble techniques. These areas highlight her commitment to addressing real-world challenges through innovative technologies. By integrating theoretical models with applied solutions, her research contributes both to the scientific community and to society at large. Her future directions aim to expand collaborations in international research networks and further explore secure, ethical, and intelligent AI applications.

Research Skill

Dr. Sheilla Ann Pacheco possesses a wide range of research skills that enable her to excel in both academic and applied studies. Her expertise includes image processing, machine learning, and adversarial defense, which she has applied in developing innovative solutions for biometric recognition and healthcare prediction models. She is proficient in programming, data analysis, and the use of advanced computational tools, allowing her to conduct rigorous and high-quality research. Her skills in academic writing and presentation have enabled her to publish and present her work at reputable conferences and to effectively communicate her findings to diverse audiences. She is also skilled in research supervision, guiding students through the research process and fostering a culture of inquiry and innovation. Combined with strong organizational and leadership skills, she demonstrates the ability to collaborate with peers, contribute to multidisciplinary projects, and advance knowledge in her field through impactful and practical research outcomes.

Publications Top Notes

Title: Enhanced content-based image retrieval using multivisual features fusion
Authors: SAB Pacheco, M Goyani, ZG Rehman, SF Rehman, T Champaneria, …
Year: 2025
Citation: 1

Title: Robust Face Recognition Under Adversarial Attack Using SARGAN Model and Improved Cross Triple MobileNetV1
Authors: SAB Pacheco, JE Estrada, MM Goyani
Year: 2025

Title: Least Variance based Modeling of Heart Disease Prediction System using Ensemble Technique
Authors: SA Pacheco, JP Bangoy, ZG Rehman, SF Rehman, SV Goyani, …
Year: 2025

Title: Hidden adversarial attack on facial biometrics – a comprehensive survey
Authors: MMG Sheilla Ann Bangoy Pacheco, Jheanel Espiritu Estrada
Year: 2025

Title: A Comprehensive Survey on Federated Learning and Its Applications in Health Care
Authors: SAB Pacheco
Year: 2024

Title: Performance of Students in Computer Programming: An Analysis
Authors: IB Christian, SA Pacheco
Year: 2023

Title: Breast Cancer Prediction using Ensemble Technique
Authors: SAB Pacheco
Year: 2022

Title: Trends and Analysis of Graduate Programs
Authors: SAB Pacheco
Year: 2022

Conclusion

Dr. Sheilla Ann Pacheco is a deserving candidate for the Best Researcher Award due to her impactful contributions in machine learning, image processing, and adversarial defense, which address critical challenges in biometrics, privacy-preserving AI, and healthcare applications. Her research outputs, academic leadership, and active involvement in professional organizations highlight her commitment to advancing both scientific knowledge and the research community. With her strong academic foundation, proven dedication, and potential for expanding her influence through future international collaborations and innovative projects, she is well-positioned to make even greater contributions to research and society in the years ahead.

Imran Riaz | Human Recognition System | Best Researcher Award

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Mr. Imran Riaz | Human Recognition System | Best Researcher Award

PhD Student at University Sains Malaysia | Malaysia

Imran Riaz is an accomplished researcher and academic with expertise in biometrics, image processing, machine learning, medical image analysis, and pattern recognition. He has made significant contributions to the advancement of biometric security systems and medical imaging techniques through impactful publications and international collaborations. His research is supported by industrial projects focusing on cybersecurity applications, such as presentation attack detection and face spoofing detection. In addition to his research, he has demonstrated leadership in academia through teaching undergraduate and postgraduate courses, supervising student projects, and serving as a focal person for artificial intelligence initiatives at his institution. He has actively engaged with the research community as a reviewer for reputable journals and as a member of international professional bodies. His involvement in student leadership, voluntary organizations, and technical workshops reflects a strong balance of professional dedication and community service, establishing him as a well-rounded and forward-looking researcher.

Professional Profiles

Google Scholar | Scopus Profile | ORCID Profile 

Education

Imran Riaz has pursued a comprehensive academic journey in the field of electrical engineering with a focus on advanced computing and biometrics. He is currently completing his PhD in Electrical Engineering at Universiti Sains Malaysia, where he has worked on projects related to multimodal biometric recognition systems and medical image analysis. His doctoral research has been supported by scholarships and has involved both academic and industrial applications, particularly in the areas of cybersecurity and biometric authentication. Prior to his PhD, he earned a Master of Science in Electrical Engineering from Mirpur University of Science and Technology, where he achieved a high academic standing with a strong focus on digital signal processing and image processing techniques. He began his academic path with a Bachelor of Science in Electrical Engineering from the University of Azad Jammu and Kashmir, which provided him with a solid foundation in electrical systems, electronics, and circuit design.

Professional Experience

Imran Riaz has extensive professional experience that combines academia, industry, and research. He is currently serving as a Lecturer at Mirpur University of Science and Technology, where he teaches both undergraduate and postgraduate courses in areas such as machine learning and medical image processing while also acting as the focal person for artificial intelligence initiatives. Earlier, he worked as a Graduate Research Assistant at Universiti Sains Malaysia, contributing to high-level research projects funded by the Ministry of Cyber Security Malaysia, focusing on biometric authentication and face spoofing detection. Prior to that, he held a long-term academic position at Mirpur University of Science and Technology as a Lecturer, supervising student projects, guiding research, and contributing to departmental responsibilities. His industry experience includes serving as Assistant Manager at Transfopower Industries in Lahore, Junior Engineer at the National Physical and Standards Laboratory, and an internship at a hydropower station, reflecting his versatile professional background.

Research Interest

Imran Riaz’s research interests lie in the interdisciplinary domains of biometrics, image processing, medical image analysis, machine learning, and pattern recognition. His work primarily focuses on developing secure, efficient, and accurate biometric authentication systems, exploring new modalities such as dorsal finger creases and finger knuckle print recognition. He has also contributed to advancements in medical imaging through deep learning-based diagnostic systems, particularly in cancer detection and histopathology image analysis. His studies aim to address real-world challenges, including overcoming fingerprint recognition failures caused by physiological factors and enhancing face recognition robustness against spoofing attacks. He also shows a keen interest in applying artificial intelligence and deep learning techniques across broader fields such as agriculture, energy optimization, and healthcare applications. His approach integrates theoretical advancements with practical implementation, bridging the gap between academic research and industry needs, while also contributing to emerging global challenges in smart technologies and intelligent systems.

Research Skill

Imran Riaz possesses strong research skills that span programming, data analysis, and advanced modeling techniques relevant to biometrics and machine learning. He is proficient in Python and MATLAB, with expertise in implementing machine and deep learning algorithms for classification, prediction, and pattern recognition tasks. His skills also extend to using statistical tools such as SPSS, along with simulation and circuit design software like SPICE and EWB, enabling him to integrate computational methods with engineering applications. Additionally, he has experience with tools such as Blender, Pepakura, and Anima8or, reflecting versatility in visualization and modeling. His technical report writing proficiency using LaTeX has supported the development of high-quality publications. These skills have been applied in multiple funded research projects, including biometric spoofing detection and medical image analysis. His ability to combine theoretical knowledge with practical implementation highlights his capacity to design, test, and validate innovative systems for real-world applications.

Publications Top Notes

Title: Automatic grading of palsy using asymmetrical facial features: a study complemented by new solutions
Authors: M Sajid, T Shafique, MJA Baig, I Riaz, S Amin, S Manzoor
Year: 2018
Citation: 55

Title: Data augmentation‐assisted makeup‐invariant face recognition
Authors: M Sajid, N Ali, SH Dar, N Iqbal Ratyal, AR Butt, B Zafar, T Shafique, …
Year: 2018
Citation: 53

Title: SA-GAN: stain acclimation generative adversarial network for histopathology image analysis
Authors: T Kausar, A Kausar, MA Ashraf, MF Siddique, M Wang, M Sajid, …
Year: 2021
Citation: 24

Title: Facial asymmetry-based anthropometric differences between gender and ethnicity
Authors: M Sajid, T Shafique, I Riaz, M Imran, M Jabbar Aziz Baig, S Baig, …
Year: 2018
Citation: 23

Title: Deep learning in age-invariant face recognition: a comparative study
Authors: M Sajid, N Ali, NI Ratyal, M Usman, FM Butt, I Riaz, U Musaddiq, …
Year: 2022
Citation: 17

Title: Demographic-assisted age-invariant face recognition and retrieval
Authors: M Sajid, T Shafique, S Manzoor, F Iqbal, H Talal, U Samad Qureshi, I Riaz
Year: 2018
Citation: 16

Title: Circular shift combination local binary pattern (CSC-LBP) method for dorsal finger crease classification
Authors: I Riaz, AN Ali, H Ibrahim
Year: 2023
Citation: 9

Title: Convolution neural network based approach for breast cancer type classification
Authors: T Kausar, MA Ashraf, A Kausar, I Riaz
Year: 2021
Citation: 8

Title: Loss of fingerprint features and recognition failure due to physiological factors-a literature survey
Authors: I Riaz, AN Ali, H Ibrahim
Year: 2024
Citation: 5

Title: Biometric classification system for dorsal finger creases utilizing multi-block circular shift combination local binary pattern
Authors: I Riaz, AN Ali, H Ibrahim, IA Huqqani
Year: 2024
Citation: 1

Title: Enhanced Parameter Estimation of Solar Photovoltaic Models Using QLESCA Algorithm
Authors: QS Hamad, SAM Saleh, SA Suandi, H Samma, YS Hamad, I Riaz
Year: 2024
Citation: 1

Title: A Finger Knuckle Print Classification System Using SVM for Different LBP Variants
Authors: I Riaz, AN Ali, IA Huqqani
Year: 2024
Citation: 1

Title: Enhancing the Dorsal Side of Fingers Using An Image Enhancement Technique with FPGA Output Comparison
Authors: TS Han, I Riaz, AN Ali
Year: 2024
Citation: 1

Title: Advanced technologies for smart fertilizer management in agriculture: A Review
Authors: JJ Liu, H Wu, I Riaz
Year: 2025

Title: Multimodal Biometric Recognition System Based on Feature-Level Fusion of Dorsal Finger Crease and Finger Knuckle Print
Authors: I Riaz, AN Ali, H Ibrahim, IA Huqqani
Year: 2025

Title: A novel sub-windowing local binary pattern approach for dorsal finger creases based biometric classification system
Authors: I Riaz, AN Ali, H Ibrahim
Year: 2024

Title: Dorsal Finger crease classification system using local binary pattern and its variants-A new finger biometric modality
Authors: I Riaz, AN Ali, H Ibrahim, IA Huqqani
Year: 2024

Title: Evaluation of Three Variants of LBP for Finger Creases Classification
Authors: NAA Salihin, I Riaz, AN Ali
Year: 2024

Title: Training Needs Assessment of Rice Growers in Rice Zone of the Punjab, Pakistan
Authors: I Riaz
Year: 2021

Conclusion

Imran Riaz is a deserving candidate for the Best Researcher Award due to his significant contributions in biometrics, medical image analysis, and machine learning, supported by a strong academic background and impactful research collaborations. His extensive publications in reputed journals and involvement in industrial projects highlight both academic excellence and real-world relevance. Alongside his research, he has contributed to society through teaching, mentoring, and volunteer services, demonstrating a balance of professional and community engagement. With his dedication to advancing biometric security and medical imaging, and his potential to lead future international collaborations and high-impact projects, he stands out as a researcher with both current achievements and promising leadership potential.

Saulius Baskutis | Manufacturing Applications | Excellence in Computer Vision Award

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Prof. Dr. Saulius Baskutis | Manufacturing Applications | Excellence in Computer Vision Award

Professor at Kaunas University of Technology | Lithuania

Prof. Dr. Saulius Baskutis is a distinguished academic and researcher whose career spans decades of teaching, research, and industrial engagement in the field of engineering sciences. He has contributed significantly to the advancement of materials testing, welding processes, coatings, renewable energy, logistics, and device diagnostics, while also actively participating in collaborative international projects. His professional journey reflects a balance between academic excellence and industrial practice, providing him with the ability to develop innovative, practical solutions for technological challenges. With numerous publications in reputable, indexed journals and presentations at international conferences, he has built a strong scholarly reputation. His leadership in EU-funded projects and membership in professional associations highlights his influence in both academic and professional communities. Recognized for his commitment to advancing science and technology, he continues to inspire students, researchers, and industry experts, while shaping the future of sustainable engineering practices through impactful research and collaborations.

Professional Profiles 

Scopus Profile | ORCID Profile 

Education

Prof. Dr. Saulius Baskutis began his academic journey at Kaunas Polytechnical Institute, where he earned an engineering qualification and later pursued doctoral studies in technical sciences, which shaped the foundation of his research career. His academic training combined deep theoretical knowledge with applied aspects of mechanical and material sciences, enabling him to explore innovative solutions in manufacturing and energy systems. During his postgraduate years, he developed expertise in welding, coatings, and materials testing, areas that would remain central to his research. He later expanded his knowledge through continuous professional development and specialized training programs across Europe, focusing on Industry 4.0, renewable energy, mechatronics, and advanced manufacturing technologies. This commitment to lifelong learning has strengthened his academic portfolio and ensured that his teaching and research remain aligned with evolving industrial needs. His education reflects a continuous pursuit of excellence, integrating traditional engineering knowledge with modern approaches to meet current and future challenges.

Professional Experience

Prof. Dr. Saulius Baskutis has built an impressive professional career combining academia and industry, demonstrating strong leadership and applied expertise in engineering. He has served at Kaunas University of Technology in various roles, progressing from assistant and lecturer to associate professor and professor, contributing extensively to teaching and curriculum development in manufacturing and mechanical engineering. His professional journey is enriched by industrial experience with organizations in Lithuania, Denmark, and Finland, where he worked as an engineer, manager, and project leader. These positions enabled him to apply academic insights to solve real-world engineering problems while fostering stronger connections between research and practice. He has also held leadership roles in European and national research projects, demonstrating the ability to guide interdisciplinary teams and achieve impactful outcomes. His active participation in Erasmus programs and scientific exchanges further broadened his expertise, while his engagement in professional associations has extended his influence within the engineering community.

Research Interest

Prof. Dr. Saulius Baskutis has a diverse range of research interests that reflect his commitment to addressing technological and societal challenges through engineering innovations. His primary focus lies in materials testing, welding processes, and coatings, where he investigates methods to improve durability, performance, and cost-effectiveness in industrial applications. He is also actively engaged in exploring renewable energy sources, with particular attention to sustainable energy production, district heating systems, and the integration of green technologies into industrial processes. His work in logistics and device diagnostics complements these efforts by improving efficiency, safety, and reliability in manufacturing and energy systems. He is keenly interested in the intersection of Industry 4.0 and smart manufacturing, contributing to projects that integrate robotics, automation, and advanced digital technologies. These research interests demonstrate his vision of combining scientific discovery with practical applications to promote sustainable development and enhance global competitiveness in engineering and technological industries.

Research Skill

Prof. Dr. Saulius Baskutis possesses a comprehensive set of research skills that reflect his long-standing academic and industrial experience in engineering. He is highly skilled in experimental methods, particularly in the testing of materials, welding techniques, and coating technologies, where precision and innovation are essential. His ability to design, conduct, and analyze experiments has led to impactful findings published in internationally recognized journals. He demonstrates strong competencies in project management, having successfully led and contributed to national and international research initiatives, including EU-funded programs. His skills extend to collaborative research, working with multidisciplinary teams across Europe to address complex technological challenges. Additionally, he is adept in applying digital tools such as CAD, CAM, and CNC systems to align with Industry 4.0 practices. His ability to bridge theoretical knowledge with applied solutions highlights his strength as both a researcher and innovator, ensuring his contributions remain relevant and forward-looking.

Publications Top Notes

Title: Numerical Method for Internal Structure and Surface Evaluation in Coatings
Authors: Tomas Kačinskas, Saulius Baskutis
Year: 2025

Title: Modeling of Vibrational-Centrifugal Strengthening for Functional Surfaces of Machine Parts
Authors: Vadym Stupnytskyy, Yaroslav Kusyi, Egidijus Dragašius, Saulius Baskutis, Rafal Chatys
Year: 2024

Title: Investigation of the Properties of Anti-Friction Coatings Deposited with Different Casting Methods
Authors: Tomas Kačinskas, Saulius Baskutis, Jolanta Baskutienė, Lina Kavaliauskienė
Year: 2024

Title: Analytical Model of Tapered Thread Made by Turning from Different Machinability Workpieces
Authors: Oleh Onysko, Volodymyr Kopei, Cristian Barz, Yaroslav Kusyi, Saulius Baskutis, Michal Bembenek, Predrag Dašić, Vitalii Panchuk
Year: 2024

Title: Control of the Parameters of the Surface Layer of Steel Parts During Their Processing Applying the Material Homogeneity Criterion
Authors: Kusyi Yaroslav, Stupnytskyy Vadym, Kostiuk Olha, Oleh Onysko, Egidijus Dragašius, Saulius Baskutis, Rafał Chatys
Year: 2024

Title: Simulation and Analytical Studies of Chip Formation Processes in the Cutting Zone of Titanium Alloys
Authors: Vadym Stupnytskyy, Xianning She, Egidijus Dragašius, Saulius Baskutis, Oleh Prodanchuk
Year: 2023

Title: Pool Boiling of Ethanol on Copper Surfaces with Rectangular Microchannels
Authors: Robert Kaniowski, Robert Pastuszko, Egidijus Dragašius, Saulius Baskutis
Year: 2023

Title: Influence of Additives on the Mechanical Characteristics of Hardox 450 Steel Welds
Authors: Saulius Baskutis, Jolanta Baskutiene, Egidijus Dragašius, Lina Kavaliauskiene, Neringa Keršiene, Yaroslav Kusyi, Vadym Stupnytskyy
Year: 2023

Title: Modeling and Simulation of Machined Surface Layer Microgeometry Parameters
Authors: Vadym Stupnytskyy, Egidijus Dragašius, Saulius Baskutis, She Xianning
Year: 2022

Title: Agent-Based Modelling Approach for Autonomous Vehicle Influence on Countries’ Welfare
Authors: Saulius Baskutis, Valentas Gružauskas, Peter Leibl, Linas Obcarskas
Year: 2022

Title: Effect of Chemical Composition of Clay on Physical-Mechanical Properties of Clay Paving Blocks
Authors: Rolandas Avizovas, Saulius Baskutis, Valentinas Navickas, László Tamándl
Year: 2022

Title: Perspectives and Problems of Using Renewable Energy Sources and Implementation of Local “Green” Initiatives: A Regional Assessment
Authors: Saulius Baskutis, Jolanta Baskutiene, Valentinas Navickas, Yuriy Bilan, Wojciech Cieśliński
Year: 2021

Title: Mechanical Properties and Microstructure of Aluminium Alloy AW6082-T6 Joints Welded by Double-Sided MIG Process Before and After Aging
Authors: Saulius Baskutis
Year: 2019

Title: Monitoring of Welding Process Parameters in Gas Tungsten Arc Welding of Carbon Steel Weldments
Authors: Saulius Baskutis
Year: 2019

Title: Effect of Weld Parameters on Mechanical Properties and Tensile Behavior of Tungsten Inert Gas Welded AW6082-T6 Aluminium Alloy
Authors: Saulius Baskutis
Year: 2019

Title: Research on Mechanical Properties of TIG Welded Aluminum Alloy
Authors: Bendikiene R., Baskutis S., Baskutiene J., Ciuplys A.
Year: 2018

Title: Comparative Study of TIG Welded Commercially Pure Titanium
Authors: Saulius Baskutis
Year: 2018

Title: Minimizing the Trade-Off Between Sustainability and Cost Effective Performance by Using Autonomous Vehicles
Authors: Saulius Baskutis
Year: 2018

Title: The Analysis of Dissimilar Metal Weld Joints
Authors: Saulius Baskutis
Year: 2018

Title: Experimental Study of Welded Joints of Aluminium Alloy AW6082
Authors: Baskutis S., Baskutiene J., Bernotaitis E.
Year: 2017

Title: The Vibratory Alignment of the Parts in Robotic Assembly
Authors: Saulius Baskutis
Year: 2017

Title: Influence of Welding Modes on Weldability of Structural Steel Lap Joints in Laser Welding
Authors: Saulius Baskutis
Year: 2017

Title: Warehouses Consolidation in the Logistic Clusters: Food Industry’s Case
Authors: Saulius Baskutis
Year: 2016

Title: Nano and Microhardness Testing of Heterogeneous Structures
Authors: Saulius Baskutis
Year: 2016

Title: The Temperature Control Impact to the Food Supply Chain
Authors: Baskutis S., Navickas V., Gružauskas V., Olencevičiute D.
Year: 2015

Conclusion

Prof. Dr. Saulius Baskutis is highly deserving of the Best Researcher Award for his longstanding dedication to advancing engineering science, particularly in the fields of coatings, welding technologies, renewable energy, and sustainable manufacturing. His significant contributions through impactful publications, leadership in international research projects, and strong collaboration between academia and industry highlight his role in driving innovation and practical solutions for society. With his expertise, professional recognition, and commitment to mentoring future generations, he demonstrates both past excellence and strong potential for continued research leadership and global impact in the years ahead.

Puja Gupta | Computer Vision | Excellence in Research

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Dr. Puja Gupta | Computer Vision | Excellence in Research

Asst Professor at Shri G.S. Institute of Technology & Science | India

Dr. Puja Gupta is a dedicated researcher and academic with expertise in artificial intelligence, machine learning, IoT, and smart computing technologies. She has contributed significantly to the field through her high-quality publications in reputed journals, patents, and innovative product development. Her work has addressed real-world challenges in healthcare, security, and sustainable technologies, bridging the gap between research and practical applications. With a strong academic foundation, she has successfully guided students in research and projects, fostering innovation and academic growth. She has been actively involved in international collaborations, research projects, and academic leadership roles, contributing to the advancement of her field. She is also a committed member of professional organizations, demonstrating her engagement in the broader research community. Her impactful contributions, leadership potential, and dedication to continuous professional development make her a valuable asset to both academia and society.

Professional Profile 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Puja Gupta holds a strong academic background in computer science and engineering, culminating in a doctoral degree specializing in artificial intelligence and smart systems. Her Ph.D. research focused on the integration of machine learning techniques and IoT frameworks to design intelligent solutions that address complex societal problems. Prior to her doctoral studies, she earned her master’s and bachelor’s degrees in computer science, gaining a solid foundation in algorithms, data structures, and system design. Throughout her academic journey, she demonstrated exceptional commitment to learning, consistently achieving top ranks and recognition for her research contributions. Her advanced education has equipped her with in-depth knowledge of computational intelligence, optimization techniques, and applied research methodologies, enabling her to contribute effectively to both theoretical advancements and practical applications in the field. Her academic background continues to support her innovative research and teaching excellence in the areas of AI, IoT, and emerging technologies.

Professional Experience

Dr. Puja Gupta has extensive professional experience in both academic and research domains, with a focus on artificial intelligence, IoT, and smart computing solutions. She has worked as a faculty member at prestigious institutions, where she has taught and mentored students at undergraduate and postgraduate levels, guiding them in research projects and fostering innovation. Alongside teaching, she has been actively involved in funded research projects, many of which involved international collaborations and multidisciplinary teams. She has successfully published her findings in reputed journals and conferences indexed in IEEE and Scopus, and her work has also resulted in patents and prototypes with practical applications. Beyond academia, she has contributed to the research community by serving as a reviewer, participating in editorial activities, and organizing academic events. Her leadership roles in academic programs and community-driven initiatives further highlight her commitment to advancing knowledge and supporting the development of future researchers.

Research Interest

Dr. Puja Gupta’s research interests revolve around artificial intelligence, machine learning, IoT, big data analytics, and smart system design. She is particularly focused on developing intelligent solutions that address pressing societal challenges in areas such as healthcare, security, and sustainability. Her work often integrates computational intelligence with real-world applications, such as predictive healthcare models, smart monitoring systems, and secure communication frameworks for IoT devices. She is also keen on advancing research in explainable AI and optimization algorithms to ensure reliability and transparency in machine learning systems. Another area of interest is the development of resource-efficient AI models for deployment in edge and cloud environments. Her multidisciplinary approach allows her to collaborate across domains, leveraging data-driven techniques to innovate practical solutions. By combining theoretical knowledge with applied research, she aims to contribute to technological advancements that enhance the quality of life and create sustainable, impactful outcomes for society.

Award and Honor

Dr. Puja Gupta has been recognized with numerous awards and honors that highlight her academic excellence, research contributions, and leadership in the field of computer science and engineering. Her achievements include recognition for publishing impactful research in reputed journals, presenting at leading international conferences, and securing patents that demonstrate the practical value of her work. She has also been honored for her contributions to student mentoring and academic program development, reflecting her dedication to nurturing young talent. Several of her awards acknowledge her innovative approaches in AI and IoT research, particularly for developing solutions with direct societal impact. In addition, she has received appreciation for her involvement in community-driven initiatives and leadership in professional organizations. These honors not only recognize her past accomplishments but also serve as a testament to her commitment, perseverance, and ability to inspire others in the academic and research communities.

Research Skill

Dr. Puja Gupta possesses advanced research skills in artificial intelligence, machine learning, IoT systems, and computational modeling, enabling her to conduct impactful and interdisciplinary research. She is proficient in applying data analysis techniques, optimization algorithms, and predictive modeling to design intelligent solutions for real-world applications. Her expertise includes working with various programming languages, simulation tools, and research frameworks that support scalable and innovative problem-solving. She has developed strong skills in experimental design, result validation, and research dissemination through high-quality publications and conference presentations. Beyond technical expertise, she excels in collaborative research, often working with international teams and multidisciplinary groups to drive innovation. She is also skilled in project management, proposal writing, and securing research funding, which have been instrumental in the successful execution of her projects. Her research skills, combined with her commitment to continuous learning, position her as a versatile and resourceful academic and researcher in her field.

Publications Top Notes

Title: Impact of knowledge management practices on innovative capacity: A study of telecommunication sector
Authors: J Jyoti, P Gupta, S Kotwal
Year: 2011
Citation: 56

Title: A Novel Algorithm for Mask Detection and Recognizing Actions of Human
Authors: P Gupta, V Sharma, S Varma
Year: 2022
Citation: 48

Title: Transcriptional mechanisms underlying sensitization of peripheral sensory neurons by granulocyte-/granulocyte-macrophage colony stimulating factors
Authors: KK Bali, V Venkataramani, VP Satagopam, P Gupta, R Schneider, …
Year: 2013
Citation: 42

Title: Minimally invasive plate osteosynthesis (MIPO) for proximal and distal fractures of the tibia: a biological approach
Authors: P Gupta, A Tiwari, A Thora, JK Gandhi, VP Jog
Year: 2016
Citation: 41

Title: SUMOylation of enzymes and ion channels in sensory neurons protects against metabolic dysfunction, neuropathy, and sensory loss in diabetes
Authors: N Agarwal, FJ Taberner, DR Rojas, M Moroni, D Omberbasic, C Njoo, …
Year: 2020
Citation: 39

Title: An introduction of soft computing approach over hard computing
Authors: P Gupta, N Kulkarni
Year: 2013
Citation: 31

Title: People detection and counting using YOLOv3 and SSD models
Authors: P Gupta, V Sharma, S Varma
Year: 2021
Citation: 30

Title: Challenges in the adaptation of IoT technology
Authors: Neha, P Gupta, MA Alam
Year: 2021
Citation: 20

Title: Role of fine needle aspiration cytology in preoperative diagnosis of ameloblastoma
Authors: S Bisht, SA Kotwal, P Gupta, R Dawar
Year: 2009
Citation: 13

Title: Let the Blind See: An AIIoT based device for real-time object recognition with the voice conversion
Authors: P Gupta, M Shukla, N Arya, U Singh, K Mishra
Year: 2022
Citation: 9

Title: The impact of artificial intelligence on renewable energy systems
Authors: P Gupta, S Kumar, YB Singh, P Singh, SK Sharma, NK Rathore
Year: 2022
Citation: 8

Title: Simultaneous feature selection and clustering of micro-array and RNA-sequence gene expression data using multiobjective optimization
Authors: AK Alok, P Gupta, S Saha, V Sharma
Year: 2020
Citation: 8

Title: Activity detection and counting people using mask-RCNN with bidirectional ConvLSTM
Authors: P Gupta, U Singh, M Shukla
Year: 2022
Citation: 7

Title: Study of cloud providers (azure, amazon, and oracle) according to service availability and price
Authors: A Rajput, P Gupta, P Ghodeshwar, S Varma, KK Sharma, U Singh
Year: 2023
Citation: 6

Title: Machine learning approaches for IoT-data classification
Authors: O Farooq, P Gupta
Year: 2020
Citation: 5

Title: Evaluation of AI system’s voice recognition performance in social conversation
Authors: SK Barnwal, P Gupta
Year: 2022
Citation: 4

Title: Analysis of CNN Model with Traditional Approach and Cloud AI based Approach
Authors: U Kushwaha, P Gupta, S Airen, M Kuliha
Year: 2022
Citation: 4

Title: Analysis of crowd features based on deep learning
Authors: P Gupta, V Sharma, S Varma
Year: 2022
Citation: 4

Title: Acknowledgment of patient in sense behaviors using bidirectional ConvLSTM
Authors: U Singh, P Gupta, M Shukla, V Sharma, S Varma, SK Sharma
Year: 2023
Citation: 3

Title: Study on the NB-IoT based smart medical system
Authors: P Gupta, AK Pandey
Year: 2023
Citation: 3

Conclusion

Dr. Puja Gupta is highly deserving of the Best Researcher Award for her significant contributions to advancing research in artificial intelligence, IoT, and smart technologies, as well as her role in mentoring students and fostering innovation. Her impactful work, including patents, high-quality publications, and practical product development, has addressed societal challenges in healthcare, security, and sustainability. With her strong academic background, leadership in academic and community initiatives, and commitment to continuous growth, she holds great potential to further excel in future research, expand global collaborations, and take on greater leadership roles in the academic and research community.

Jie Han | Image Deblurring | Best Researcher Award

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Mr. Jie Han | Image Deblurring | Best Researcher Award

Lecturer at Nanjing University of Information Science and Technology | China

Mr. Jie Han is an academic researcher specializing in mathematical modeling, surveying engineering, and remote sensing image processing. His expertise lies in developing advanced algorithms for errors-in-variables models, optical image restoration, and ill-posed inversion problems. He has consistently contributed to high-impact publications in leading journals such as IEEE Transactions on Geoscience and Remote Sensing, Remote Sensing, and Information Sciences, which reflect his commitment to advancing knowledge and practice in his field. Dr. Han has participated in prominent international conferences, sharing his research and collaborating with peers from diverse backgrounds. As a lecturer at Nanjing University of Information Science and Technology, he combines teaching with active research, mentoring young scholars, and fostering innovation. His scholarly contributions have not only enhanced theoretical approaches but also supported practical applications in geoinformatics and environmental monitoring. With an expanding academic portfolio, Dr. Han continues to establish himself as a promising researcher with international impact.

Professional Profile 

ORCID Profile 

Education

Mr. Jie Han has pursued an extensive academic journey in the fields of surveying engineering, geoinformatics, and remote sensing. He began his undergraduate studies in Surveying and Mapping Engineering at Hefei University of Technology, where he gained a solid foundation in geospatial sciences, cartography, and mathematical modeling. He later continued his academic career by pursuing doctoral studies at Tongji University in Shanghai, one of China’s leading institutions in engineering and geoscience research. There, he specialized in surveying and geoinformatics, focusing on errors-in-variables models and advanced statistical approaches for solving ill-posed problems. His doctoral research contributed significantly to advancements in precision estimation, image restoration, and hyperspectral data analysis. Following his doctoral degree, he transitioned into academia as a lecturer at the School of Mathematics and Statistics at Nanjing University of Information Science and Technology. His educational background combines rigorous training in engineering and mathematics, which serves as the basis for his diverse research contributions.

Professional Experience

Mr. Jie Han has developed his professional career through a balanced combination of teaching, research, and scholarly collaboration. He currently serves as a lecturer at the School of Mathematics and Statistics at Nanjing University of Information Science and Technology, where he contributes to both undergraduate and graduate-level education while supervising research activities. In his role, he integrates theoretical knowledge with practical research applications, fostering academic growth among students and peers. Prior to this appointment, his professional experience was shaped by his doctoral training at Tongji University, where he engaged in several collaborative projects in surveying, image processing, and statistical modeling. He has also actively participated in international conferences such as IEEE IGARSS and ISPRS, presenting his research and establishing academic connections worldwide. His professional journey reflects a consistent dedication to advancing geoinformatics and remote sensing applications while contributing to the global academic community through research dissemination and academic service.

Research Interest

Mr. Jie Han’s research interests lie at the intersection of mathematical modeling, geoinformatics, and remote sensing image analysis. A central area of his work focuses on errors-in-variables models, where he explores improved estimation methods for solving statistical and mathematical challenges in measurement and modeling. He is also deeply engaged in the development of algorithms for optical image restoration, including image denoising, deblurring, and dehazing, which have direct applications in enhancing the quality and usability of remote sensing data. His research extends to ill-posed inversion problems, where he investigates solutions for complex data reconstruction in radar and hyperspectral imaging. Beyond theory, Dr. Han is interested in practical applications such as environmental monitoring, image-based mapping, and atmospheric studies. By combining statistical analysis with computational techniques, his research bridges theoretical foundations with real-world challenges, contributing to advancements in geospatial sciences, remote sensing technologies, and interdisciplinary applications across scientific and engineering domains.

Award and Honor

Mr. Jie Han has been recognized for his academic achievements through his publications in highly reputed journals and his participation in international conferences that highlight the significance of his contributions. The acceptance of his work in leading journals such as IEEE Transactions on Geoscience and Remote Sensing, Remote Sensing, and Information Sciences serves as a mark of distinction, as these platforms are highly competitive and widely respected in the scientific world. Additionally, his role as first and corresponding author in multiple publications showcases his leadership and initiative in research. His invited participation in academic gatherings such as IEEE IGARSS and ISPRS further highlights his growing visibility in the global scientific arena. With continued engagement and expansion of his research influence, future recognitions and awards are strongly anticipated.

Research Skill

Mr. Jie Han possesses a wide range of research skills that combine mathematical expertise with computational and applied techniques. He is proficient in designing and applying errors-in-variables models for statistical estimation problems, offering innovative methods for handling uncertainty in measurements. His skills in optical image processing include advanced methods for denoising, deblurring, and dehazing, which significantly improve the quality of remote sensing data. He is also experienced in addressing ill-posed inversion problems using sparse regularization and tensor-based techniques, which are essential for solving complex imaging challenges. His technical competence extends to hyperspectral image analysis, radar imaging, and environmental data modeling. In addition, he has strong analytical and programming skills, enabling the development of algorithms and models that bridge theory with real-world applications. His experience in writing and publishing high-quality research papers also demonstrates his ability to communicate complex concepts effectively, making his skills both versatile and impactful in the scientific community.

Publications Top Notes

Title: Novel Regularization Method of Adaptively Balanced L1 and L2 Norms with Bias Removal
Authors: Jie Han; Zhichao Zhang; Shouzhu Zheng; Qing Fu; Wenping Song; Haiyong Ding; Minghua Wang; Weiguo Huang
Year: 2025

Title: Comparison of Posterior Precision Estimation Methods of Weighted Total Least-Squares Solution for Errors-in-Variables Model
Authors: Jie Han; Songlin Zhang; Shimeng Dong; Qingyun Yan
Year: 2024

Title: A Nonblind Deconvolution Method by Bias Correction for Inaccurate Blur Kernel Estimation in Image Deblurring
Authors: Jie Han; Songlin Zhang; Zhen Ye
Year: 2023

Title: Double-Factor Tensor Cascaded-Rank Decomposition for Hyperspectral Image Denoising
Authors: Jie Han; Chuang Pan; Haiyong Ding; Zhichao Zhang
Year: 2023

Title: Automatic Registration of Very Low Overlapping Array InSAR Point Clouds in Urban Scenes
Authors: Xiaohua Tong; Xin Zhang; Shijie Liu; Zhen Ye; Yongjiu Feng; Huan Xie; Longyong Chen; Fubo Zhang; Jie Han; Yanmin Jin et al.
Year: 2022

Title: Bias Analysis and Correction for Ill-Posed Inversion Problem with Sparsity Regularization Based on L1 Norm for Azimuth Super-Resolution of Radar Forward-Looking Imaging
Authors: Jie Han; Songlin Zhang; Shouzhu Zheng; Minghua Wang; Haiyong Ding; Qingyun Yan
Year: 2022

Title: Local Patchwise Minimal and Maximal Values Prior for Single Optical Remote Sensing Image Dehazing
Authors: Jie Han
Year: 2022

Title: Spatiotemporal PM2.5 Estimations in China from 2015 to 2020 Using an Improved Gradient Boosting Decision Tree
Authors: Weihuan He; Huan Meng; Jie Han; Gaohui Zhou; Hui Zheng; Songlin Zhang
Year: 2022

Title: Closure to “New First-Order Approximate Precision Estimation Method for Parameters in an Errors-in-Variables Model”
Authors: Jie Han; Songlin Zhang; Jingchang Li
Year: 2022

Title: Indoor Map Boundary Correction Based on Normalized Total Least Squares of Condition Equation
Authors: Jie Han
Year: 2021

Title: Quality Evaluation of Linear Inequality Constrained Estimation by Monte Carlo Sampling in Parameter Space
Authors: Songlin Zhang; Jingchang Li; Kun Zhang; Jie Han
Year: 2021

Title: New First-Order Approximate Precision Estimation Method for Parameters in an Errors-in-Variables Model
Authors: Jie Han; Songlin Zhang; Jingchang Li
Year: 2021

Title: A General Partial Errors-in-Variables Model and a Corresponding Weighted Total Least-Squares Algorithm
Authors: Jie Han; Songlin Zhang; Yali Li; Xin Zhang
Year: 2020

Conclusion

Mr. Jie Han is a deserving candidate for the Best Researcher Award as he has made significant contributions in mathematical modeling, remote sensing, and optical image restoration through innovative methods and high-quality publications in reputed journals and conferences. His research has advanced both theoretical foundations and practical applications, contributing to progress in geoinformatics and environmental monitoring. With his strong academic background, consistent research output, and growing recognition in the scientific community, he shows great potential for future leadership, expanded collaborations, and impactful contributions to global scientific and societal challenges.

Mustapha Omenesa Idris | Energy Application | Excellence in Research

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Dr. Mustapha Omenesa Idris | Energy Application | Excellence in Research

Researcher/Lecturer at Prince Abubakar Audu University, Nigeria

Dr. Mustapha Omenesa Idris is an accomplished researcher in materials chemistry and bio-electrochemical systems, specializing in microbial fuel cells, nanomaterials, and sustainable technologies. He has a strong academic record and a proven ability to integrate innovative solutions into practical applications, particularly in energy generation, waste valorization, and environmental remediation. His work demonstrates creativity, resilience, and the capacity to work under challenging conditions while mentoring students and collaborating with international research teams. Dr. Idris has an extensive publication record in high-impact journals, reflecting his dedication to advancing scientific knowledge. He is skilled in managing complex research projects, supervising students, and contributing to scientific communities through peer review, workshops, and seminars. His leadership, professionalism, and commitment to research excellence make him a highly valuable contributor to both academia and society, with a promising trajectory for future innovation and scientific leadership.

Professional Profile 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Mustapha Omenesa Idris earned his Bachelor of Science in Industrial Chemistry from Bayero University Kano with first-class honors, followed by a Master of Science in Industrial Chemistry from the University of Benin with distinction. He completed his Ph.D. in Material Chemistry and Advanced Materials at Universiti Sains Malaysia, where he focused on the design and application of graphene derivative anode electrodes for microbial fuel cells. His academic training provided a strong foundation in chemistry, nanomaterials, electrochemistry, and environmental remediation, equipping him with the expertise to address complex scientific challenges. Throughout his education, he demonstrated excellence in research, laboratory management, and analytical techniques, as well as the ability to conceptualize and implement innovative solutions. His training included both theoretical knowledge and practical application, preparing him for impactful contributions in energy generation, bio-waste conversion, and sustainable technologies. This strong educational background has been instrumental in shaping his research focus and professional achievements.

Professional Experience

Dr. Mustapha Omenesa Idris has extensive professional experience in academia and research, currently serving as a lecturer at Kogi State University. Over his career, he has been responsible for undergraduate and postgraduate teaching, laboratory setup and management, and supervision of numerous research projects. He has coordinated and guided students through final-year projects, postgraduate diplomas, and co-supervised MSc and Ph.D. research, particularly in the field of microbial fuel cells and advanced electrode materials. He has actively participated in workshops, conferences, and seminars, contributing to knowledge dissemination and professional development. His work has involved both national and international collaborations, demonstrating his ability to manage research projects across diverse teams. In addition to teaching and research, he has served as a peer reviewer for reputable journals, ensuring high-quality scientific contributions. His professional experience reflects a balance of academic leadership, research excellence, and mentorship.

Research Interest

Dr. Mustapha Omenesa Idris’s research interests focus on sustainable energy, environmental remediation, and advanced materials. He specializes in microbial fuel cells, exploring bio-electrochemical approaches for energy generation and wastewater treatment. His work involves the design, synthesis, and characterization of nanomaterials and graphene derivatives for efficient electrode applications. He is also interested in converting biowaste into value-added products, investigating electrochemical properties, and optimizing bioelectricity production. Additional research areas include electro-microbiology, phylogenetic analysis, photocatalytic degradation of pollutants, and the development of eco-sustainable technologies. Dr. Idris integrates theoretical knowledge with practical applications to address energy and environmental challenges, aiming for scalable and commercially viable solutions. His research contributes to both scientific understanding and societal impact, promoting sustainability and innovative solutions in materials chemistry and bio-electrochemical systems.

Award and Honor

Dr. Mustapha Omenesa Idris has received recognition for his academic excellence and research contributions. He was awarded the Sanggar Sanjung Award for outstanding student achievement at Universiti Sains Malaysia and received a Graduate on Time certificate for the timely completion of his Ph.D. His work has been acknowledged through publications in high-impact journals and invitations to present at conferences and workshops. He has also been recognized for his contributions to peer review, mentoring, and academic leadership. These awards and honors highlight his commitment to research quality, professional development, and knowledge dissemination. They reflect his dedication to advancing sustainable technologies and innovative materials research. Dr. Idris’s achievements demonstrate both academic excellence and practical impact, positioning him as a prominent figure in his field.

Research Skill

Dr. Mustapha Omenesa Idris possesses a wide range of research skills in materials chemistry, bio-electrochemical systems, and environmental technologies. He is proficient in the design, synthesis, and characterization of advanced electrode materials and nanomaterials, including graphene derivatives. He has expertise in microbial fuel cell setup, operation, and electrochemical measurements, as well as biowaste conversion and pollutant remediation. He is skilled in data analysis using software tools, literature review, scientific writing, and peer review. Additionally, he has experience in laboratory management, supervising undergraduate and postgraduate research projects, and coordinating collaborative research efforts. His ability to integrate theoretical understanding with practical experimentation enables him to contribute effectively to high-impact research. These skills, combined with his professional ethics, leadership, and communication capabilities, make him a versatile and accomplished researcher.

Publications Top Notes

Title: Exploring the effectiveness of microbial fuel cell for the degradation of organic pollutants coupled with bio-energy generation
Authors: MO Idris, HC Kim, AA Yaqoob, MNM Ibrahim
Year: 2022
Citation: 87

Title: Scalability of biomass-derived graphene derivative materials as viable anode electrode for a commercialized microbial fuel cell: a systematic review
Authors: MO Idris, C Guerrero-Barajas, HC Kim, AA Yaqoob, MNM Ibrahim
Year: 2023
Citation: 62

Title: Sustainable microbial fuel cell functionalized with a bio-waste: a feasible route to formaldehyde bioremediation along with bioelectricity generation
Authors: MO Idris, NAM Noh, MNM Ibrahim, AA Yaqoob
Year: 2023
Citation: 61

Title: Introduction of adsorption techniques for heavy metals remediation
Authors: MO Idris, AA Yaqoob, MNM Ibrahim, A Ahmad, MB Alshammari
Year: 2023
Citation: 44

Title: Extraction and Characterization of Chitosan from Crab Shells: Kinetic and Thermodynamic Studies of Arsenic and Copper Adsorption from Electroplating Wastewater
Authors: A Sumaila, MM Ndamitso, YA Iyaka, AS Abdulkareem, JO Tijani, MO Idris
Year: 2019
Citation: 36

Title: Synthesis and fabrication of palm kernel shell-derived modified electrodes: A practical step towards the industrialization of microbial fuel cells
Authors: MO Idris, MNM Ibrahim, NAM Noh, AA Yaqoob, MH Hussin
Year: 2023
Citation: 20

Title: Simultaneous naphthalene degradation and electricity production in a biowaste-powered microbial fuel cell
Authors: MO Idris, MNM Ibrahim, NAM Noh, AA Yaqoob, MH Hussin, IAM Shukri
Year: 2023
Citation: 17

Title: Biosynthesized metallic nanoarchitecture for photocatalytic degradation of emerging organochlorine and organophosphate pollutants: a review
Authors: SS Emmanuel, MO Idris, CO Olawoyin, AA Adesibikan, AA Aliyu
Year: 2024
Citation: 16

Title: Removal of toxic metal ions from wastewater through microbial fuel cells
Authors: AA Yaqoob, MO Idris, A Ahmad, NNM Daud, MNM Ibrahim
Year: 2022
Citation: 15

Title: Photocatalytic degradation of maxilon dye pollutants using nano‐architecture functional materials: a review
Authors: SS Emmanuel, AA Adesibikan, CO Olawoyin, MO Idris
Year: 2024
Citation: 14

Title: Structural properties of thermoluminescence dosimeter materials, preparation, application, and adaptability: a systematic review
Authors: GI Efenji, SM Iskandar, NN Yusof, JA Rabba, OI Mustapha, IM Fadhirul
Year: 2024
Citation: 11

Title: A review on eco‐sustainable photocatalytic degradation of pharmaceutical pollutants using biosynthesized nanoparticles
Authors: SS Emmanuel, AO Esan, FSO Afigo, AA Adesibikan, MO Idris
Year: 2024
Citation: 10

Title: Impact of Commercial Sugar as a Substrate in Single‐Chamber Microbial Fuel Cells to Improve the Energy Production with Bioremediation of Metals
Authors: M Omenesa Idris, N Al-Zaqri, I Warad, AMA Hossain, N Masud, M Ali
Year: 2023
Citation: 10

Title: Electrochemical measurements of microbial fuel cells (MFCs)
Authors: MO Idris, AA Yaqoob, MNM Ibrahim, NAM Noh, NNM Daud
Year: 2022
Citation: 9

Title: Assessment of biomass material as valuable electrode for high energy performance in microbial fuel cell with biodegradation of organic pollutant
Authors: MO Idris, MNM Ibrahim, NAM Noh, AA Yaqoob, NNM Daud, MH Hussin
Year: 2024
Citation: 8

Title: Biomass-derived activated carbon: a viable material for remediation of pb2+ and 2, 4-dichlorophenol (2, 4 DCP) through adsorption
Authors: MO Idris, AO Usman, Q Musa, AI Suleiman, JL Emmanuel, P Sambo
Year: 2021
Citation: 6

Title: Oxidation of vegetable waste and organic pollutant degradation to generate energy through microbial fuel cell
Authors: MO Idris, NAM Noh, MNM Ibrahim, AA Yaqoob, R Almeer, K Umar
Year: 2024
Citation: 5

Title: DETERMINATION OF ACTIVITY CONCENTRATION OF RADIOACTIVE ELEMENTS IN BOREHOLE AND WELL WATER SAMPLES FROM ADANKOLO NEW LAYOUT LOKOJA
Authors: GI Efenji, E E.K, I I, O S.F, FO Uloko, JA Nakale, KJ Ayua, MO Idris
Year: 2022
Citation: 4

Title: Spores and extracts of entomopathogenic fungal isolate (Paecilomyces formosus) as potential biolarvicide of anopheles mosquitoes
Authors: AI Suleiman, A Nasidi, R Nasir, JL Emmanuel, NS Sadi, MO Idris
Year: 2021
Citation: 3

Title: Isolation and characterization of select Crab Shells obtained
Authors: A Sumaila, MM Ndamitso
Year: 2020
Citation: 3

Conclusion

Dr. Mustapha Omenesa Idris is a deserving candidate for the Best Researcher Award due to his significant contributions to materials chemistry, bio-electrochemical systems, and sustainable technologies. His work in microbial fuel cells, nanomaterials, and environmental remediation has both scientific and societal impact, addressing critical energy and environmental challenges. With his strong track record of research, mentorship, and international collaboration, he has the potential to lead innovative projects and inspire future advancements in his field.

Mohamed Hebaishy | Computer Vision | Excellence in Computer Vision Award

Published on by Computer Vision Awards

Assoc. Prof. Dr. Mohamed Hebaishy | Computer Vision | Excellence in Computer Vision Award

Associate Prof. in ERI at Electronics Research Institute, Egypt

Dr. Mohamed Ahmed Hebaishy is a distinguished researcher with extensive expertise in biometrics, iris recognition, image processing, computer vision, and satellite imaging. He has made remarkable contributions through his work in human identification systems, advanced image representation, and security technologies. His career spans academia, research institutions, and international collaborations, combining theoretical innovation with real-world applications in areas such as space research and remote sensing. He has published in reputed journals and conferences, including IEEE and Springer platforms, and actively engages in research that bridges science and technology. Beyond his research output, he has held significant leadership roles, mentored graduate students, and reviewed research projects for universities and conferences. His diverse professional experiences, strong academic foundation, and continuous pursuit of impactful research highlight his commitment to advancing scientific knowledge and addressing global challenges, making him a valuable contributor to the academic and research community.

Professional Profile 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Mohamed Ahmed Hebaishy completed his Bachelor of Science in Electronic Engineering with a focus on automatic control and measurements at Menoufia University, where he built a strong foundation in control systems and electronics. He later pursued a Master of Science degree in Electronics and Communication at Cairo University, with his thesis centered on developing a fuzzy controller for flexible joint manipulators, reflecting his early focus on control and automation. His academic journey culminated in earning a Doctor of Philosophy in Information Technology from Vladimir State University in the Russian Federation, specializing in control system analysis and data processing. His doctoral thesis focused on using iris image processing in human identification systems, marking the beginning of his long-term contributions to the field of biometrics. Through these academic achievements, he has combined expertise in engineering, computing, and data-driven technologies, equipping him with the knowledge and skills to contribute meaningfully to interdisciplinary research.

Professional Experience

Dr. Mohamed Ahmed Hebaishy has built a rich professional career across academia and research institutions, holding positions that span lecturer, assistant professor, and department head roles. He has served as a researcher at the Electronics Research Institute, contributing to significant projects in informatics and computer science. His work extended to leadership in national space programs, where he played a key role in satellite image processing and payload command systems for EgyptSat missions. He also gained international academic experience as an assistant professor at Shaqra University in Saudi Arabia, where he later became head of the computer science department. His contributions include guiding research projects, supervising theses, and leading academic initiatives. Additionally, he has been a reviewer for major universities and scientific conferences, reflecting his involvement in shaping the academic community. His experience demonstrates a balance of teaching, research, and leadership, making him a well-rounded academic and professional.

Research Interest

Dr. Mohamed Ahmed Hebaishy’s research interests lie at the intersection of biometrics, image processing, computer vision, and artificial intelligence, with a strong emphasis on human identification systems and security technologies. He has worked extensively on iris recognition, exploring innovative approaches to enhance accuracy and efficiency in biometric applications. His interests also extend to satellite imaging and remote sensing, where he has contributed to projects in national space programs, including the development of image processing systems for EgyptSat satellites. In recent years, his focus has broadened to include advanced methods in pattern recognition, machine learning, and computer-aided automation systems. He is also engaged in applied research addressing real-world challenges such as waste sorting, wireless communication, and medical applications of imaging. His diverse interests reflect a commitment to advancing cutting-edge technologies that improve security, automation, and sustainability, while also fostering new interdisciplinary pathways in computer science and engineering.

Award and Honor

Throughout his career, Dr. Mohamed Ahmed Hebaishy has received recognition for his contributions to research, teaching, and leadership within the fields of biometrics, image processing, and space technology. His involvement in the EgyptSat satellite programs and ITIDA-funded security projects demonstrated his ability to translate research into impactful applications, earning him acknowledgment within the scientific community. He has also been invited as a reviewer for universities, research conferences, and scientific committees, reflecting trust in his expertise and judgment. His leadership as head of the computer science department at Shaqra University further highlights his role in shaping academic excellence and guiding student development. While his curriculum vitae does not list specific awards, his record of sustained contributions, successful project leadership, and active engagement in international research platforms stands as a form of recognition in itself. His ongoing publications in reputed journals further strengthen his professional standing as a dedicated and accomplished researcher.

Research Skill

Dr. Mohamed Ahmed Hebaishy possesses a broad set of research skills that reflect his deep expertise in both theoretical and applied aspects of computer science and engineering. He is skilled in biometric system design, with specialization in iris recognition, image processing algorithms, and human identification technologies. His technical capabilities extend to satellite image analysis, data processing, and control systems, where he has led projects involving payload command systems for national space programs. He is proficient in developing and applying advanced algorithms, including fuzzy logic, wavelet transforms, and optimization techniques, to solve complex research problems. His experience also covers interdisciplinary areas such as wireless communication systems, security applications, and automated testing tools. Beyond technical expertise, he has strong skills in project leadership, academic supervision, and research collaboration, enabling him to contribute effectively to both academic and applied research communities. His skill set demonstrates adaptability, innovation, and problem-solving ability.

Publications Top Notes

Title: A comparative study of QTP and load runner automated testing tools and their contributions to software project scenario
Authors: M Imran, M Hebaishy, AS Alotaibi
Year: 2016
Citation: 12

Title: Road extraction from high resolution satellite images by morphological direction filtering and length filtering
Authors: TM Talal, MI Dessouky, A El-Sayed, M Hebaishy, FA El-Samie
Year: 2008
Citation: 12

Title: Increasing the Efficiency of Iris Recognition Systems by Using Multi-Channel Frequencies of Gabor Filter
Authors: AS Alotaibi, MA Hebaishy
Year: 2014
Citation: 7

Title: Extraction of roads from high-resolution satellite images with the discrete wavelet transform
Authors: TM Talal, A El-Sayed, M Hebaishy, MI Dessouky, SA Alshebeili
Year: 2013
Citation: 4

Title: Optimized Daugman’s algorithm for iris localization
Authors: MA Hebaishy
Year: 2008
Citation: 4

Title: Sibs: A sparse encoder utilizing self-inspired bases for efficient image representation
Authors: AN Omara, MA Hebaishy, MS Abdallah, YI Cho
Year: 2024
Citation: 3

Title: Poster: Optimized Daugman’s algorithm for iris localization
Authors: M Hebaishy
Year: 2008
Citation: 3

Title: Fast Fingerprint Identification based on the DoG Filter
Authors: MA Hebaishy, FA Syam
Year: 2025

Title: S-shaped patch antenna array for automotive applications in X-band for wireless communications
Authors: MA Hebaishy
Year: 2024

Title: Building an automatic waste sorting system with controller based wireless sensor smart segregation system
Authors: MA Hebaishy
Year: 2024

Title: Security system based on human iris
Authors: HS Ahmed, MA Hebaishy
Year: 2014

Title: Attitude determination for geostationary satellite using optimized real time image registration algorithm
Authors: AE OA Elsayed, A Farrag, M Hebaishy
Year: 2009

Title: Texture analysis of the human iris for high authentication
Authors: MA Hebaishy, BV Gerkov
Year: 2002

Title: Using phase demodulator for encoding iris
Authors: AS Alotaibi, MA Hebaishy

Conclusion

Dr. Mohamed Ahmed Hebaishy is highly deserving of the Best Researcher Award for his significant contributions to biometrics, image processing, and satellite imaging, which have advanced both scientific understanding and practical applications in security and space research. His extensive academic career, impactful publications, leadership roles, and dedication to mentoring students highlight his commitment to advancing knowledge and fostering innovation. With his proven expertise and strong foundation in applied research, he is well positioned to continue driving advancements in computer vision, human identification systems, and international collaborations, further strengthening his role as a leader in research and society.

Christine Carrie Bruce | Digital Pathology | Best Researcher Award

Published on by Computer Vision Awards

Ms. Christine Carrie Bruce | Digital Pathology | Best Researcher Award

Senior Program Director at University Health Network, Canada

Christine Bruce is a distinguished healthcare leader and researcher whose career spans leadership in laboratory medicine, digital pathology, and healthcare transformation. She has demonstrated expertise in advancing diagnostic services, implementing innovative solutions in laboratory operations, and leading system-level strategies that improve patient care and access. Her contributions extend from establishing large-scale COVID-19 testing operations to pioneering digital pathology for rural and remote communities, ensuring equitable healthcare delivery across regions. She has authored and co-authored impactful publications in high-impact journals and has actively shared knowledge through national and international conferences. Christine also plays a vital role in academia, mentoring students and professionals through teaching, lectures, and professional board service. With a strong focus on healthcare integration, innovation, and applied research, she continues to bridge the gap between research, policy, and clinical practice. Her leadership, research contributions, and dedication to advancing laboratory medicine highlight her as a prominent figure in the field.

Professional Profile 

Scopus Profile

Education

Christine Bruce has pursued a lifelong commitment to education and professional development in healthcare and management. She began her academic journey with a Diploma in Medical Laboratory Science from St. Lawrence College, which laid the foundation for her clinical and technical expertise. She later earned a Bachelor of Health Administration in Health Services Management from Toronto Metropolitan University, followed by a Master of Health Administration in Community Care from the same institution. Christine is currently pursuing a Doctor of Business Administration at Heriot Watt University in Edinburgh, reflecting her dedication to combining research with practical healthcare leadership. Alongside formal degrees, she has acquired advanced certifications, including a Lean Sigma Black Belt, a Lean Sigma Green Belt, and a Clinical Laboratory Quality Manager Certificate, which enhance her capacity for innovation, quality improvement, and system optimization. She is also recognized as a Certified Health Executive by the Canadian College of Health Leaders, underscoring her academic and professional excellence.

Professional Experience

Christine Bruce has built a distinguished career through progressive leadership roles in healthcare organizations across Canada. She currently serves as Executive Director of Regionalization for Northwest Ontario Laboratory Services and as Senior Director of the Laboratory Medicine Program at University Health Network, where she oversees one of the country’s largest laboratory medicine programs. In these roles, she leads teams of hundreds of professionals and physicians, managing multimillion-dollar budgets while driving transformative initiatives such as digital pathology adoption, standardized quality management, and advanced workforce development. She previously held leadership positions at Sinai Health, where she helped establish Ontario’s largest COVID-19 testing facility, and at Grand River Hospital and St. Mary’s General Hospital, where she directed integrated laboratory services. Her earlier career includes senior roles at LifeLabs, where she contributed to quality systems and business development. Christine’s work has consistently emphasized system integration, operational efficiency, and innovative models of care that advance patient outcomes.

Research Interest

Christine Bruce’s research interests focus on the intersection of laboratory medicine, healthcare innovation, and digital transformation. She is particularly passionate about advancing digital pathology and implementing diagnostic technologies that improve access to high-quality care in underserved and remote communities. Her work explores practical applications of research to transform health systems, including biomarker implementation, laboratory service optimization, and integration of artificial intelligence into clinical diagnostics. She has also contributed significantly to pandemic-related research, particularly in scaling up COVID-19 diagnostic services and developing system-level strategies for rapid response and resiliency. Christine’s interests extend to healthcare workforce innovation, where she studies new models of professional roles and interdisciplinary academic practice to address national shortages in laboratory science. Through collaborative and translational research, she aims to align scientific discovery with policy and clinical implementation, ensuring measurable improvements in patient care, healthcare accessibility, and system sustainability while positioning Canada as a leader in laboratory innovation.

Award and Honor

Christine Bruce has received numerous awards and honors that reflect her outstanding contributions to healthcare, research, and professional leadership. She was recognized with the A.R. Shearer Pride of the Profession Award by the Canadian Society for Medical Laboratory Science for her dedication to advancing laboratory medicine. At University Health Network, she received the Local Impact Award for her leadership in driving system transformation and innovation. She was also awarded an Honorary Diploma from St. Lawrence College, acknowledging her significant contributions to education and the profession. Christine has been a nominee for the Premier’s Award for College Graduates, which highlights individuals who have made extraordinary achievements in their fields. Other notable recognitions include the Gaman Modi Award of Excellence, the Founders Fund Award, and the PreAnalytical Excellence Award, each highlighting different facets of her impact from research excellence to operational innovation. These honors collectively underscore her national influence and professional distinction.

Research Skill

Christine Bruce possesses a diverse range of research skills that have enabled her to make impactful contributions to laboratory medicine and healthcare innovation. She is skilled in applied clinical research, with expertise in designing and implementing studies that translate directly into improved patient care and healthcare delivery models. Her proficiency in digital pathology, biomarker implementation, and diagnostic system transformation demonstrates her ability to integrate emerging technologies into practical clinical settings. She has extensive experience with quality management systems, Lean Six Sigma methodologies, and data-driven performance evaluation, which enhance her capability to conduct rigorous research with measurable outcomes. Christine is also adept at collaborative and interdisciplinary research, working across academic institutions, hospitals, and health networks to achieve shared objectives. Her skills in academic writing and dissemination are evident in her publications in high-impact journals, as well as her presentations at international conferences. Collectively, these skills position her as a strong applied research leader.

Publications Top Notes

Title: Guidance for securing approvals for new biomarkers: from discovery to clinical implementation

Authors: Harriet Feilotter, Christine Bruce, Eleftherios P. Diamandis, Miyo K. Chatanaka, George M. Yousef

Year: 2024

Citation: 3

Summary: This review article provides a comprehensive guide for the technical and clinical validation parameters necessary for the successful commercialization of molecular biomarkers. It outlines the various steps involved in translating a molecular discovery into clinical practice, emphasizing the importance of rigorous validation processes. The paper also discusses different options for regulatory approvals, including companion diagnostics, laboratory-developed tests, and direct-to-consumer testing, highlighting the complexities and considerations at each stage of biomarker development.

Conclusion

Christine Bruce is highly deserving of the Best Researcher Award for her exceptional contributions to laboratory medicine, digital pathology, and healthcare transformation. Her research has advanced diagnostic science, improved access to high-quality laboratory services, and shaped policy and practice across Canada. With her ongoing academic engagement, leadership roles, and dedication to innovation, she demonstrates strong potential for continued impact in both national and global research communities, making her a distinguished candidate for this recognition.