Mohd Faizan l Diagnostic Analytics | Research Excellence Award

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Mr. Mohd Faizan l Diagnostic Analytics | Research Excellence Award

University of Lille | France

Mohd Faizan is a PhD Candidate in Control Systems at the University of Lille, France, specializing in resilience control, fault-tolerant systems, power electronics, and DC microgrids. He holds an M.Tech and B.E. in Electrical Engineering from Aligarh Muslim University with first-class distinction. His professional experience includes PhD research at CRIStAL Laboratory on RTTR-based performance degradation prediction and prior research at NTUST, Taiwan, on LSTM-based PV fault detection. His research interests span resilience control, fault diagnosis, DC microgrids, and ML for control, supported by strong skills in MATLAB, Python, power converters, and hardware prototyping. He has received multiple scholarships, academic honors, and IEEE publications, positioning him for impactful research and engineering roles.

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Featured Publications

Online Estimation of Remaining Time to Recovery to Enhance Resilience Using Bond Graph Based Power Loss Estimation


IFAC Journal of Systems and Control, 2026
| Mohd Faizan, Mahdi Boukerdja, Anne Lise Gehin, Belkacem Ould Bouamama, Sumit Sood

Non-Linear Control of Interleaved Boost Converter Using Disturbance Observer-Based Approach


IEEE Access, 2025
| Avinash Mishra, Sanjoy Mandal, Jean-Yves Dieulot, Mrinal R. Bachute, Mohd Faizan et al.

Long Short-Term Memory-Based Feedforward Neural Network Algorithm for Photovoltaic Fault Detection Under Irradiance Conditions


IEEE Transactions on Instrumentation and Measurement, 2024
| Nien-Che Yang, Mohd Faizan

 

Design of 31-Level Asymmetrical Inverter With Reduced Components


International Journal of Circuit Theory and Applications, 2022

Design of Single Phase Five Level Packed U-Cell Inverter for Standalone and Grid Connected Modes


IEEE SEFET 2022 – International Conference on Sustainable Energy and Future Electric Transportation

Deme Hirko l Water Engineer | Research Excellence Award

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Dr. Deme Hirko l Water Engineer | Research Excellence Award

 Jimma University | Ethiopia

Dr. Deme Hirko is a motivated, results-oriented civil engineer with a PhD (defended November 2025, Stellenbosch University) and over 10 years of academic and research experience in water resources engineering, hydrology, and climate change modelling. His education spans a PhD in Civil Engineering, an MSc in Water Resources and Irrigation Engineering, and a BSc in Hydraulic and Water Resources Engineering. Professionally, Dr. Deme Hirko has served as lecturer, teaching assistant, hydrology analyst, site engineer, and departmental coordinator. His research interests include climate-resilient water allocation, hydrological modelling, and AI applications. He possesses strong skills in machine learning, WEAP, Python, GIS, and HPC, has supervised numerous theses, received institutional recognition for leadership, and is committed to advancing sustainable, data-driven water management through postdoctoral research.

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Featured Publications

Bushra Abro | Artificial Intelligence | Research Excellence Award

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Ms. Bushra Abro | Artificial Intelligence | Research Excellence Award

National Centre Of Robotics And Automation | Pakistan

Ms. Bushra Abro is a Computer and Information Engineer with a strong foundation in Electronic Engineering. She holds a Master’s degree in Computer and Information Engineering and a Bachelor’s in Electronic Engineering from Mehran University of Engineering & Technology, Pakistan, graduating top of her class. With professional experience as a Research Associate and Assistant at the National Centre of Robotics and Automation, she has contributed to projects in deep learning, computer vision, federated learning, and real-time condition monitoring. Her work has earned multiple publications and awards, including a gold medal at the All Pakistan IEEEP Student Seminar. She is passionate about advancing AI-driven technological innovation.

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Featured Publications


Towards Smarter Road Maintenance: YOLOv7-Seg for Real-Time Detection of Surface Defects

– Book Chapter, 2025Contributors: Bushra Abro; Sahil Jatoi; Muhammad Zakir Shaikh; Enrique Nava Baro; Bhawani Shankar Chowdhry; Mariofanna Milanova


Federated Learning-Based Road Defect Detection with Transformer Models for Real-Time Monitoring

– Computers, 2025Contributors: Bushra Abro; Sahil Jatoi; Muhammad Zakir Shaikh; Enrique Nava Baro; Mariofanna Milanova; Bhawani Shankar Chowdhry


Harnessing Machine Learning for Accurate Smog Level Prediction: A Study of Air Quality in India

– VAWKUM Transactions on Computer Sciences, 2025Contributors: Sahil Jatoi; Bushra Abro; Sanam Narejo; Yaqoob Ali Baloch; Kehkashan Asma


Learning Through Vision: Image Recognition in Early Education

– ICETECC Conference, 2025Contributors: Sahil Jatoi; Bushra Abro; Shafi Jiskani; Yaqoob Ali Baloch; Sanam Narejo; Kehkashan Asma


From Detection to Diagnosis: Elevating Track Fault Identification with Transfer Learning

– ICRAI Conference, 2024Contributors: Sahil Jatoi; Bushra Abro; Noorulain Mushtaq; Ali Akbar Shah Syed; Sanam Narejo; Mahaveer Rathi; Nida Maryam

Bardia Rodd | Machine Learning and AI Applications | Best Researcher Award

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Prof. Bardia Rodd | Machine Learning and AI Applications | Best Researcher Award

SUNY Upstate Medical University | United States

Prof. Bardia Rodd is an Associate Professor at SUNY Upstate Medical University and Associate Director of AI Innovation at the AI for Health Equity, Analytics, and Diagnostics (AHEAD) Center. He completed dual PhDs in Electrical & Computer Engineering from Université Laval and in Computer Science from the University of Malaya, alongside a postdoctoral fellowship at the University of Pennsylvania Perelman School of Medicine, focusing on precision medicine, artificial intelligence, and image-guided systems. His academic career spans faculty positions at SUNY Upstate, the University of Maryland, and Laval University, with extensive experience in biocomputational engineering, machine learning, medical image analysis, and data-driven healthcare solutions. He has led numerous grants, including initiatives in AI in education, biomedical research, and curriculum development. Prof. Rodd has supervised multiple graduate and postgraduate students and contributed to open educational resources, advancing accessible teaching in machine learning and bioengineering. He is an active member of professional societies including IEEE, SPIE, and the Association of Pathology Informatics, serving on multiple technical committees and leadership roles. His research interests include AI-driven biomedical imaging, predictive modeling, and health equity applications. Prof. Rodd has authored a textbook on machine learning for data analysis and continues to integrate research, teaching, and innovation to advance computational medicine and AI education.

Profile : Scopus

Featured Publications

Siezen, H., Awasthi, N., Rad, M. S., Ma, L., & Rodd, B. (2025). “Low-rank distribution embedding of dynamic thermographic data for breast cancer detection.” Journal of Thermal Biology, 104303.

Usamentiaga, R., Fidanza, A., Yousefi, B., Iacutone, G., Logroscino, G., & Sfarra, S. (2025). “Advancing knee injury prevention and anomaly detection in rugby players through automated processing of infrared thermography: A novel biothermodynamics approach.” Thermal Science and Engineering Progress, 103782.

Rad, M. S., Huang, J. V., Hosseini, M. M., Choudhary, R., Siezen, H., Akabari, R., et al. (2025). “Deep learning for digital pathology: A critical overview of methodological framework.” Journal of Pathology Informatics, 100514.

Yousefi, B., Khansari, M., Trask, R., Tallon, P., Carino, C., Afrasiyabi, A., Kundra, V., Ma, L., Ren, L., Farahani, K., & Hershman, M. (2025). “Measuring subtle HD data representation and multimodal imaging phenotype embedding for precision medicine.” IEEE Transactions on Instrumentation and Measurement, TIM-24-01821.

Cao, Y., Sutera, P., Mendes, W. S., Yousefi, B., Hrinivich, T., Deek, M., et al. (2024). “Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.” Radiotherapy and Oncology, 199, 110443.

Ying Jin | Big Data Analytics | Best Researcher Awards

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Ying Jin | Big Data Analytics | Best Researcher Award

Professor, Nanjing University, China

Profile

 Scopus 

Prof. Jin Ying is a Professor at the Department of Computer Science and Technology, Nanjing University, widely recognized for her leadership and contributions in computer education, data mining, citation analysis, big data, and virtual reality applications. Her career bridges teaching, research, and academic service, reflecting a strong commitment to advancing both theoretical knowledge and practical innovations in the field of computer science. She has served in multiple influential academic and professional roles, helping shape the direction of education and technology integration in China.

Education

Prof. Jin began her academic journey in geochemistry and later transitioned into information management and computer science, bringing an interdisciplinary perspective to her work. She earned her Ph.D. in Management with a specialization in data mining and CSSCI research, which laid the foundation for her impactful contributions in citation analysis, big data, and education technology. Her diverse educational background allows her to integrate cross-disciplinary insights into computer science education and applied research.

Experience

Throughout her career at Nanjing University, Prof. Jin has advanced from teaching assistant to lecturer, associate professor, and eventually full professor. She teaches a wide range of courses including computer applications, Visual Basic, Python, and C programming, with her pedagogy focusing on innovation and practical engagement. Beyond the classroom, she serves as Group Lead of Visual Basic and Secretary of the Center of College Computer Test in Jiangsu Province. She is also an active member of the Computer Education Supervisory Committee (MOE, Liberal Arts), Jiangsu Computer Foundation Education Committee, and the Council on East China University Computer Foundation Education. Additionally, she contributes as a judge for the National College Computer Design Competition and as a peer reviewer for CSSCI-indexed journals.

Research Interests

Prof. Jin’s research integrates education and technology, addressing critical issues such as AI-driven talent training models, programming pedagogy reform in the AI era, flipped classroom strategies, big data course design for non-computer majors, and innovations in youth IT education. She has published widely in international journals and conferences, making significant contributions to the fields of data mining, education reform, and computational thinking. Her authored teaching resources, including College Basic Computer Applications and New Visual Basic Programming, are used across universities and contribute to improving computer education nationwide.

Awards

Prof. Jin’s achievements have been recognized through numerous awards and honors. She received the prestigious First Tan Haoqiang Computer Education Fund Outstanding Teacher Award and Nanjing University’s Shilin Teaching Award, reflecting her excellence in teaching and mentorship. She has also earned multiple best paper awards and nominations at international conferences, alongside prizes for guiding student projects in national-level computer design competitions. These recognitions highlight her dual impact as both a researcher and educator.

Featured Publications

  • Jin, Y. (2024). Exploration of K12 Multi-level Information and AI Talent Training Model.

  • Jin, Y. (2024). Exploration on Teaching Content Reform of Programming Course in the Era of AI.

  • Jin, Y. (2024). A survey of research on several problems in the RoboCup3D simulation environment.

  • Jin, Y. (2023). The Role of Science and Technology Innovation Competition in Talent Cultivation and Development.

  • Jin, Y. (2022). An approach for evaluating course acceptance based on Bayesian network. Int. J. of Intelligent Internet of Things Computing.

  • Jin, Y. (2022). Design and Implementation of a Cloud-Native Platform for Financial Big Data Processing Course.

  • Jin, Y. (2022). Hierarchical and Diverse Cultivation of Data Thinking Capability in College Based on New High School Curriculum Standards.

Conclusion

By combining rigorous scholarship, innovative teaching, and impactful leadership, Prof. Jin Ying has significantly advanced computer education reform and promoted the integration of technology with pedagogy. Her interdisciplinary vision and ability to merge data-driven methods with educational practice have influenced generations of students, equipping them with computational skills essential for the digital age. Through her roles in research, teaching, academic committees, and national projects, she continues to shape the landscape of computer science education in China and beyond, fostering a forward-looking environment that bridges research, practice, and innovation.

Hugo Hervé-Côte – Machine Learning and AI Applications – Best Researcher Award 

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Mr. Hugo Hervé-Côte - Machine Learning and AI Applications - Best Researcher Award 

Ecole de Technologie Supérieure - Canada 

Author Profile

Early Academic Pursuits

Mr. Hugo Hervé-Côté embarked on his academic journey with a strong foundation in mechanical and industrial engineering. He began his education at the prestigious Ecole Nationale Supérieure des Arts et Métiers (ENSAM) in Angers, France, where he pursued a Diploma in Mechanical, Industrial, and Energy Engineering. During his time at ENSAM, from September 2019 to July 2021, Hugo honed his expertise in various engineering disciplines, setting the stage for his future endeavors in artificial intelligence and machine learning.

Following his graduation from ENSAM, Hugo continued to build on his technical knowledge and skills by pursuing a Master's degree in Automated Production Engineering (M.Sc.A) at the Ecole de Technologie Supérieure (ETS) in Montreal, Canada. From September 2021 to December 2023, he delved deeper into advanced engineering concepts and specialized in machine learning and computer vision. This dual-degree experience equipped him with a robust academic background and practical skills, making him a versatile engineer ready to tackle complex challenges in the industry.

Professional Endeavors

Mr. Hugo's professional journey has been marked by significant contributions to the fields of non-destructive testing (NDT) and industrial inspection. His career began with a notable role as an Artificial Intelligence Engineer at EddyFi Technologies in Quebec, Canada, from December 2023 to March 2024. In this position, Hugo focused on the development and implementation of machine learning models for analyzing eddy current images in non-destructive testing. He was instrumental in creating a user-friendly interface for visualizing and labeling complex magnetic data, and he developed a comprehensive data pipeline for training, validating, and testing machine learning models. His work culminated in a proof of concept that demonstrated the effectiveness of these models in real-world applications.

Prior to his role at EddyFi Technologies, Hugo gained valuable experience as a Student Researcher in Ultrasonic Inspection using Machine Learning at ETS and in collaboration with Nucleom. From September 2021 to December 2023, he worked on detecting defects in ultrasonic images, developing a standardized database for ultrasonic imaging, and addressing industrial challenges through regular project progress meetings with Nucleom and EddyFi. His efforts contributed to the advancement of ultrasonic inspection techniques and the integration of machine learning in this domain.

Contributions and Research Focus

Mr. Hugo's research focus has been centered on the application of machine learning and artificial intelligence in non-destructive testing and industrial inspection. His master's thesis involved the development of a tool for analyzing ultrasonic images using machine learning. This project, conducted from September 2021 to December 2023, included the acquisition of defect data from welds using Full Matrix Capture (FMC) and the development of algorithms for image reconstruction and defect detection. Hugo also created a significant labeled database of over 100,000 images, which served as a foundation for training deep learning models. His work was presented at conferences such as OnDuty in Windsor, ON (2022) and Toronto, ON (2023), and was published in the scientific journal Ultrasonics.

Hugo's research contributions extend to developing post-processors for CNC machines and designing cryogenic tool mounts. His projects have involved the practical application of engineering principles to solve real-world problems, demonstrating his ability to bridge the gap between theory and practice.

Accolades and Recognition

Mr. Hugo Hervé-Côté's work has been recognized both academically and professionally. His publication in Ultrasonics, titled "Automatic flaw detection in sectoral scans using machine learning," has garnered attention in the scientific community, showcasing his expertise in applying advanced technologies to improve industrial inspection processes. This publication is a testament to his dedication to research and innovation in the field of non-destructive testing.

Throughout his academic and professional career, Hugo has demonstrated excellence in his field, earning the respect of his peers and supervisors. His involvement in various projects and his ability to present his findings at conferences highlight his commitment to advancing knowledge and contributing to the engineering community.

Impact and Influence

Mr. Hugo's work has had a significant impact on the field of non-destructive testing and industrial inspection. By integrating machine learning techniques into traditional inspection methods, he has contributed to the development of more accurate and efficient defect detection processes. His research on ultrasonic imaging and eddy current testing has the potential to revolutionize how industries approach quality control and maintenance, leading to safer and more reliable infrastructure.

Hugo's contributions have also influenced the development of standardized databases and formats for storing and analyzing inspection data, facilitating collaboration and knowledge sharing among researchers and industry professionals. His efforts to create user-friendly interfaces and data pipelines have made advanced technologies more accessible to engineers and technicians, promoting the adoption of machine learning in industrial applications.

Legacy and Future Contributions

Mr. Hugo Hervé-Côté's legacy lies in his innovative approach to solving complex engineering problems through the application of machine learning and artificial intelligence. His work has paved the way for future advancements in non-destructive testing and industrial inspection, setting a high standard for integrating cutting-edge technologies into practical applications.

Looking ahead, Hugo's future contributions are likely to continue shaping the field of engineering. His strong foundation in mechanical and industrial engineering, combined with his expertise in artificial intelligence, positions him as a leader in the development of next-generation inspection techniques. As industries increasingly rely on data-driven solutions, Hugo's work will remain at the forefront of technological innovation, driving progress and improving safety and efficiency across various sectors.

Notable Publication