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

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

Sinan Xiao – Structural Reliability – Best Researcher Award 

Dr. Sinan Xiao - Structural Reliability - Best Researcher Award 

University of Bath - United Kingdom

Author Profile

Early Academic Pursuits

Dr. Sinan Xiao's academic journey began at Northwestern Polytechnical University in Xi’an, China, where he pursued both his Bachelor's and PhD degrees in Flight Vehicle Design. His undergraduate studies, which spanned from September 2009 to July 2013, laid the foundational knowledge required for his advanced studies. Sinan then continued at the same university to earn his PhD from September 2013 to June 2018. His doctoral research focused on Structure Reliability and Global Sensitivity Analysis, which provided him with a robust framework in reliability sensitivity analysis, aimed at enhancing the dependability and robustness of aerospace structures. This period was crucial for Sinan as it equipped him with the skills and expertise necessary to navigate and contribute to the complex field of aerospace engineering and uncertainty quantification.

Professional Endeavors

After completing his PhD, Sinan embarked on a series of postdoctoral appointments that expanded his research experience across different countries and institutions. He started as a Post-doctor at the University of Stuttgart, Germany, from August 2018 to March 2021, where he worked on global sensitivity analysis of dynamic models with applications to hydrosystems. His tenure in Germany continued with a position at Forschungszentrum Jülich GmbH from February 2020 to March 2021, focusing on the computational frameworks for calibration and validation of mathematical models. In June 2021, Sinan joined the University of Bath in the UK as a Research Associate, engaging in Bayesian Uncertainty Quantification for aerospace composite structures, demonstrating his continued focus on enhancing model predictability and robustness in engineering applications.

Contributions and Research Focus

Dr. Sinan Xiao’s contributions to the field are marked by his deep commitment to advancing the methodologies of uncertainty quantification, reliability analysis, and sensitivity analysis. His doctoral and post-doctoral research have notably contributed to the understanding and improvement of structural reliability and the development of robust modeling techniques in aerospace engineering. His significant projects include the Bayesian inversion of multi-Gaussian fields, the development of generalized global sensitivity analysis methods, and creating computational frameworks that improve the predictive accuracy of complex models. Sinan’s work is characterized by his innovative use of Bayesian statistical methods, Markov chain Monte Carlo simulations, and various other advanced statistical techniques to tackle the challenges in his field.

Accolades and Recognition

Dr. Sinan Xiao's scholarly excellence and research impact have been recognized with several awards and scholarships. He received the Outstanding Doctoral Dissertation award from Northwestern Polytechnical University, acknowledging his exceptional contributions to aerospace engineering research. Additionally, his academic endeavors have been supported by prestigious scholarships such as the China Scholarship Council (CSC) scholarship and the Deutscher Akademischer Austauschdienst (DAAD) scholarship, which facilitated his research collaborations and postdoctoral training in Germany.

Impact and Influence

Dr. Sinan’s research has not only advanced academic knowledge but also provided practical solutions to real-world problems in aerospace structures and beyond. His works are frequently cited in academic circles, underlining their relevance and impact on the field of engineering. Sinan also serves as a reviewer for several esteemed journals, contributing his expertise to uphold the quality and integrity of scholarly publications across various engineering disciplines.

Legacy and Future Contributions

Looking to the future, Sinan Xiao is poised to continue influencing the fields of uncertainty quantification and reliability engineering through both his ongoing research and teaching endeavors. He mentors young scholars and engineers, preparing the next generation to push the boundaries of aerospace engineering and computational analysis further. His interdisciplinary approach and international collaborations are likely to foster innovations that transcend traditional boundaries, enhancing the safety, efficiency, and reliability of engineering applications worldwide. As Sinan progresses in his career, his work is expected to lead to more resilient and adaptive engineering practices, reflective of his deep expertise and forward-thinking mindset.

Citations

A total of  810 citations for his publications, demonstrating the impact and recognition of her research within the academic community.