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

Snehashis Majhi – Computer Vision – Best Researcher Award

Mr. Snehashis Majhi - Computer Vision - Best Researcher Award 

Institut National de Recherche en Informatique et en Automatique - France

Author Profile

Early Academic Pursuits

Mr. Snehashis Majhi embarked on his academic journey with a Bachelor of Technology in Computer Science and Engineering from the National Institute of Science and Technology, Berhampur, India. During this time, he delved into the realm of computer vision with his thesis titled "Design and Development of CBIR system using Classification Confidence," under the guidance of Dr. Jatindra Kumar Dash. This early exposure laid the foundation for his future endeavors in research and development.

Professional Endeavors

Mr. Majhi's professional journey commenced with an internship at INRIA, Sophia Antipolis Cedex BP 93, France, where he contributed to the development of a deep learning pipeline for human activity detection in smart home scenarios. This experience provided him with practical insights into real-world applications of deep learning technologies.

Following his internship, Majhi ventured into the field of glaucoma detection during his tenure as a Research Fellow at MNIT Jaipur, India. Here, he developed a deep convolutional neural network for detecting glaucoma diseases in fundus images, showcasing his versatility in applying deep learning techniques to healthcare domains.

Currently serving as a Research Scholar at INRIA Sophia Antipolis, France, Majhi's primary focus lies in developing human-scene centric video abnormality detection methods for smart-city surveillance systems. His research involves collaboration with Toyota Motor Europe and Woven Planet, demonstrating his ability to work on interdisciplinary projects with industry partners.

Contributions and Research Focus

Mr. Majhi's research contributions span various aspects of computer vision and deep learning. Noteworthy among his works is the development of the Human-Scene Network (HSN) and the Outlier-Embedded Cross Temporal Scale Transformer (OE-CTST) for video anomaly detection. These innovations address critical challenges in surveillance systems, showcasing Majhi's expertise in designing novel architectures and algorithms for complex tasks.

His research endeavors also encompass weakly-supervised learning techniques and optimization methods tailored for anomaly detection, underscoring his commitment to advancing the state-of-the-art in video analysis.

Accolades and Recognition

Mr. Majhi's contributions have been recognized through publications in reputable conferences and journals, including IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) and International Conference on Neural Information Processing (ICONIP). His papers have received commendable rankings, reflecting the significance and impact of his research in the scientific community.

Impact and Influence

Through his research and academic pursuits, Mr. Majhi has made significant contributions to the fields of computer vision and machine learning. His innovative approaches to video anomaly detection have the potential to enhance surveillance systems' effectiveness, thereby contributing to the advancement of public safety and security measures.

Legacy and Future Contributions

Mr. Majhi's legacy lies in his pioneering work in video anomaly detection and deep learning applications. His research serves as a stepping stone for future advancements in smart-city surveillance, healthcare diagnostics, and beyond. As he continues his academic and professional journey, Majhi is poised to make further contributions to the field, driving innovation and addressing societal challenges through cutting-edge technologies.

Citations

  • Citations   97
  • h-index       5
  • i10-index   4

Notable Publication