Ms. Aurélie Cools, University of Mons, Belgium.
Aurélie Cools is a Ph.D. candidate in Engineering Sciences at the University of Mons (UMons), specializing in deep neural networks and dimensionality reduction for CBIR search engines. She holds dual Master’s degrees: Civil Engineering in Computer Science and Management (Summa Cum Laude) and Management Engineering (Magna Cum Laude), showcasing her expertise in software engineering, business analytics, and optimization. Alongside her research, she contributes as a teaching assistant at UMons. With a strong foundation in Python, SQL, and PyTorch, Aurélie is multilingual and adept at problem-solving, team management, and communication. 
Publication Profile
Education and Experience
Education
- Ph.D. in Engineering Sciences
- Institution: University of Mons (UMons), Polytechnic Faculty
- Thesis Topic: CBIR search engine with deep neural networks and dimensionality reduction methods
- Duration: 2021 – Present
- Master’s in Civil Engineering (Summa Cum Laude)
- Institution: UMons, Polytechnic Faculty
- Specialization: Software Engineering and Business Intelligence
- Duration: 2018 – 2021
- Master’s in Management Engineering (Magna Cum Laude)
- Institution: UCL Mons
- Specialization: Business Analytics – Logistics and Transportation
- Duration: 2015 – 2017
- Bachelor’s in Management Engineering (Cum Laude)
- Institution: UCL Mons
- Duration: 2012 – 2015
Experience
- Teaching Assistant & Ph.D. Student
- Institution: UMons
- Duration: September 2021 – Present
- Credit Analyst
- Institution: CPH Bank, La Louvière
- Duration: July 2017 – August 2021
- Student Worker
- Institution: Colruyt Group, Mons
- Duration: March 2013 – December 2016
Suitability For The Award
Ms. Aurélie Cools is an outstanding candidate for the Best Researcher Award, combining academic excellence with impactful research. Currently pursuing a Ph.D. in Engineering Sciences at the University of Mons, her work on CBIR systems using deep neural networks and dimensionality reduction demonstrates innovation and technical expertise. With dual Master’s degrees in Civil and Management Engineering earned with high honors, Aurélie excels in both research and practical applications. Her proficiency in programming, data analysis, and problem-solving, coupled with strong communication skills, makes her a deserving nominee.
Professional Development
Aurélie excels in the realms of engineering and management, leveraging cutting-edge techniques like deep neural networks and dimensionality reduction. Her research bridges technical and analytical fields, emphasizing CBIR technologies for efficient image retrieval. With years of experience as a teaching assistant, she fosters innovation and critical thinking among students. Aurélie’s blend of programming skills in Python, SQL, and PyTorch, coupled with proficiency in tools like MongoDB and Excel, enhances her adaptability in diverse challenges. A polyglot and skilled communicator, she thrives in team management, problem-solving, and delivering impactful solutions.
Research Focus
Aurélie’s research focuses on developing advanced Content-Based Image Retrieval (CBIR) systems, leveraging deep neural networks and cutting-edge dimensionality reduction techniques to enhance image search and analysis efficiency. Her interdisciplinary approach combines software engineering, artificial intelligence, and data science for innovative solutions. With a keen interest in the practical applications of CBIR, such as medical imaging or multimedia management, Aurélie contributes to expanding the potential of machine learning in real-world scenarios. Her expertise lies at the intersection of engineering precision and computational intelligence, making her a significant contributor to AI-driven image processing.
Publication Top Notes
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A New Comparative Study of Dimensionality Reduction Methods in Large-Scale Image Retrieval (2022) | Published: 2022-05-13
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A Comparative Study of Reduction Methods Applied on a Convolutional Neural Network (2022) | Published: 2022-04-28