Aurélie Cools | Deep Neural Networks | Best Researcher Award

Posted on by ScienceFather

Aurélie Cools | Deep Neural Networks | Best Researcher Award

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

Orcid

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

  • A New Comparative Study of Dimensionality Reduction Methods in Large-Scale Image Retrieval (2022) 📚 | Published: 2022-05-13
  • A Comparative Study of Reduction Methods Applied on a Convolutional Neural Network (2022) 📖 | 🗓️ Published: 2022-04-28

Prof. Jin Ho Suh | Deep Neural Network Awards | Best Researcher Award

Posted on by ScienceFather

Prof. Jin Ho Suh | Deep Neural Network Awards | Best Researcher Award

Prof. Jin Ho Suh, Pukyong National University, South Korea

Dr. Jin-Ho Suh is a distinguished professor and expert in robotics, currently leading the Field Robotics Laboratory (FRLab) within the Major of Mechanical System Engineering at Pukyong National University, South Korea. With a Ph.D. in Control Engineering from the Tokyo Institute of Technology, Japan, and over two decades of academic and professional experience, Dr. Suh has significantly contributed to the fields of robotics and mechanical systems. He has held prominent roles, including Director of the Institute of Control, Robotics, and Systems, and is a Senior Member of IEEE. His leadership extends to national initiatives as the Chairman of the National Core Technology Committee for Robotics in South Korea and as an expert member of the Presidential Advisory Council on Science & Technology.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award: Prof. Jin-Ho Suh

Prof. Jin-Ho Suh, a distinguished researcher in the field of robotics and control engineering, holds a Ph.D. in Control Engineering from the Tokyo Institute of Technology and currently serves as a professor at Pukyong National University in South Korea. His extensive academic and professional experience, combined with significant contributions to the field, makes him an excellent candidate for the Best Researcher Award.

Education 🎓

  • Ph.D. in Control Engineering
    Tokyo Institute of Technology, Japan (Dec 1998 – Mar 2002)
  • Master of Engineering
    Graduate School of Engineering, Pukyong National University, South Korea (Mar 1996 – Feb 1998)
  • Bachelor of Science in Mathematics
    Hanyang University, South Korea (Mar 1989 – Feb 1993)

Work Experience 🛠️

  • Professor (Sep 2018 – Present)
    Major of Mechanical System Engineering, Pukyong National University
  • Senior Member (Nov 2022 – Present)
    Institute of Electrical and Electronics Engineers (IEEE)
  • Director
    • Institute of Control, Robotics, and Systems (Jan 2022 – Present)
    • Korean Society for Precision and Engineering (Jan 2020 – Present)
    • Korea Robotics Society (Jan 2018 – Present)
    • Korean Society for Power System Engineering (Jan 2017 – Present)
  • Chairman of the National Core Technology Committee (Robot) (Nov 2017 – Present)
    Korean Association for Industrial Technology Security
  • Expert Member (Jan 2021 – Present)
    Presidential Advisory Council on Science & Technology, South Korea
  • Adjunct Professor (Dec 2013 – Aug 2018)
    Department of Mechanical Engineering, POSTECH
  • Director of R&D Division (Apr 2006 – Aug 2018)
    Korea Institute of Robotics and Convergence Technology (KIRO)
  • Post-Doctoral Fellow (Jun 2003 – Feb 2006)
    National Research Laboratory, Dong-A University

Achievements 🏆

  • Patents
    • 28 patents (7 international PCT)
  • Publications
    • 14 papers in international journals (13 SCI)
    • 23 papers in domestic journals (15 SCOPUS)
    • 15 papers in international conferences
    • 60 papers in domestic conferences

Awards and Honors 🌟

  • Director Roles in Leading Engineering Societies
    • Institute of Control, Robotics and Systems
    • Korea Robotics Society
    • Korean Society for Precision and Engineering
  • Presidential Advisory Council Member
    • Significant contributions to national robotics and precision engineering strategies.
  • Chairman, National Core Technology Committee (Robot)
    • Recognized leader in industrial robotics technology and security.

Publication Top Notes

Artificial Neural Network for Glider Detection in a Marine Environment by Improving a CNN Vision Encoder

Development of a Multi-Robot System for Pier Construction

Model-Free RBF Neural Network Intelligent-PID Control Applying Adaptive Robust Term for Quadrotor System

Development of Fishcake Gripping and Classification Automation Process Based on Suction Shape Transformation Gripper

Estimation and Control of a Towed Underwater Vehicle with Active Stationary and Low-Speed Maneuvering Capabilities

Adaptive Robust RBF-NN Nonsingular Terminal Sliding Mode Control Scheme for Application to Snake Robot’s Head for Image Stabilization

Development of Recovery System for Underwater Glider

Dr. Seyed Reza Nabavi | Nueral Network Awards | Best Researcher Award

Posted on by ScienceFather

Dr. Seyed Reza Nabavi | Nueral Network Awards | Best Researcher Award 

Dr. Seyed Reza Nabavi, University of Mazandaran, Iran

Dr. Seyed Reza Nabavi is an Associate Professor of Applied Chemistry in the Department of Applied Chemistry at the University of Mazandaran, Babolsar, Iran. he earned his Ph.D. in Applied Chemistry from the University of Tabriz in 2009, focusing on hybrid modeling and artificial intelligence applications for olefin process optimization. As a visiting scholar at the National University of Singapore in 2008, Dr. Nabavi further honed his expertise in chemical and biomolecular engineering. His teaching repertoire spans diverse topics, including transport phenomena, chemical reactor design, and chemical process modeling at both undergraduate and postgraduate levels. A prolific researcher, his interests lie in polymer nanotechnology, catalytic processes, machine learning in chemical process optimization, and pyrolysis. Notably, he has collaborated on significant projects, such as studying coke formation and inhibitors in naphtha thermal cracking at the bench scale, bridging academia and industry. Married and based in Iran, Dr. Nabavi has received recognition for his academic excellence, including being the top-ranked B.Sc. graduate.

Professional Profile:

ORCID

Suitability for Best Researcher Award: Seyed Reza Nabavi

Based on the provided curriculum vitae, Dr. Seyed Reza Nabavi demonstrates exceptional qualifications that make him a strong candidate for the Best Researcher Award. Below is a summary of his key accomplishments and attributes supporting his suitability for this recognition

🎓 Educational Background

  • Ph.D. in Applied Chemistry (2009), University of Tabriz 🧬
    Thesis: Application of Hybrid Modeling and Artificial Intelligence in Modeling and Optimization of Olefin Processes.

    • Visiting Scholar: National University of Singapore (Apr-Dec 2008).
  • M.Sc. in Applied Chemistry (2003), University of Tabriz 🧵
    Thesis: Preparation and Characterization of Conducting Polyaniline/Nylon-6 Composite Fibers.
  • B.Sc. in Applied Chemistry (2000), University of Sistan and Baluchestan 🏛️
    • 🎖️ First Rank among graduate students.

👨‍🏫 Teaching Experience

  • Expertise in teaching at M.Sc. and B.Sc. levels 📚, including advanced courses:
    • Transport Phenomena, Design of Experiments (DOE), Chemical Reactors, Process Control, and Petrochemical Processes.
    • Proficient in Modeling and Simulation and Unit Operation Laboratories.

🔬 Research Interests

  • Nanotechnology of Polymers: Electrospinning and Nanofiber Membranes 🧵.
  • Catalytic Processes: Ozonation, Photocatalysts, and Reaction Engineering ⚗️.
  • Modeling and Optimization: Applying Machine Learning and Evolutionary Algorithms 🤖.
  • Thermal Cracking & Pyrolysis: Exploring Coke Formation and Mitigation 🔥.

🏅 Academic Positions

  • Associate Professor: 2022 – Present, University of Mazandaran 🏫.
  • Assistant Professor: 2012 – 2022, University of Mazandaran.

🧪 Research Highlights

  • Lead researcher in projects like Coke Formation and Inhibitors in thermal cracking of naphtha (collaboration with Tabriz Petrochemical Company) 🛢️.
  • Published impactful research on polymers, reaction engineering, and optimization using cutting-edge AI techniques.

Publication top Notes:

Multi-Criteria Decision Making in Chemical and Process Engineering: Methods, Progress, and Potential

A liter scale synthesis of hierarchically mesoporous UiO-66 for removal of large antibiotics from wastewater

Data-Based Modeling, Multi-Objective Optimization and Multi-Criteria Decision Making of a Catalytic Ozonation Process for Degradation of a Colored Effluent

A bacterial cellulose-based LiSrVO4:Eu3+ nanosensor platform for smartphone sensing of levodopa and dopamine: point-of-care diagnosis of Parkinson’s disease

Parametric optimization of poly(ether sulfone) electrospun membrane for effective oil/water separation

Deep Learning Aided Multi-Objective Optimization and Multi-Criteria Decision Making in Thermal Cracking Process for Olefines Production