Manjunath BR | Machine Learning | Best Researcher Award

Prof. Dr. Manjunath BR | Machine Learning | Best Researcher Award

Professor | Tecnologico De Monterrey | Mexico

Prof. Dr. Manjunath BR is an accomplished academic leader and finance professional specializing in business analytics, financial modeling, econometrics, fintech, and artificial intelligence applications in finance. With extensive experience across academia and industry, he has contributed significantly to advancing data-driven financial education and research. His expertise spans financial analytics, investment management, corporate restructuring, and data visualization using advanced tools such as EViews, R, Python, Tableau, and Power BI. He has published extensively in ABDC, Scopus, UGC, and peer-reviewed journals, focusing on the intersection of finance, data science, and technology. As a researcher and educator, he integrates predictive analytics and machine learning into financial decision-making, contributing to the understanding of fintech adoption, banking innovations, and risk management. His academic leadership includes curriculum design, faculty development, and corporate collaborations to enhance experiential learning. He has served as a resource person for numerous international workshops and training programs on financial analytics, econometrics, and data visualization, empowering professionals and students with analytical and quantitative skills. Dr. Manjunath has authored and edited several books with leading global publishers, covering transformative areas such as AI in management education, blockchain economics, sustainable investment, and Quality 5.0 paradigms. He has also secured a patent for the application of AI in optimizing HR data management and authored a textbook on machine and deep learning. His professional journey embodies innovation, interdisciplinary scholarship, and a commitment to integrating technology with finance to foster global academic and industry excellence.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

Raju, J. K., Manjunath, B. R., & Rehaman, M. (2018). An empirical study on the effect of gross domestic product on inflation: Evidence from Indian data. Academy of Accounting and Financial Studies Journal, 22(6), 1–11.

Raju, J. K., Manjunath, B. R., & Dhakal, M. H. (2015). Impact and challenges of merger and acquisition in Nepalese banking and financial institutions. Journal of Exclusive Management Science, 4(8), 25–33.

Raju, J. K., Manjunath, B. R., & G. M. M. N. (2015). Performance evaluation of Indian equity mutual fund schemes. Journal of Business Management & Social Sciences Research (JBM&SSR).

Manjunath, B. R., & Raju, J. K. (2020). Short-run performance evaluation of under-priced Indian IPOs. Law and Financial Markets Review.

Chaitra, R., Manjunath, B. R., & Rehaman, M. (2019). An analysis of pre and post-merger of Indian banks: An event analysis approach. International Journal for Research in Engineering Application & Management, 4.

Abdelrhman BASSIOUNY | Computer Science and Artificial Intelligence | Best Researcher Award

Mr. Abdelrhman BASSIOUNY | Computer Science and Artificial Intelligence | Best Researcher Award

University of Bremen Germany

Abdelrhman Bassiouny is a passionate Egyptian robotics researcher specializing in marine robotics, autonomous systems, and AI-powered disassembly. With international experience across Germany, France, and Egypt, he combines technical mastery in robotics with a strong academic background. He thrives in hands-on innovation, contributing to cutting-edge projects from underwater VSLAM to robotic e-waste disassembly. 🌊🤖

Profile

Research Gate

Scopus

🎓 Education

Abdelrhman is currently completing his Erasmus Mundus Joint Master’s Degree in Marine & Maritime Intelligent Robotics (MIR), where he studied at Université de Toulon (France) and Universidad Jaume I (Spain). He graduated with honors in Mechatronics & Automation Engineering from Ain Shams University, Egypt. He also expanded his knowledge through specialized online courses in Deep Learning, Self-Driving Cars, and Project Management. 📘🌍
🔗 Master MIR Program
🔗 Ain Shams University

🛠️ Experience

Abdelrhman brings versatile research and teaching experience:

  • Master Thesis Intern at University of Bremen (Germany): Developed a query interface and machine learning pipeline for NEEMs robotics database.

  • Underwater VSLAM Intern at Laboratoire COSMER (France): Benchmarked SLAM algorithms using BlueROV in collaboration with IFREMER.

  • Research Assistant at Ain Shams University (Egypt): Led autonomous robotic disassembly projects, winning 3rd place in Robothon 2021.

  • Teaching Assistant at Ain Shams University: Taught ROS-based robotic control and supervised final-year projects.
    🌐 LinkedIn | 🌍 Personal Website

🔬 Research Interests

Abdelrhman’s research centers on:

  • Autonomous Robotics & Human-Robot Interaction 🤝

  • Symbolic Reasoning & Knowledge Representation 🧠

  • Underwater SLAM and Marine Robotics 🌊

  • E-waste Disassembly Automation using AI ♻️

  • ROS, TensorFlow, and Vision-based Robotics 📷

🏆 Awards

  • 🥇 Best Scientific Methodology AwardRoboCup MSL 2022 (Thailand)
    RoboCup 2022 History

  • 🥈 Runner-UpMIR Championship – Guerledus Challenge 2022
    Challenge Info

  • 🥉 3rd Place + Lightning Speed AwardRobothon Grand Challenge 2021 (TUM, Germany)
    Robothon Video

📚 Publications Top Notes: 

Prompt: Publications with hyperlinks, published year, journal (if applicable), and citation details in paragraph form.

Abdelrhman has authored two impactful research publications related to robotic disassembly of electronic waste:

“Comparison of Different Computer Vision Approaches for E-waste Components Detection to Automate E-waste Disassembly” (2021) – This paper evaluates vision-based algorithms for component detection, supporting more efficient and sustainable e-waste recycling.
🔗 View Publication
📈 Cited by: Google Scholar results

“Autonomous Non-Destructive Assembly/Disassembly of Electronic Components using A Robotic Arm” (2021) – Introduced a robotic system for semi-destructive disassembly using ROS and vision systems.
🔗 View Publication
📈 Cited by: Google Scholar results