Paolo Dini | Data Analysis | Best Researcher Award

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Dr. Paolo Dini | Data Analysis | Best Researcher Award

Dr. Paolo Dini | Data Analysis | Leading Researcher at Centre Tecnològic de Telecomunicacions de Catalunya | Spain

Dr. Paolo Dini is a distinguished researcher in the field of information engineering, currently affiliated with the Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), where he leads research at the intersection of sustainable computing, wireless communication, and artificial intelligence. Dr. Paolo Dini holds a Ph.D. in Information and Communication Technologies, and his academic foundation has enabled him to make impactful contributions to the development of energy-efficient and intelligent network infrastructures. Over the years, he has amassed a prolific research portfolio with more than 60 peer-reviewed publications and over 2,300 citations, earning him an h-index of 25 and an i10-index of 54, according to Scopus. Professionally, Dr. Paolo Dini has held research and leadership roles in multiple European and international collaborative projects, contributing both to academia and industrial innovation. He has worked alongside prominent researchers from institutions like Ericsson, Politecnico di Bari, University of Padova, and CTTC, fostering multidisciplinary research in areas such as mobile traffic modeling, green networking, and edge intelligence. His expertise includes machine learning for network optimization, distributed systems, multi-agent systems, 5G and beyond architectures, and sustainable AI. These skills are further demonstrated by his role in developing algorithms and models for energy harvesting in mobile networks and predictive analytics for traffic anomaly detection. Dr. Paolo Dini’s research interests continue to evolve with the current technological landscape, focusing on combining AI with wireless systems to enable smarter, greener, and more adaptive communication environments.

Professional Profile: ORCID | Google Scholar

Selected Publications:

  1. Mobile traffic prediction from raw data using LSTM networks (2018) – 245 Citations

  2. HetNets powered by renewable energy sources: Sustainable next-generation cellular networks (2012) – 201 Citations

  3. SolarStat: Modeling photovoltaic sources through stochastic Markov processes (2014) – 108 Citations

  4. Detecting mobile traffic anomalies through physical control channel fingerprinting: A deep semi-supervised approach (2019) – 81 Citations

 

 

Dr. Jie Lv | Real-time Monitoring | Best Researcher Award

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Dr. Jie Lv | Real-time Monitoring | Best Researcher Award

Dr. Jie Lv, Kunming University of Science and Technology, China

Dr. Jie Lv is the Director of the Teaching and Research Office at Kunming University of Science and Technology, where she also serves as a Graduate Supervisor. She earned her Master’s degree in Land Resource Management and Ph.D. in Earth Exploration and Information Technology from the same institution. With a distinguished academic and research profile, Dr. Lv has led over 20 research projects—both completed and ongoing—and authored 35 peer-reviewed publications, including Tier 1 SCI and EI-indexed journals. Her work focuses on remote sensing applications for environmental monitoring, disaster assessment, and deep learning integration. A patent holder, accomplished author of three academic books, and key contributor to a nationally funded exploration project, Dr. Lv is also an active member of several professional bodies. Her dedication to interdisciplinary research and teaching excellence has significantly advanced geospatial science and remote sensing education in China.

Professional Profile:

ORCID

🏆 Summary of Suitability for Best Researcher Award

Nominee: Dr. Jie Lv
Designation: Director of Teaching and Research Office
Institution: Kunming University of Science and Technology

Dr. Jie Lv is a distinguished academic and researcher whose exceptional contributions to remote sensing, environmental monitoring, and deep learning applications position her as an ideal candidate for the Best Researcher Award. With a robust academic foundation and over a decade of experience, she has demonstrated consistent excellence in both theoretical and applied research.

🎓 Education

  • Ph.D. in Earth Exploration and Information Technology
    Kunming University of Science and Technology – 2014

  • Master’s Degree in Land Resource Management
    Kunming University of Science and Technology – 2011

💼 Work Experience

  • Director of Teaching and Research Office
    Kunming University of Science and Technology
    (Graduate Supervisor for master’s students)
    📌 Focused on remote sensing, disaster monitoring, and deep learning applications in environmental studies.

🌟 Key Achievements

  • 🧪 Research Projects: 10 completed + 11 ongoing

  • 📊 Publications: 35 peer-reviewed journal articles (19 as first/corresponding author)

    • 🥇 3 Tier 1 (SCI-indexed)

    • 🥈 7 Tier 1 (EI-indexed)

    • 🥉 5 in Tier 2 and Tier 3

  • 📚 Books Published:

    • Comprehensive Analysis and Study of Remote Sensing Survey for the Karst Mountainous Environment in Southeastern Yunnan (ISBN: 9787541682094)

    • Principles and Applications of Remote Sensing (ISBN: 978752213822)

    • Applied Research on Regional Eco-environmental Monitoring and Assessment Using Remote Sensing (ISBN: 9787568141963)

  • ⚙️ Patents: 3 granted invention patents, 6 utility model patents, 3 pending patents

  • 🛰️ Collaborative Research:
    National Science and Technology Major Project on Deep Earth Exploration (Total funding: RMB 7.05 million)

🏅 Awards & Honors

  • 🧠 CNKI Scholar – Recognized Editorial Contributor

  • 👩‍🏫 Expert Panel Member – Yunnan Land Evaluation & Registration Association

  • 🧾 Peer Reviewer – Ministry of Education Thesis Inspection System

  • 🌐 Professional Memberships:

    • Geographical Society of China (GSC)

    • China Remote Sensing Application Association (CRSAA)

    • China Association of Higher Education (CAHE)

Publication Top Notes:

Bridge Crack Segmentation Algorithm Based on Improved U-Net

Enhanced Landslide Visualization and Trace Identification Using LiDAR-Derived DEM

Ms. Yuri Kim | Data Monitoring | Best Researcher Award

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Ms. Yuri Kim | Data Monitoring | Best Researcher Award 

Ms. Yuri Kim, Korea University, South Korea

Yuri Kim is a Ph.D. candidate in Computer Science at Korea University, Seoul, South Korea, where she has been conducting research since September 2020 as a recipient of the ICT Elite Talent Development Program Scholarship. She holds a B.Sc. and M.Sc. in Computer Science from Eötvös Loránd University, Budapest, Hungary, where she graduated with distinction under the Stipendium Hungaricum Scholarship. Yuri has extensive experience in both academia and industry, having served as a lecturer at Korea University Graduate School of Education and Eötvös Loránd University, teaching advanced data structures and functional programming. She has also worked as a project manager at The Mihalik Group, leading the development of in-house business automation software, and as a student backend developer at Ericsson. Her research interests span natural language processing, machine learning, and financial modeling, with notable publications on stock trading recommendation systems, serendipity-based recommender systems, and Linked Open Data structures. She has been actively involved in entrepreneurial ventures, participating in the 2024 Korean I-Corps Program and the 2023 Innovation Startup School, focusing on AI-driven solutions. Proficient in Python, C, Go, and Clean, Yuri combines her expertise in programming with strong project management skills in Agile and Scrum methodologies.

Professional Profile:

ORCID

Summary of Suitability for the Best Researcher Award

Yuri Kim is an exceptional researcher in computer science, artificial intelligence, natural language processing (NLP), and recommender systems, demonstrating a strong academic and professional background. Her diverse expertise, high-impact research contributions, and interdisciplinary approach make her a highly suitable candidate for the Best Researcher Award.

🎓 Education

📍 Korea University, Seoul, South Korea
🔹 Ph.D. Candidate in Computer Science (2020.09 – Present)
🔹 🎖️ Recipient of ICT Elite Talent Development Program Scholarship
🔹 🏫 Research Assistant

📍 Eötvös Loránd University, Budapest, Hungary
🔹 B.Sc. & M.Sc. in Computer Science (Integrated Program) (2016.09 – 2019.08)
🔹 📊 GPA: 4.28 / 4.5 (Master’s) | 4.48 / 4.5 (Bachelor’s)
🔹 🎖️ Recipient of Stipendium Hungaricum (Hungarian Government Scholarship)

💼 Work Experience

📍 The Mihalik Group, Chicago, IL (Remote)
🔹 Project Manager (PM) (2023.08 – 2024.07)
🔹 🏗️ Developed in-house business automation software
🔹 📅 Planned & designed project phases, coordinated schedules
🔹 🔍 Managed resources & monitored project performance

📍 Korea University Graduate School of Education, Seoul, South Korea
🔹 Lecturer – Advanced Data Structures (2022.09 – 2023.02)
🔹 🖥️ Delivered lectures on Data Structures & Algorithms
🔹 📝 Designed quizzes, assignments, exams & projects
🔹 📚 Developed instructional materials

📍 Eötvös Loránd University, Budapest, Hungary
🔹 Lecturer – Functional Programming (Clean Language) (2018.02 – 2019.02)
🔹 🏛️ Taught Clean Functional Programming
🔹 ✍️ Designed evaluations, assignments, exams & projects
🔹 📖 Created instructional materials

📍 Ericsson, Budapest, Hungary
🔹 Student Backend Developer (2018.09 – 2019.01)
🔹 ⚙️ Developed & evaluated performance test cases using C
🔹 🛠️ Updated codebase to align with modern standards
🔹 🔍 Conducted code reviews & refactoring

🏆 Awards & Honors

🔹 2024 Korean I-Corps Program 🚀

  • AI-Based Personalized Makeup Consulting
    🔹 2023 Innovation Startup School 🏅
  • AI-Based Celebrity Memorabilia Donation & Auction Platform

🎖️ Achievements

📌 Publications & Research 📚
🔹 Developed a Rule-Based Stock Trading Recommendation System 📈
🔹 Designed a Serendipity-Incorporated Recommender System 🤖
🔹 Implemented a Linked Data Visualization System 🔗
🔹 Proposed methods for Enhancing Linked Open Data (LOD) 🛠️
🔹 Researched Interdisciplinary Applications for Functional Programming 🎨
🔹 Compared Clean vs. C Programming for Education 💻
🔹 Explored Distributed Computation Patterns in Go & RabbitMQ ☁️

📌 Projects 🔍
🔹 SEC 13D/G & 13F Report Tracking Platform (2024.12) 📊
🔹 Text-to-Speech-Based Audiobook Auto-Generation (2024.12) 🎙️
🔹 AI Avatar-Based Motion Slide Auto-Generation (2024.12) 🖼️
🔹 DART Stock Large Shareholder Report Crawling System (2024.10) 📑

Publication Top Notes:

A Rule-Based Stock Trading Recommendation System Using Sentiment Analysis and Technical Indicators

Introduction to programming Using Clean