Prof. Keon Baek | Data analysis | Best Researcher Award

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Keon Baek | Data analysis | Best Researcher Award

Keon Baek | Chosun University | South Korea

Keon Baek is a dedicated Data Scientist and Electrical Engineer based in Gwangju, South Korea 1 🇰🇷. With a strong academic background and practical experience, he focuses on power market analysis, policy design, and technology development through insightful data analysis 📊. His research interests include consumer behavior 💡, demand flexibility 🔄, market and policy implications 🏛️, and the growing field of vehicle electrification 🚗⚡. Keon’s passion lies in leveraging data to shape the future of sustainable energy.

Professional profile : 

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Summary of Suitability : 

Keon Baek, a dedicated Data Scientist and Electrical Engineer from Gwangju, South Korea, is an excellent candidate for the Best Researcher Award. With a robust academic foundation and a wealth of hands-on experience, Keon has demonstrated significant contributions to the fields of power market analysis, policy design, and technology development. His expertise lies in using data to inform decisions around sustainable energy, which aligns perfectly with the award’s criteria for groundbreaking research that drives innovation and societal impact.

Education :

  • Ph.D. (Power System & Economics) – Gwangju Institute of Science and Technology (2020.03 – 2023.02) ⚡💰
  • M.S. (Power System & Economics) – Gwangju Institute of Science and Technology (2018.03 – 2020.02) 💡📈
  • B.S. (Electrical Engineering) – Korea Advanced Institute of Science and Technology (2004.03 – 2011.02) ⚙️🔌

Experience :

  • Assistant Professor, Dept. of Electrical Engineering – Chosun University (2023. 09 – 2023. 08) 👨‍🏫💡
  • Post-doc., Research Institute for Solar and Sustainable Energies (RISE) – Gwangju Institute of Science and Technology (2023. 02 – 2023.08) ☀️🌱
  • Electric Engineer, Distribution Transformer Division – Hyundai (2017. 04 – 2018. 07) 🏭⚡
  • Engineer, Offshore Plant Engineering Center – Korea Shipbuilding & Offshore Engineering (2015. 02 – 2017. 03) 🚢🌊
  • Associate Researcher, Wind Power System Research Center – Korea Shipbuilding & Offshore Engineering (2011. 02 – 2015. 01)
  • Publication Top NOTES :
    Resident Behavior Detection Model for Environment Responsive Demand Response :

    • Authors: K. Baek, E. Lee, J. Kim

    • Published in: IEEE Transactions on Smart Grid, 2021, Vol. 12, Issue 5, Pages 3980-3989

    • Citations: 35

    • Summary: This paper proposes a model for detecting resident behavior in smart grid environments, aiming to optimize demand response (DR) mechanisms. The approach focuses on adjusting electricity usage patterns by predicting and responding to residents’ behavior, enhancing both energy efficiency and grid reliability. This model is crucial for increasing the responsiveness and flexibility of demand response programs in residential areas.

    Evaluation of Demand Response Potential Flexibility in the Industry Based on a Data-Driven Approach :

    • Authors: E. Lee, K. Baek, J. Kim

    • Published in: Energies, 2020, Vol. 13, Issue 23, Article 6355

    • Citations: 28

    • Summary: This study assesses the potential flexibility of demand response programs in industrial settings using a data-driven approach. It evaluates how various industrial processes can be adjusted to provide flexibility in energy consumption without negatively impacting production efficiency. The research also explores the use of real-time data to enhance decision-making in demand response strategies, enabling more effective integration of renewable energy sources.

    Multi-Objective Optimization of Home Appliances and Electric Vehicles Considering Customer’s Benefits and Offsite Shared Photovoltaic Curtailment :

    • Authors: Y. Kwon, T. Kim, K. Baek, J. Kim

    • Published in: Energies, 2020, Vol. 13, Issue 11, Article 2852

    • Citations: 22

    • Summary: This paper discusses a multi-objective optimization approach for managing home appliances and electric vehicles (EVs) while considering customer benefits and photovoltaic (PV) energy curtailment. It focuses on maximizing the benefits to consumers by coordinating the use of home appliances and EVs with the availability of solar energy while reducing the waste of excess PV power. The study is significant for improving the efficiency of residential energy management systems.

    Stochastic Optimization-Based Hosting Capacity Estimation with Volatile Net Load Deviation in Distribution Grids : 

    • Authors: Y. Cho, E. Lee, K. Baek, J. Kim

    • Published in: Applied Energy, 2023, Vol. 341, Article 121075

    • Citations: 13

    • Summary: The research proposes a stochastic optimization method to estimate hosting capacity in distribution grids, accounting for the volatile nature of net load deviation. The study addresses challenges related to integrating renewable energy sources, such as solar and wind, into existing power grids. It develops a model that quantifies the grid’s capacity to absorb additional renewable energy without compromising stability, providing valuable insights for grid operators managing increasing renewable penetration.

    Datasets on South Korean Manufacturing Factories’ Electricity Consumption and Demand Response Participation :

    • Authors: E. Lee, K. Baek, J. Kim

    • Summary: This dataset publication presents detailed information on electricity consumption patterns and the participation of South Korean manufacturing factories in demand response programs. It provides real-world data that can be used to evaluate the effectiveness of demand response strategies and analyze consumption behaviors in industrial sectors. Researchers and energy managers can leverage this dataset to optimize industrial demand response programs and improve grid reliability.

Prof. Dr. Lei Geng | Data Analysis | Best Researcher Award

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Prof. Dr. Lei Geng | Data Analysis | Best Researcher Award

Prof. Dr. Lei Geng, Tiangong University, China

Prof. Dr. Lei Geng is a distinguished professor at the School of Life Sciences, Tiangong University, with a focus on computer vision, machine learning, and measurement technology. He received his Ph.D. in 2012 from Tianjin University and has since made significant contributions to the fields of AI, machine vision, and medical technology. With over 80 published papers, Dr. Geng has played a pivotal role in the development of advanced imaging and measurement technologies for industrial and medical applications. His research includes applications in image analysis, 3D dimensional measurement, and hemostatic medical equipment. As a leader in his field, he has led more than 10 national and provincial-level projects and received numerous awards for his technological innovations. 🚀

Professional Profile:

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Suitability for the Award

Prof. Dr. Lei Geng is highly suitable for the Best Researcher Award due to his groundbreaking work in AI, machine vision, and medical technology. His research has led to the development of advanced image analysis techniques and high-precision measurement tools, with far-reaching implications for both industrial and healthcare applications. Dr. Geng’s leadership in national and provincial projects, combined with his three provincial-level awards, highlights his ability to drive technological advancements that have a direct impact on society. His contributions to AI-based diagnostics, particularly in otolaryngology, underscore his dedication to improving healthcare through cutting-edge technologies. Prof. Geng’s consistent excellence in research, innovation, and application makes him an ideal candidate for this prestigious award. 🏅

Education

🎓 Dr. Lei Geng earned his Ph.D. in 2012 from Tianjin University, specializing in areas at the intersection of computer vision, machine learning, and measurement technology. His academic journey laid the foundation for his extensive contributions to these fields, including the development of cutting-edge applications in industrial and medical sectors. Dr. Geng’s deep understanding of both theoretical and practical aspects of machine vision and artificial intelligence has made him an expert in creating innovative solutions across multiple industries. His education has fueled his ongoing research and contributions to advancements in AI-driven healthcare and precision measurement technologies. 📘

Experience

🧑‍🏫 Prof. Dr. Lei Geng has extensive teaching and research experience, currently serving as a professor at the School of Life Sciences at Tiangong University. He has been involved in both undergraduate and postgraduate education, teaching courses such as Machine Vision and Deep Learning. Over his career, Dr. Geng has undertaken more than 10 national, provincial, and ministerial-level projects, focusing on industrial and medical applications of machine vision and AI. His experience includes pioneering work in hemostatic medical equipment and high-precision 2D/3D measurement systems. This broad range of expertise positions Dr. Geng as a leader in his field, particularly in the integration of AI technologies with practical, real-world applications. 🌍

Awards and Honors

🏅 Dr. Lei Geng’s excellence in research and technological innovation has been recognized through several prestigious awards. He has received three provincial-level awards, including the Tianjin Second Prize for Technological Invention and the Special Prize of the National Award for Business Science and Technology Progress. These accolades are a testament to his significant contributions to the fields of AI, computer vision, and medical technology. Dr. Geng’s ability to bridge the gap between advanced scientific research and practical applications in industries such as healthcare and manufacturing has made him a highly respected figure in the scientific community. 🌟

Research Focus

🔬 Dr. Lei Geng’s research focuses on four key areas:

  1. Image Analysis & Understanding: Developing AI-based systems for image classification, object detection, and segmentation for industrial and medical applications.
  2. Dimensional Measurement: Applying machine vision and 3D scanning technology for high-precision industrial measurement and target positioning.
  3. Hemostatic Medical Equipment: Innovating in extracorporeal compression and intravascular interventional devices for medical bleeding control.
  4. AI in Otorhinolaryngology: Applying deep learning for disease diagnosis in ear, nose, and throat (ENT) medicine.

His work in these areas aims to integrate AI and machine vision to solve real-world problems, particularly in medical diagnostics and industrial automation. 💡

Publication Top Notes:

  • Direct May Not Be the Best: An Incremental Evolution View of Pose Generation
    • Year: 2024
    • Citations: 1
  • Multi-parametric investigations on the effects of vascular disrupting agents based on a platform of chorioallantoic membrane of chick embryos
    • Year: 2024
  • Label-Aware Dual Graph Neural Networks for Multi-Label Fundus Image Classification
    • Year: 2024
  • Cross-scale contrastive triplet networks for graph representation learning
    • Year: 2024
    • Citations: 4
  • Objective rating method for fabric pilling based on LSNet network
    • Year: 2024
    • Citations: 3

Prof Dr. Ahmed Ghezal | Stochastic Volatility Modeling | Excellence in Research

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Prof Dr. Ahmed Ghezal | Stochastic Volatility Modeling | Excellence in Research

Prof Dr. Ahmed Ghezal, Department of Mathematics, Abdelhafid Boussouf University Center of Mila, Algeria

Ahmed Ghezal, born on September 21, 1987, in Ain M’lila, Algeria, is a Teaching Researcher holding a University Habilitation in Applied Statistics (May 2018) from USTHB and a Ph.D. in Mathematics specializing in Applied Statistics (May 2015) from UMC. He also earned a Master’s Degree in Mathematics in Probability and Statistics (January 2012) and a Bachelor’s Degree in Mathematics with a focus on Probability and Statistics (June 2009) from UMC. Ahmed has been an Associate Professor Class A at Abdelhafid Boussouf University Center-Mila since May 2018, focusing on studying the probabilistic and statistical properties of linear and nonlinear time series models. His research interests encompass causality and invertibility, autocorrelation structure, higher-order moments, estimation methods, and asymptotic properties of estimators in time series models. 📊

Professional Profile:

Google Scholar

🎓 Education:

Completed his University Habilitation in Applied Statistics from USTHB (May 2018), focusing on Markovian and periodic regime change time series models. Prior to this, he earned a Ph.D. in Mathematics with a specialization in Applied Statistics from UMC (May 2015), where his research delved into asymptotic statistics in Markov-switching bilinear regime change models. He also holds a Master’s Degree in Probability and Statistics (2012) and a Bachelor’s Degree in Mathematics from UMC.

👨‍🏫 Professional Experience:

Currently serving as Associate Professor Class A at Abdelhafid Boussouf University Center-Mila since 2018, and previously as Associate Professor Class B from 2015. He started his academic career as an Assistant Professor at the same university in 2012. Ahmed’s secondary education took place at Chihani Bachir High School in Teleghma, Mila.

📚 Teaching:

Ahmed has taught a variety of courses including Introduction to Random Processes, Ordinary Differential Equations, Inferential Statistics, Analytical Mathematics, Descriptive Statistics, and Complex Analysis.

🔍 Research Area:

His research focuses on the probabilistic and statistical properties of linear and nonlinear time series models. He explores topics such as causality and invertibility, autocorrelation structure, higher-order moments, estimation methods, and asymptotic properties of estimators.

Publication Top Notes: