Mr. Lin Li | Compressive Sensing Award | Best Innovation Award

Mr. Lin Li | Compressive Sensing Award | Best Innovation Award

Mr. Lin Li, Chengdu University of Technology, China

Li Lin received his M.S. degree in Educational Technology from Sichuan Normal University in Chengdu, China, in 2011. He is currently pursuing a Ph.D. in Earth Exploration and Information Technology at the same institution. His research interests focus on machine learning theory and 3D point cloud processes. From July 2011 to July 2019, Li Lin worked as a Senior Engineer specializing in production design at ThinkGeo (US) Science and Technology Co., Ltd. in Chengdu, Sichuan. With a strong background in computer science, Li Lin continues to contribute to the fields of technology and research.

Professional Profile:

 

Summary of Suitability for Best Innovation Award:

Li Lin’s background and research accomplishments demonstrate significant expertise and innovation in the field of 3D point cloud processes, particularly in machine learning and LiDAR technology. His academic journey, with an M.S. degree in educational technology and current Ph.D. studies in Earth exploration and information technology, shows his commitment to advancing technological solutions in a complex and emerging area. His research focuses on applying machine learning theory to 3D point cloud processing, which is crucial for various applications like geospatial analysis and environmental monitoring.

Education:

  • Master’s Degree in Educational Technology
    • Institution: Sichuan Normal University, Chengdu, China
    • Duration: September 1, 2008, to July 1, 2011
  • Ph.D. in Earth Exploration and Information Technology (Pursuing)
    • Institution: Sichuan Normal University, Chengdu, China
    • Current Status: Ongoing

Work Experience:

  • Senior Engineer (Production Design)
    • Company: ThinkGeo (US) Science and Technology Co., Ltd., Chengdu, Sichuan, China
    • Duration: July 1, 2011, to July 3, 2019

Research Interests:

  • Machine Learning Theory
  • 3D Point Cloud Processes

This outlines Li Lin’s career trajectory and expertise in both education and industry

Publication top Notes:

Compressing and Recovering Short-Range MEMS-Based LiDAR Point Clouds Based on Adaptive Clustered Compressive Sensing and Application to 3D Rock Fragment Surface Point Clouds

 

Assist Prof Dr. Hwa-Dong Liu | Signal Processing | Best Researcher Award

Assist Prof Dr. Hwa-Dong Liu | Signal Processing | Best Researcher Award

Assist Prof Dr. Hwa-Dong Liu, Undergraduate Program of Vehicle and Energy Engineering, National Taiwan Normal University, Taiwan

Hwa-Dong Liu is an Assistant Professor at National Taiwan Normal University (NTNU) in Taipei, Taiwan, specializing in power electronics, microcontrollers, rail vehicle power systems, and solar power systems. He holds a Ph.D. in Electrical Engineering from National Taiwan University of Science and Technology (NTUST). His research interests include the development of advanced power converters, control strategies for renewable energy systems, and innovative solutions for electric vehicle charging. Dr. Liu has authored numerous papers in reputable journals, with a focus on improving the efficiency and performance of power electronic systems and renewable energy technologies. His recent work includes contributions to energy management systems, high-gain boost converters, and novel MPPT algorithms for solar power generation.

Professional Profile:

Summary of Suitability for Best Researcher Award 

Hwa-Dong Liu has expertise in several cutting-edge fields including power electronics, microcontrollers, rail vehicle power systems, and solar power systems. This diversity indicates a broad impact on multiple important areas of research.

Education

  • Ph.D. in Electrical Engineering from National Taiwan University of Science and Technology (NTUST).

Work Experience

  • Assistant Professor at National Taiwan Normal University (NTNU).

Expertise

  1. Power Electronics
  2. Microcontroller
  3. Rail Vehicle Power Systems
  4. Solar Power Systems

Publication top Notes:

An improved solar step-up power converter for next-generation electric vehicle charging

Hybrid Management Strategy for Outsourcing Electromechanical Maintenance and Selecting Contractors in Taipei MRT

An Improved High Gain Continuous Input Current Quadratic Boost Converter for Next-Generation Sustainable Energy Application

Novel MPPT algorithm based on honey bees foraging characteristics for solar power generation systems

High-Voltage Autonomous Current-Fed Push-Pull Converter with Wireless Communication Applied to X-Ray Generation

 

 

 

Mr. Yeonjae Park | Signal Cleaning Award | Best Scholar Award

Mr. Yeonjae Park | Signal Cleaning Award | Best Scholar Award

Mr. Yeonjae Park, The Graduate School of Yonsei University, South Korea

Yeonjae Park is a Master’s student at Yonsei University in the Department of Medical Informatics and Biostatistics, under the guidance of Professor Dae Ryong Kang. With a strong foundation in Computer and Telecommunication Engineering as well as Information and Statistics, Park obtained dual B.S. degrees from Yonsei University, where they were mentored by Professors Cho Young-rae and Na Seongyong. Their research interests span machine learning, deep learning, generative models, multi-modal data analysis, and time series forecasting. Park has gained valuable research experience through various positions, including as a researcher intern at the Artificial Intelligence-Information Retrieval Lab, a researcher at the Applied Data Science Lab, and their current role at the National Health BigData Clinical Research Institute. Their projects encompass a range of topics, from text extraction and OCR recognition to complex analyses in genomics, disease correlations, and the effectiveness of medical treatments.

Professional Profile:

Summary of Suitability for Best Scholar Award:

Yeonjae Park has a strong academic foundation, holding dual Bachelor’s degrees in Computer and Telecommunication Engineering and Information and Statistics from Yonsei University, one of South Korea’s most prestigious institutions. Currently, Yeonjae is pursuing a Master’s degree in Medical Informatics and Biostatistics at the same university, under the guidance of a notable advisor, Dae Ryong Kang.

Education 📚

  • Samseon Middle School, Seoul, Korea (Mar. 2010 ~ Jul. 2010)
  • SungSan Middle School, Seoul, Korea (Jul. 2010 ~ Feb. 2013)
  • Kwangsung High School, Seoul, Korea (Mar. 2013 ~ Feb. 2016)
  • Yonsei University, Department of Computer and Telecommunication Engineering 🖥️ (Mar. 2016 ~ Aug. 2021)
    • B.S. in Computer and Telecommunication Engineering
    • Advisor: Prof. Cho Young-rae
  • Yonsei University, Department of Information and Statistics 📊 (Feb. 2016 ~ Aug. 2021)
    • B.S. in Information and Statistics
    • Advisor: Prof. Na Seongyong
  • Yonsei University, Department of Medical Informatics and Biostatistics 🧬 (Aug. 2021 ~ Present)
    • Master Student
    • Advisor: Prof. Dae Ryong Kang

Research Interests 🔍

  • Machine Learning / Deep Learning 🤖
  • Generative Models 🌀
  • Multi Modal 🧠
  • Time Series Forecasting ⏳

Research Experiences 💼

  • Researcher Intern at Artificial Intelligence-Information Retrieval Lab, Yonsei University, Korea (May. 2019 ~ Apr. 2020)
  • Researcher at Applied Data Science Lab, Yonsei University, Korea (May. 2020 ~ Jan. 2021)
  • Researcher at National Health BigData Clinical Research Institute, Korea (Jan. 2021 ~ Present)

 

Publication top Notes:

Development and Validation of a Real-Time Service Model for Noise Removal and Arrhythmia Classification Using Electrocardiogram Signals

Intracardiac Echocardiogram: Feasibility, Efficacy, and Safety for Guidance of Transcatheter Multiple Atrial Septal Defects Closure