Prof Dr. Chia-Yen Lee | Environmental Sensors Award | Best Researcher Award

Prof Dr. Chia-Yen Lee | Environmental Sensors Award | Best Researcher Award 

Prof Dr. Chia-Yen Lee, National Pingtung University of Science and Technology, Taiwan

Prof. Chia-Yen Lee is a distinguished academic in the field of Mechanical Engineering with a focus on micro-sensors, micro-electro-mechanical systems (MEMS), HVAC systems, and indoor environment monitoring. He completed his B.S. and M.S. degrees in Mechanical Engineering at National Taiwan University, Taipei, Taiwan, in 1991 and 1993, respectively. He earned his Ph.D. in Engineering Science from National Cheng Kung University, Tainan, Taiwan, in 2004. Prof. Lee has held significant academic positions at National Pingtung University of Science and Technology (NPUST), where he served as a Professor from August 2010 to July 2021 and as a Distinguished Professor from August 2018. Prior to his tenure at NPUST, he was an Associate Professor at Da-Yeh University and a Visiting Scholar at the California Institute of Technology in 2007. His professional career also includes roles in industry as a Section Head at DiCon Fiberoptics, Inc., and senior engineering positions at TECO Electric and Machinery Co., Ltd.

Professional Profile:

 

Summary of Suitability for the Best Researcher Award:

  • Professor Lee’s research expertise includes micro-sensors, MEMS technology, and indoor environment monitoring. His recent work involves the development of infrared sensors based on ZnO thin films, MEMS-based pyroelectric infrared sensors, and Hall sensor arrays for magnetic field mapping.

Education:

🎓 B.S. in Mechanical Engineering
Department of Mechanical Engineering, National Taiwan University, Taipei, Taiwan (1987-1991)

🎓 M.S. in Mechanical Engineering
Department of Mechanical Engineering, National Taiwan University, Taipei, Taiwan (1991-1993)

🎓 Ph.D. in Engineering Science
Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan (2001-2004)

Professional History:

👨‍🏫 Distinguished Professor
National Pingtung University of Science and Technology, Taiwan (Aug 2018 – Jul 2021)

👨‍🏫 Professor
Department of Materials Engineering, National Pingtung University of Science and Technology, Taiwan (Aug 2010 – Jul 2018)

👩‍🏫 Associate Professor
Department of Materials Engineering, National Pingtung University of Science and Technology, Taiwan (Aug 2008 – Jul 2010)

👩‍🏫 Associate Professor
Department of Mechanical and Automation Engineering, Da-Yeh University, Taiwan (Aug 2007 – Jul 2008)

🌍 Visiting Scholar
Department of Electrical Engineering, California Institute of Technology, CA, U.S.A. (Jul 2007 – Aug 2007)

👨‍🏫 Assistant Professor
Department of Mechanical and Automation Engineering, Da-Yeh University, Taiwan (Aug 2004 – Jul 2007)

Publication top Notes:

Positioning System of Infrared Sensors Based on ZnO Thin Film

Positioning System of Infrared Sensors Based on ZnO Thin Film

Effect of Substrate-Thickness on Voltage Responsivity of MEMS-Based ZnO Pyroelectric Infrared Sensors

Design and Application of MEMS-Based Hall Sensor Array for Magnetic Field Mapping

Best Machine Learning for Sensing

Introduction Best Machine Learning for Sensing

Welcome to the ‘Best Machine Learning for Sensing‘ award, honoring innovative solutions that leverage machine learning to advance sensing technologies. This award recognizes outstanding contributions in developing algorithms, models, and systems that enhance sensing capabilities across various domains.

About the Award:
  • Eligibility: Open to individuals, teams, academic institutions, and organizations worldwide.
  • Age Limits: None.
  • Qualification: Projects or research work showcasing the application of machine learning in sensing technologies.
  • Publications: Relevant publications or patents are encouraged but not required.
  • Requirements: Submissions must demonstrate innovative use of machine learning in sensing, with clear impact and potential for advancement in the field.
Evaluation Criteria:
  • Innovation: Uniqueness and originality of the approach.
  • Impact: Significance and relevance of the work in advancing sensing technologies.
  • Technical Merit: Soundness of the methodology and technical rigor.
  • Applicability: Potential for practical application and scalability.
  • Presentation: Clarity, organization, and effectiveness of the submission.
Submission Guidelines:
  • Submissions should include a detailed description of the project or research work.
  • Supplementary materials such as videos, code repositories, and datasets are welcome.
  • All submissions must be in English.
Recognition:
  • The winner will receive a prestigious award and recognition at a special ceremony.
  • Winners and finalists will be featured on our website and in press releases.
Community Impact:
  • Projects with demonstrated positive impact on society or the environment will be highly regarded.
  • Community engagement and collaboration will be considered favorably.
Biography:
  • Provide a brief biography highlighting the key contributors and their roles in the project.
Abstract:
  • A concise summary of the project, highlighting its significance and key findings.
Supporting Files:
  • Upload any relevant files such as research papers, presentations, or supplementary materials.