Dr. Longbin Jin | Signal Processing Awards | Best Researcher Award

Dr. Longbin Jin | Signal Processing Awards | Best Researcher Awardย 

Dr. Longbin Jin, Konkuk University, South Korea

Longbin Jin, is a Ph.D. candidate in Computer Science at Konkuk University, Korea, with an expected graduation in February 2025. His research focuses on adaptive visual prompting for video action recognition in vision-language models under the guidance of Professor Eun Yi Kim. He holds a Masterโ€™s degree in Smart ICT Convergence and a Bachelorโ€™s degree in Mechanical Engineering & Automation from Shanghai University, China. Throughout his academic career, Longbin has received numerous accolades, including winning the ICASSP 2023 SPGC Challenge and multiple Excellence and Encouragement Prizes at the Korea Software Congress. Currently, he serves as an AI Researcher at Voinosis in Seoul, where he develops AI models for early detection of hearing loss and cognitive impairment in the elderly. He is also an instructor at Konkuk University, teaching courses on Artificial Intelligence, Computer Vision, and Machine Learning. His project experience includes collaborations on medical imaging and virtual reality, demonstrating his expertise in applying AI technologies across diverse fields. Longbin is proficient in English, Chinese, and Korean, reflecting his international background and commitment to advancing technology in healthcare and education.

Professional Profile:

GOOGLE SCHOLAR

Research for Community Impact Award: Longbin Jin’s Suitability

Longbin Jin is a highly qualified candidate for the Research for Community Impact Award due to his significant contributions in the fields of artificial intelligence and healthcare, particularly in projects that directly benefit the community.

๐Ÿ“š Education

  • Ph.D. in Computer Science
    Konkuk University, Korea
    Expected: February 2025
    Thesis: Adaptive Visual Prompting for Video Action Recognition in Vision-Language Models
    Advisor: Prof. Eun Yi Kim
  • M.S. in Smart ICT Convergence
    Konkuk University, Korea
    Graduated: August 2020
    Thesis: E-EmoticonNet: EEG-based Emotion Recognition with Context Information
    Advisor: Prof. Eun Yi Kim
  • B.S. in Mechanical Engineering & Automation
    Shanghai University, China
    Graduated: August 2018

๐Ÿ’ผ Work Experience

  • AI Researcher
    Voinosis, Seoul, Korea
    December 2022 โ€“ Present

    • Researcher on AI models for early detection of hearing loss and cognitive impairment based on voice analysis for the elderly (VoiceCheck & BrainGuardDoctor Apps).
  • Instructor
    Konkuk University, Seoul, Korea
    March 2022 โ€“ Present

    • Teaching courses on Computer Vision, Artificial Intelligence, and Machine Learning.
  • AI Engineer
    Lulla, Seoul, Korea
    October 2022 โ€“ November 2022

    • Main researcher for an AI model for a child face-matching system to assist kindergarten teachers (Lulla App).

๐Ÿ† Achievements, Awards, and Honors

  • Winner of ICASSP 2023 SPGC Challenge: Multilingual Alzheimer’s Dementia Recognition through Spontaneous Speech (First Author) ๐Ÿฅ‡
  • Excellence Prize, Korea Software Congress 2023 ๐Ÿฅ‡
  • Encouragement Prize, ACM Student Research Competition, Computer Human Interaction 2020 (First Author) ๐ŸŽ–๏ธ
  • Excellence Prize, Korea Software Congress 2019 (First Author) ๐Ÿ…
  • Encouragement Prize, Korea Software Congress 2019 (First Author) ๐ŸŽ–๏ธ
  • Excellent Presentation, International Conference on Culture Technology 2018 ๐ŸŒŸ

Publicationย Top Notes:

Interpretable Cross-Subject EEG-Based Emotion Recognition Using Channel-Wise Featuresโ€ 

CITED:29

Consen: Complementary and simultaneous ensemble for alzheimerโ€™s disease detection and mmse score prediction

CITED:15

Eeg-based user identification using channel-wise features

CITED:7

E-EmotiConNet: EEG-based emotion recognition with context information

CITED:2

Emotion Recognition based BCI using Channel-wise Features

CITED:1

 

Kim Bjerge | Signal Processing | Best Researcher Award

Kim Bjerge | Signal Processing | Best Researcher Award

Mr. Kim Bjerge, Aarhus University, Denmark.

Kim Bjerge is an Associate Professor at Aarhus University in the Department of Electrical and Computer Engineering, specializing in Signal Processing and Machine Learning. With a Ph.D. focused on Computer Vision and Deep Learning for Insect Monitoring, Kim combines academic expertise with significant industry experience. He has held various teaching and leadership positions at Aarhus University and has contributed to research projects in computer vision. His work has resulted in a notable H-index of 14 and 1080 citations on Google Scholar. Kim is dedicated to advancing technology in engineering education and research.ย ๐ŸŽ“๐Ÿ’ป๐Ÿ“ˆ

Publication Profilesย 

Googlescholoar

Education and Experience

  • Ph.D. in Computer Vision and Deep Learning for Insect Monitoringย (Aarhus University, 2022 – present)ย ๐Ÿ“š
  • M.Sc. Eng. in Information Technologyย (Aarhus University, 2013)ย ๐Ÿ“–
  • B. Eng. in Electronics Engineeringย (Engineering College of Aarhus, 1989)ย ๐Ÿ”ง
  • Associate Professor and Group Leaderย (Aarhus University, 2021 – present)ย ๐ŸŽ“
  • Associate Professor and Group Leader, Signal Processingย (Aarhus University, 2009 – 2020)ย ๐Ÿ“Š
  • Senior Consultant, IT-Developmentย (Danish Technological Institute, 2007 – 2009)ย ๐Ÿ› ๏ธ
  • Software Development Managerย (TC Electronic A/S, 1999 – 2007)ย ๐ŸŽถ
  • System Developerย (Crisplant A/S, 1996 – 1999)ย ๐Ÿ“ฆ
  • System Managerย (Sam-system A/S, 1989 – 1996)ย ๐Ÿ’ผ

Suitability For The Award

Mr. Kim Bjerge, Associate Professor at Aarhus Universityโ€™s Department of Electrical and Computer Engineering, is an exemplary candidate for theย Best Researcher Awardย due to his outstanding contributions to computer vision, deep learning, and signal processing. With a remarkable career spanning academia and industry, he has made groundbreaking advancements in the fields of artificial intelligence, embedded systems, and digital signal processing, impacting both research and application development globally.

Professional Development

Kim Bjerge has pursued extensive professional development through various programs. He completed the Pedagogical Programme in Engineering at the Center for Engineering Education Research and Development, earning 10 ECTS credits. Additionally, he participated in project management training at Provinu and various management courses at Aarhus Business College, enhancing his skills in human resources, organizational strategy, and software engineering. His commitment to ongoing learning ensures that he remains at the forefront of engineering education and technology.ย ๐Ÿ“š๐Ÿ”ง๐ŸŒฑ

Research Focus

Kim Bjerge’s research focuses on the intersection of computer vision, deep learning, and machine learning, particularly in the context of insect monitoring. His work aims to develop innovative solutions that enhance the understanding and management of ecological systems through advanced image analysis and artificial intelligence techniques. By leveraging his expertise in signal processing, he contributes to the development of cutting-edge technologies that have practical applications in various fields, including agriculture and environmental science.ย ๐ŸŒฑ๐Ÿ”๐Ÿค–

Publication Top Notesย 

  • Deep learning and computer vision will transform entomologyย – Cited by: 362, Year: 2021ย ๐Ÿ“–
  • Towards the fully automated monitoring of ecological communitiesย – Cited by: 141, Year: 2022ย ๐ŸŒฑ
  • An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learningย – Cited by: 119, Year: 2021ย ๐Ÿฆ‹
  • Real-time insect tracking and monitoring with computer vision and deep learningย – Cited by: 110, Year: 2021ย ๐Ÿ“น
  • A computer vision system to monitor the infestation level of Varroa destructor in a honeybee colonyย – Cited by: 85, Year: 2019ย ๐Ÿ
  • Accurate detection and identification of insects from camera trap images with deep learningย – Cited by: 61, Year: 2023ย ๐Ÿ”
  • A living laboratory exploring mobile support for everyday life with diabetesย – Cited by: 40, Year: 2010ย ๐Ÿ“ฑ
  • Hierarchical classification of insects with multitask learning and anomaly detectionย – Cited by: 26, Year: 2023ย ๐Ÿ“Š
  • Enhancing non-technical skills by a multidisciplinary engineering summer schoolย – Cited by: 19, Year: 2017ย ๐ŸŽ“

Prof. Shing-Tai Pan | Signal Processing Awards | Best Researcher Award

Prof. Shing-Tai Pan | Signal Processing Awards | Best Researcher Awardย 

Prof. Shing-Tai Pan, National University of Kaohsiung, Taiwan

Shing-Tai Pan, is a distinguished academic in the field of computer science and engineering. He earned his M.S. degree in Electrical Engineering from National Sun Yat-Sen University, Kaohsiung, Taiwan, in 1992, followed by a Ph.D. from National Chiao Tung University, Hsinchu, Taiwan, in 1996. Since 2006, he has been a Professor in the Department of Computer Science and Information Engineering at the National University of Kaohsiung, Taiwan. Prof. Pan is an active member of several professional organizations, including the Taiwanese Association for Artificial Intelligence (TAAI), the Chinese Automatic Control Society (CACS), and The Association for Computational Linguistics and Chinese Language Processing (ACLCLP). His research interests encompass biomedical signal processing, digital signal processing, speech recognition, evolutionary computations, artificial intelligence applications, and intelligent control system design.

Professional Profile:

SCOPUS

ORCID

Summary of Suitability for the Best Researcher Award: Shing-Tai Pan

Shing-Tai Pan is a distinguished academic and researcher whose extensive contributions to the fields of biomedical signal processing, speech recognition, and artificial intelligence make him a highly suitable candidate for the Best Researcher Award. With a career spanning over two decades, his work reflects innovation, collaboration, and a commitment to advancing technology for societal benefits.

Education

  1. M.S. in Electrical Engineering
    • Institution: National Sun Yat-Sen University, Kaohsiung, Taiwan
    • Year: 1992
  2. Ph.D. in Electrical Engineering
    • Institution: National Chiao Tung University, Hsinchu, Taiwan
    • Year: 1996

Work Experience

  1. Department of Computer Science and Information Engineering
    • Position: Professor
    • Institution: National University of Kaohsiung, Kaohsiung, Taiwan
    • Joined: 2006

Professional Memberships

  • Taiwanese Association for Artificial Intelligence (TAAI)
  • Chinese Automatic Control Society (CACS)
  • The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)

Research Interests

  • Biomedical Signal Processing
  • Digital Signal Processing
  • Speech Recognition
  • Evolutionary Computations
  • Artificial Intelligence Applications
  • Intelligent Control Systems Design

Publication Top Notes:

Fuzzyโ€HMM modeling for emotion detection using electrocardiogram signals

Performance Improvement of Speech Emotion Recognition Systems by Combining 1D CNN and LSTM with Data Augmentation

Editorial for special issue entitled โ€œCACS2020: Applications of emerging intelligent techniques on modeling and control of modern systemsโ€

Editorial for special section โ€œCACS18: Modelling and control for practical systemsโ€

Efficient robust speech recognition with empirical mode decomposition using an FPGA chip with dual core