Dr. Liu Gaohua, Tianjin University, China

Gaohua Liu is a dedicated engineer and researcher at the School of Electronic and Information Engineering at Tianjin University, where she has been engaged in teaching and motion recognition research since March 2013. She earned her Master of Engineering degree in Electromagnetic Field and Microwave from Tianjin University in 2013, where her thesis focused on downlink logical channel design and algorithm research in LTE under the supervision of Prof. HanSong Su. Prior to that, she completed her Bachelor of Engineering in Communication Engineering at Qingdao University of Science & Technology in 2010. Currently, Gaohua is pursuing her Ph.D. in Information and Communication Engineering, specializing in motion recognition based on multimodal signals, under the guidance of Prof. Jie Jin. Her contributions to the field have been recognized with several awards, including the “Shen-Zhikang Award” for outstanding young teachers at Tianjin University in June 2019 and a National First Prize in the fifth “Dingyang Cup” National Electrical and Electronic Teaching Case Design Competition in May 2018.

Suitability for the Best Researcher Award

Gaohua Liu holds a Master’s degree in Electromagnetic Field and Microwave from Tianjin University, where she conducted significant research on LTE downlink logical channel design. Her foundational education in Communication Engineering from Qingdao University of Science & Technology further solidifies her expertise in the field.

🎓 Education

2010-2013: M.E. in Electromagnetic Field and Microwave

Institution: Tianjin University, Tianjin, China
Thesis Title: Downlink Logical Channel Design and Algorithm Research in LTE
Supervisor: Prof. HanSong Su

2006-2010: B.E. in Communication Engineering

Institution: Qingdao University of Science & Technology, Qingdao, China

💼 Work Experience

03/2013 – Present: Engineer

Department: School of Electronic and Information Engineering, Tianjin University
Focus: Teaching and Motion Recognition research

09/2018 – Present: Ph.D. Candidate

Research Topic: Motion Recognition Based on Multimodal Signals
Supervisor: Prof. Jie Jin
Institution: Tianjin University, Tianjin, China

Publication Top Notes:

Improved encoder-decoder temporal action detection algorithm

Improved human action recognition algorithm based on two-stream faster region convolutional neural network

Algorithm for student behavior detection based on neural network

Improved class room face recognition algorithm based on insightface and its application

Classroom face detection algorithm based on convolutional neural network

Dr. Sangyeop Lee | Signal Processing | Best Researcher Award

Dr. Sangyeop Lee, LG Electronics, South Korea

Sangyeop Lee, Ph.D., is a seasoned Senior Researcher and Data Scientist at LG Electronics, currently based at the Life Data Fusion Laboratory within the B2B Advanced Technology Center in Seoul, Republic of Korea. With a robust academic background, including a Ph.D. in Computer Science from Yonsei University, Sangyeop has been actively involved in both research and academia. His research interests span various domains, notably including LLM fine-tuning, artificial neural networks for biomedical signal processing, and context-awareness in the clinical domain using machine learning techniques. Throughout his career, he has contributed significantly to cutting-edge projects such as Smartcare in Kindergarten and neptuNE, addressing critical issues like child behavior monitoring and home healthcare. Sangyeop’s expertise extends to teaching and mentoring, evident from his engagements as a lecturer and teaching assistant at Yonsei University. His dedication to advancing technology and solving real-world problems underscores his commitment to innovation in the fields of data science and healthcare.

Orcid

Affiliation:

Sangyeop is currently affiliated with the LEAD technology task at the Life Data Fusion Laboratory within the B2B Advanced Technology Center at LG Electronics, located in Seocho R&D Campus, Seoul, Republic of Korea.

Research Interests:

His research interests include LLM fine-tuning, artificial neural networks for biomedical signal processing, and context-awareness using machine learning techniques in clinical settings.

Teaching Experience:

Sangyeop has contributed to education as a lecturer and teaching assistant at Yonsei University, covering subjects like AI for Medical Problems and Engineering Information Processing, where he taught Python practice.

Projects:

Smartcare in Kindergarten: Collaborated with DNX Kidsnote and Severance Hospital to utilize AI technology in studying children’s behavior and location in kindergartens using wearables/radars.
neptuNE: Developed sensors and mobile devices for home monitoring, addressing nocturnal enuresis in children, in collaboration with Samsung Electronics and Severance Hospital.
Ready-Made Implant: Conducted a confidential study on mass production with pre-made implants and recommending customized implant models through dental data analysis, in collaboration with Ostem Implant and Yonsei University.

Publications:

Sangyeop has several publications in prestigious conferences and journals, including IEEE Radar Conference and Sensors, focusing on topics like artificial intelligence, biomedical engineering, and healthcare.

Application:

Sangyeop has contributed to the development of in-home monitoring with wearables and NE Diary Application, enhancing healthcare solutions through technology.

Sangyeop’s dedication to advancing data-driven solutions in healthcare underscores his commitment to innovation and improving patient outcomes. 🌟

Dr. Liu Gaohua | Signal Processing Awards | Best Researcher Award

Dr. Liu Gaohua | Signal Processing Awards | Best Researcher Award

Dr. Liu Gaohua, Tianjin University, China

Professional Profile:

Suitability for the Best Researcher Award

🎓 Education

💼 Work Experience

🏆 Awards and Honors

Publication Top Notes:

Improved encoder-decoder temporal action detection algorithm

Improved human action recognition algorithm based on two-stream faster region convolutional neural network

Algorithm for student behavior detection based on neural network

Improved class room face recognition algorithm based on insightface and its application

Classroom face detection algorithm based on convolutional neural network