About me

Education

Professor Yoon Lee is a designer and researcher who approaches AI-based interaction design from both hardware and software perspectives, leveraging interdisciplinary experience in design and engineering. She earned a Bachelor’s degree in Industrial Design from the College of Fine Arts at Hongik University, with a minor in Business Administration from the same university. She then obtained a Master’s degree in Industrial Design Engineering from Delft University of Technology in the Netherlands, followed by a Ph.D. from the Faculty of Electrical Engineering, Mathematics and Computer Science at the same university.

Work experience

Professor Lee Yoon worked as a researcher at Hyundai WIA, a subsidiary of Hyundai Motor Group's Research and Development Headquarters, where she led the mass production of the hardware and software design for the iTROL+ machine tool controller, a national research project. He also applied for design patents and won the PIN UP Design Awards. Afterward, she served as a coach and mentor at Delft University of Technology, where she taught undergraduate and master's students through courses such as Bachelor Seminar, Software Project, and Research Project. Additionally, she worked as a technological consultant at Nanyang Technological University in Singapore, focusing on design research for an interactive feedback system. Following the completion of his Ph.D., she became a postdoctoral researcher at the University of Oulu, where she worked on the Hybrid Intelligence project. This project aimed to develop behavior-based AI models by integrating human intelligence and machine intelligence based on cognitive engineering indicators. She also chaired the Data Forum at the same university, leading discussions on AI and data-driven interaction design using multimodal data. Furthermore, she lectured at Aalen University in Germany, teaching a course on Digital Transformation and Industry 4.0 for the Fourth Industrial Revolution. She is currently an assistant professor of industrial design in the Department of Design at the University of Seoul.

Key Achievements

2023 European Conference on Technology Enhanced Learning Best Paper Award Nominee
2016 PIN UP DESIGN AWARD Best 100 (iTROL+ Bar Type)
2016 PIN UP DESIGN AWARD Finalist (iTROL+ Folder Type)
2014 SK Telecom Smart Appcessory Design Challenge Finalist (Grabeat)
Design Patent: Hyundai WIA Machine Tool Controller iTROL+ Bar Type (30-09075930000)
Design Patent: Hyundai WIA Machine Tool Controller iTROL+ Folder Type (30-09043320000)

Thesis

Lee, Y. (2024). Interactive Intelligence: Multimodal AI for Real-Time Interaction Loop towards Attentive E-Reading.
Lee, Y. (2019). Noise fatigue in the ICU: platform for sound data collection and visualization: Cacophony Mapper.

Journal Papers

Lee, Y., Migut, G., & Specht, M. (2023). What Attention Regulation Behaviors Tell Us About Learners in E-reading?: Adaptive Data-driven Persona Development and Application based on Unsupervised Learning. (IEEE Access, SCIE, IF=3.9)

Conference Papers

Lee, Y., & Specht, M. (2023, March). Can We Empower Attentive E-reading with a Social Robot? An Introductory Study with a Novel Multimodal Dataset and Deep Learning Approaches. In LAK23: 13th International Learning Analytics and Knowledge Conference (LAK, CORE A=excellent)
Lee, Y., Chen, H., Zhao, G., & Specht, M. (2022, November). WEDAR: Webcam-based Attention Analysis via Attention Regulator Behavior Recognition with a Novel E-reading Dataset. In Proceedings of the 2022 International Conference on Multimodal Interaction (ICMI, CORE B=good to very good)
Lee, Y., Limbu, B., Rusak, Z., & Specht, M. (2023, August). Role of Multimodal Learning Systems in Technology-Enhanced Learning (TEL): A Scoping Review. In European Conference on Technology Enhanced Learning (ECTEL, CORE B=good to very good). Best paper nominee
Lee, Y., Migut, G., & Specht, M. (2023). Behavior-based Feedback Loop for Attentive E-reading (BFLAe): A Real-Time Computer Vision Approach. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI, CORE A*=exceptional)
Chen, H., Tan, E., Lee, Y., Praharaj, S., Specht, M., & Zhao, G. (2020, June). Developing AI into explanatory supporting models: An explanation-visualized deep learning prototype. In 14th International Conference of the Learning Sciences: The Interdisciplinarity of the Learning Sciences, ICLS 2020. International Society of the Learning Sciences
Chen, H., Yu, Z., Liu, X., Peng, W., Lee, Y., & Zhao, G. (2020). 2nd place scheme on action recognition track of eccv 2020 vipriors challenges: an efficient optical flow stream guided framework. arXiv preprint arXiv:2008.03996
Lee, Y., Chen, H., Tan, E., Praharaj, S., & Specht, M. (2020). FLOWer: Feedback Loop for Group Work Supporter. In The International Learning Analytics and Knowledge Conference (LAK demo session, CORE A=excellent)