Personal profile
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 Industrial Design Department 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.
Professional Experience
Professor Yoon Lee worked as a researcher at Hyundai WIA’s R&D Center within Hyundai Motor Group, where she led the mass production of the iTROL+ machine tool controller's hardware and software design, filed design patents, and won the PIN UP DESIGN AWARD. Following this, she taught undergraduate and master’s students as a coach and mentor in courses such as Bachelor Seminar, Software Project, and Research Project at Delft University of Technology. She also served as a Technological Consultant at Nanyang Technological University, where she conducted design research on interactive feedback systems. After obtaining her Ph.D., she worked as a postdoctoral researcher at the University of Oulu, where she participated in the Hybrid Intelligence project, developing behavior-based AI models by integrating human and machine intelligence using cognitive engineering indicators. She also coordinated the Data Forum at the University of Oulu, leading discussions on AI and data-driven interaction design using multimodal data. Additionally, she served as a lecturer at Aalen University in Germany, teaching the course "Digital Transformation and Industry 4.0" for the fourth industrial era. She is currently an Assistant Professor in the Graduate School of Design at University of Seoul, specializing in Industrial Design.
Major Research Achievements
*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)
* Publications
[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)
-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)
* Publications
[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)