My research lies in the intersection areas of Human-Computer Interaction, Cyber-Physical Systems, and AI Technologies. My research goal is to create sustainable, scalable and intelligent ubiquitous computing systems that can facilitate human interactions, enhance well-being and promote environmental responsiveness on a large scale. Unlike most approaches that add layers of technology onto existing structures, my approach is integrating computing technology directly into everyday materials (e.g. textile and wood), offering bottom-up solutions to facilitate the creation of smart objects and environments. In addition to it, I have also worked on other topics such as wearable technologies, text entry systems and Human-AI interactions in VR/AR. My research is generally published in top HCI venues like CHI and UIST, while I also contribute to top AI conferences such as ICLR. My research has attracted considerable public interests via Internet News (e.g. Engadget, Times). Currently, I direct the MakeX Lab at FSU. I am looking for PhD students who are highly motivated and interested in Human-Computer Interaction research. If you're interested in working with me, please send an email to [tw23l (at) fsu (dot) edu] with your CV and a one-page research statement outlining the topic you wish to explore.
Yuning Su, Tingyu Zhang, Jiuen Feng, Yonghao Shi, Xing-Dong Yang, Te-Yen Wu (CHI 2024)
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Tagnoo is a computational plywood augmented with RFID tags, aimed at empowering woodworkers to effortlessly create room-scale smart environments. Unlike existing solutions, Tagnoo does not necessitate technical expertise or disrupt established woodworking routines. This battery-free and cost-effective solution seamlessly integrates computation capabilities into plywood, while preserving its original appearance and functionality.
Yonghao Shi, Chenzheng Li, Yuning Su, Xing-Dong Yang, Te-Yen Wu (CHI 2024)
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WooDowel presents a new approach that enables the woodworker to manually isolate short-circuited electrodes. This method facilitates the creation of sensors using overlapping electrodes, while also incorporating EM shielding, thereby resulting in a substantial improvement in the sensor's robustness when detecting user activities.
Te-Yen Wu , Xing-Dong Yang. (UIST 2022)
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iWood is interactive plywood that can sense vibration based on triboelectric effect. As a material, iWood survives common woodworking operations, such as sawing, screwing, and nailing and can be used to create furniture and artifacts.
Te-Yen Wu , Huizhong Ye, Chi-Jung Lee, Xing-Dong Yang, Bing-Yu Chen, Rong-Hao Liang(DIS 2022)
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This paper presents an investigation of body-centric interactions between the NFC device users and their surroundings, and an accessible method for fabricating fexible, extensible, and scalable NFC extenders on clothing pieces, and an easy-to-use toolkit for facilitating designers to realize the interactive experiences.
Te-Yen Wu , Zheer Xu, Xing-Dong Yang, Steve Hodges, Teddy Seyed(CHI 2021)
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Project Tasca presents a pocket-based textile sensor that detects user input and recognizes everyday objects usually carried in the pockets of a pair of pants (e.g., keys, coins, electronic devices, or plastic items). By creating a new fabric-based sensor capable of detecting in-pocket touch and pressure, and recognizing metallic, non-metallic, and tagged objects inside the pocket, we enable a rich variety of subtle, eyes-free, and always-available input, as well as context-driven interactions in wearable scenarios
Te-Yen Wu , Lu Tan, Yuji Zhang, Teddy Seyed, Xing-Dong Yang (UIST 2020)
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Capacitivo is a contact-based object recognition technique developed for interactive fabrics, using capacitive sensing. Unlike prior work that has focused on metallic objects, our technique recognizes non-metallic objects such as food, different types of fruits, liquids, and other types of objects that are often found around a home or in a workplace.
Pin-Sung Ku, Qijia Shao, Te-Yen Wu , Jun Gong, Ziyan Zhu, Xia Zhou, Xing-Dong Yang (CHI 2020)
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We propose a new sensing technique for one-dimensional touch input workable on an interactive thread of less than 0.4 mm thick. Our technique locates up to two touches using impedance sensing with a spacing resolution unachievable by the existing methods.
Pin-Sung Ku, Jun Gong, Te-Yen Wu , YiXin Wei, Yiwen Tang, Barrett Ens, Xing-Dong Yang (CHI 2020)
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This paper explored the possibilities of interaction with ubiquitous zipper-bearing objects, with a focus on opportunities for foreground and background interactions. Based on the findings, we built a self-contained prototype, Zippro that can replace a common zipper slider.
Zheer Xu, Weihao Chen, Dongyang Zhao, Jiehui Luo, Te-Yen Wu , Jun Gong, Sicheng Yin, Jialun Zhai, Xing-Dong Yang (CHI 2020)
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We present a bimanual text input method on a miniature fingertip keyboard, that invisibly resides on the first segment of a user’s index finger on both hands.
Zheer Xu, Pui Chung Wong, Jun Gong, Te-Yen Wu , Aditya Shekhar Nittala, Xiaojun Bi, Jurgen Steimle, Hongbo Fu, Kening Zhu, Xing-Dong Yang (UIST 2019)
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Best Paper Award
In this paper, we propose and investigate a new text entry technique using micro thumb-tip gestures. Our technique features a miniature QWERTY keyboard residing invisibly on the first segment of the user’s index finger. Text entry can be carried out using the thumb-tip to tap the tip of the index finger.
Ruei-Che Chang, Wen-Ping Wang, Chi-Huan Chiang, Te-Yen Wu , Zheer Xu, Justin Luo, Bing-Yu Chen, Xing-Dong Yang (CHI 2021)
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In this paper, we propose the designs for low cost and 3D-printable add-on components to adapt existing breadboards, circuit components and electronics tools for blind or low vision (BLV) users.
Josh Urban Davis, Te-Yen Wu , Bo Shi, Hanyi Lui, Anthina Panotopoulou, Emily Whiting, Xing-Dong Yang (CHI 2020)
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Honorable Mention Award
We present a novel haptic and audio feedback device that allows blind and visually impaired (BVI) users to understand circuit diagrams. TangibleCircuits allows users to interact with a 3D printed tangible model of a circuit which provides audio tutorial directions while being touched.
Te-Yen Wu , Jun Gong, Teddy Seyed, Xing-Dong Yang (UIST 2019)
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In this paper, we propose blending the virtual and physical worlds for prototyping circuits using physical proxies. With physical proxies, real-world components (e.g. a motor, or light sensor) can be used with a virtual counterpart for a circuit designed in software.
Te-Yen Wu , Hao-Ping Shen, Yu-Chian Wu, Yu-An Chen, Pin-Sung Ku, Ming-Wei Hsu, Jun-You Liu, Yu-Chih Lin, Mike Y. Chen (UIST 2017)
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We present CurrentViz, a system that can sense and visualize the electric current flowing through a circuit, which helps users quickly understand otherwise invisible circuit behavior.
Te-Yen Wu , Bryan Wang, Jiun-Yu Lee, Hao-Ping Shen, Yu-Chian Wu, Yu-An Chen, Pin-Sung Ku, Ming-Wei Hsu, Yu-Chih Lin, Mike Y. Chen (UIST 2017)
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CircuitSense is a system that automatically recognizes the wires and electronic components placed on breadboards.
Chiuan Wang, Hsuan-Ming Yeh, Bryan Wang, Te-Yen Wu , Hsin-Ruey Tsai, Rong-Hao Liang, Yi-Ping Hung, Mike Y. Chen (UIST 2016)
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CircuitStack is a system that com- bines the flexibility of breadboarding with the correctness of printed circuits, for enabling rapid and extensible circuit con- struction.
Ruibo Liu, Jason Wei, Shixiang Shane Gu, Te-Yen Wu , Soroush Vosoughi, Claire Cui, Denny Zhou, Andrew M. Dai (ICLR 2023)
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We present Mind’s Eye, a paradigm to ground language model reasoning in the physical world. Given a physical reasoning question, we use a computational physics engine (DeepMind’s MuJoCo) to simulate the possible outcomes, and then use the simulation results as part of the input, which enables language models to perform reasoning.
Xuhai Xu, Mengjie Yu, Tanya Jonker, Kashyap Todi, Feiyu Lu, Xun Qian, João Belo, Tianyi Wang, Michelle Li, Aran Mun, Te-Yen Wu , Junxiao Shen, Ting Zhang, Narine Kokhlikyan, Fulton Wang, Paul Sorenson, Sophie Kahyun Kim, Hrvoje Benko (CHI 2023)
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We propose XAIR, a design framework that addresses when, what, and how to provide explanations of AI output in AR. The framework was based on a multi-disciplinary literature review of XAI and HCI research, a large-scale survey probing 500+ end-users’ preferences for AR-based explanations, and three workshops with 12 experts collecting their insights about XAI design in AR.
Yi-Hao Peng, Ming-Wei Hsu, Paul Taele, Ting-Yu Lin, Po-En Lai, Leon Hsu, Tzu-chuan Chen, Te-Yen Wu , Yu-An Chen, Hsien-Hui Tang, Mike Y. Chen (CHI 2018)
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In this paper, we interviewed and co-designed with eight DHH participants to address the following challenges: 1) associating utterances with speakers, 2) ordering utterances from different speakers, 3) displaying optimal content length, and 4) visualizing utterances from out-of-view speakers.
Yu-An Chen, Te-Yen Wu , Tim Chang, Jun You Liu, Yuan-Chang Hsieh, Leon Yulun Hsu, Ming-Wei Hsu, Paul Taele, Neng-Hao Yu, Mike Y. Chen (MobileHCI 2018)
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We present an AR direct-manipulation interface that lets users plan an aerial video by physically moving their mobile devices around a miniature 3D model of the scene, shown via Augmented Reality (AR).
Chi-Jung Lee, Rong-Hao Liang, Ling-Chien Yang, Chi-Huan Chiang, Te-Yen Wu , Bing-Yu Chen (UIST 2022)
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NFCStack is a physical building block system that can support stacking and frictionless interaction based on near-field communication (NFC).
Yu-Chian Wu, Te-Yen Wu , Paul Taele, Bryan Wang, Jun-You Liu, Ping-sung Ku, Po-en Lai, Mike Y. Chen (CHI 2018)
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We present ActiveErgo, the first active approach to improving ergonomics by combining sensing and actuation of motorized furniture. It provides automatic and personalized ergonomics of computer workspaces in accordance to the recommended ergonomics guidelines.
Conference Organinzing Committee: UIST'23, UIST'24
Program/Associate Chairs: CHI'24, UIST'24, CHI'25
Conference Review: CHI'19 - '25, UIST'19 - '24, ISS'20, CSCW'21, TEI'20 - '21, MobileHCI'22
Journal Review: IMWUT'22 - '23, Natural Communications'23
First Year Assistant Professor (FYAP) grant, FSU