TY - GEN
T1 - Computational Approaches for Understanding, Generating, and Adapting User Interfaces
AU - Jiang, Yue
AU - Lu, Yuwen
AU - Nichols, Jeffrey
AU - Stuerzlinger, Wolfgang
AU - Yu, Chun
AU - Lutteroth, Christof
AU - Li, Yang
AU - Kumar, Ranjitha
AU - Li, Toby Jia Jun
N1 - Yue Jiang is a Ph.D. student in the Department of Visual Computing and Artificial Intelligence supervised by Prof. Christian Theobalt at the Max Planck Institute for Informatics, Germany. Her main research interests are in HCI and Graphics with a focus on adaptive user interfaces and 3D human performance capture. Her recent work with Prof. Wolfgang Stuerzlinger and Prof. Christof Lutteroth focuses on adaptive GUI layout based on OR-Constraints (ORC). Yuwen Lu is a Ph.D. student in the Department of Computer Science and Engineering at the University of Notre Dame, working on using data-driven approaches for understanding and generating user interfaces to support UX research and design work. Prior to joining Notre Dame, Yuwen received a Master’s degree in Human-Computer Interaction from Carnegie Mellon University. Jeffery Nichols is a Research Scientist in the AI/ML group at Apple working on intelligent user interfaces. Previously he was a Staff Research Scientist at Google working on the open-source Fuchsia operating system. His most important academic contribution recently was the creation of the RICO dataset [4]. He also worked on the PUC project [26], whose primary focus was creating a specification language that can define any device and an automatic user interface generator that can create control panels from this specification language. Wolfgang Stuerzlinger is a Professor at the School of Interactive Arts + Technology at Simon Fraser University. His work aims to gain a deeper understanding of and to find innovative solutions for real-world problems. Current research projects include better 3D interaction techniques for Virtual and Augmented Reality applications, new human-in-the-loop systems for big data analysis, the characterization of the effects of technology limitations on human performance, investigations of human behaviors with occasionally failing technologies, user interfaces for versions, scenarios, and alternatives, and new Virtual/Augmented Reality hardware and software. Chun Yu is an Associate Professor at Tsinghua University. He is keen to research computational models and AI algorithms that facilitate the interaction between human and computers. Current research directions include novel sensing and interaction techniques, accessibility and user interface modeling. His research outcome has been integrated into commercial products serving hundreds of millions of users on smart phones, such as a touch sensing algorithm, a software keyboard decoding algorithm, a smart keyboard, and a screen reader for visually impaired people. Christof Lutteroth is a Reader in the Department of Computer Science at the University of Bath. His main research interests are in HCI with a focus on immersive technology, interaction methods, and user interface design. In particular, he has a long-standing interest in methods for user interface layout. He is the director of the REal and Virtual Environments Augmentation Labs (REVEAL), the HCI research centre at the University of Bath. Yang Li is a Senior Staff Research Scientist at Google, and an affiliate faculty member at the University of Washington CSE, focusing on the area intersecting AI and HCI. He pioneered on-device interactive ML on Android by developing impactful product features such as next app prediction and Gesture Search. Yang has extensively published in top venues across both the HCI and ML fields, including CHI, UIST, ICML, ACL, EMNLP, CVPR, NeurIPS (NIPS), ICLR, and KDD, and has constantly served as area chairs or senior area (track) chairs across the fields. Yang is also an editor of the upcoming Springer book on "AI for HCI: A Modern Approach", which is the first thorough treatment of the topic. Ranjitha Kumar is an Associate Professor of Computer Science at the University of Illinois at Urbana-Champaign (UIUC) and the Chief Research Scientist at UserTesting. At UIUC, she runs the Data Driven Design Group, where she and her students leverage data mining and machine learning to address the central challenge of creating good user experiences: tying design decisions to desired outcomes. Her research has won best paper awards/nominations at premier conferences in HCI, and is supported by grants from the NSF, Google, Amazon, and Adobe. She received her BS and PhD from the Computer Science Department at Stanford University, and co-founded Apropose, Inc., a data-driven design startup based on her dissertation work that was backed by Andreessen Horowitz and New Enterprise Associates.
PY - 2022/4/27
Y1 - 2022/4/27
N2 - Computational approaches for user interfaces have been used in adapting interfaces for different modalities, usage scenarios and device form factors, understanding screen semantics for accessibility, task-automation, information extraction, and in assisting interface design. Recent advances in machine learning (ML) have drawn considerable attention across HCI and related fields such as computer vision and natural language processing, leading to new ML-based user interface approaches. Similarly, significant progress has been made with more traditional optimization- and planning-based approaches to accommodate the need for adapting UIs for screens with different sizes, orientations and aspect ratios, and in emerging domains such as VR/AR and 3D interfaces. The proposed workshop seeks to bring together researchers interested in all kinds of computational approaches for user interfaces across different sectors as a community, including those who develop algorithms and models and those who build applications, to discuss common issues including the need for resources, opportunities for new applications, design implications for human-AI interaction in this domain, and practical challenges such as user privacy.
AB - Computational approaches for user interfaces have been used in adapting interfaces for different modalities, usage scenarios and device form factors, understanding screen semantics for accessibility, task-automation, information extraction, and in assisting interface design. Recent advances in machine learning (ML) have drawn considerable attention across HCI and related fields such as computer vision and natural language processing, leading to new ML-based user interface approaches. Similarly, significant progress has been made with more traditional optimization- and planning-based approaches to accommodate the need for adapting UIs for screens with different sizes, orientations and aspect ratios, and in emerging domains such as VR/AR and 3D interfaces. The proposed workshop seeks to bring together researchers interested in all kinds of computational approaches for user interfaces across different sectors as a community, including those who develop algorithms and models and those who build applications, to discuss common issues including the need for resources, opportunities for new applications, design implications for human-AI interaction in this domain, and practical challenges such as user privacy.
KW - adative interfaces
KW - design mining
KW - interface generation
KW - interface semantics
KW - user interfaces
UR - http://www.scopus.com/inward/record.url?scp=85129691824&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85129691824&partnerID=8YFLogxK
U2 - 10.1145/3491101.3504030
DO - 10.1145/3491101.3504030
M3 - Conference contribution
AN - SCOPUS:85129691824
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2022 - Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2022 CHI Conference on Human Factors in Computing Systems, CHI EA 2022
Y2 - 30 April 2022 through 5 May 2022
ER -