Qi Yang
· Assistant Professor of Design StudiesUniversity of Wisconsin-Madison · Design Studies
Active 2008–2025
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About
Qi Yang is an Assistant Professor in the Department of Design Studies at the University of Wisconsin–Madison. Their educational background spans Design, Psychology, and Systems Engineering. Qi Yang's research centers around two connected agendas: how people move through ideas and how people move through space. In the mental realm, they investigate metacognition in design processes, studying how people reflect on and guide their own thinking. They design Human AI co-creative systems that augment cognition, encourage curiosity, support long-term skill growth, and help overcome design fixation. This work aims to create tools that are wise, expanding human creativity and learning rather than simply producing outcomes. In the physical environment, Qi Yang studies wayfinding in healthcare and educational settings. Using empirical studies and computational modeling, they examine how spaces shape human wayfinding behavior and cognition, such as perceived uncertainty, with the goal of improving human health and well-being. Their research interests include wayfinding, human-building interaction, human AI interaction, creativity support tools, metacognition, spatial cognition, human behavior modeling, environmental psychology, and research through design.
Research topics
- Chemistry
- Materials science
- Composite material
- Nanotechnology
- Chromatography
- Meteorology
- Engineering
- Photochemistry
- Physics
- Nuclear chemistry
Selected publications
PackVFL: Efficient HE Packing for Vertical Federated Learning
arXiv (Cornell University) · 2024-05-01
preprintOpen accessSenior authorAs an essential tool of secure distributed machine learning, vertical federated learning (VFL) based on homomorphic encryption (HE) suffers from severe efficiency problems due to data inflation and time-consuming operations. To this core, we propose PackVFL, an efficient VFL framework based on packed HE (PackedHE), to accelerate the existing HE-based VFL algorithms. PackVFL packs multiple cleartexts into one ciphertext and supports single-instruction-multiple-data (SIMD)-style parallelism. We fo…
SSRN Electronic Journal · 2024-01-01
preprintOpen access
Recent grants
Frequent coauthors
- 119 shared
Qingwen Tian
- 103 shared
Guigan Fang
State Forestry and Grassland Administration
- 98 shared
Xiang Li
Henan Normal University
- 53 shared
Yawei Zhu
Chinese Academy of Forestry
- 46 shared
Aixiang Pan
Institute of Chemical Industry of Forest Products
- 33 shared
Hang Yin
- 30 shared
T. W. Wu
Chinese Academy of Forestry
- 21 shared
Xingjian Liu
Zhejiang University of Technology
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