About

Meichun Liu is an assistant professor of Industrial Design at the University of Washington's School of Art + Art History + Design. She is a scholar, international award-winning designer, and entrepreneur whose work explores the boundaries between mass production and handcraft, tangible and digital, individual and systemic. Her research includes integrating craft materials and processes with digital technologies to enhance sensory experiences and interactions, as well as developing biomedical products and wearable devices such as EEG helmets and smart contact lenses in collaboration with biomedical engineers. Her current research investigates how technological artifacts mediate human experience and interaction with the world at different scales. This includes studying the persuasive effects of digitally enhanced artifacts on human behavior and decision-making, as well as employing techniques like agent-based modeling and simulation to analyze emergent properties driven by design interventions in complex adaptive systems. Liu's background includes a PhD in Design from North Carolina State University, a Master of Design from the University of Alberta, and a bachelor's degree in Industrial Design from National Cheng Kung University. She has received numerous awards for her design and innovation work, including iF, Red Dot, G-Mark, and Franz awards, as well as a Fulbright Grant for her doctoral studies. Prior to her current position, Liu served as an Associate Professor and Department Chair of Creative Product Design at Asia University in Taiwan, where she received the Excellent Teaching Award in 2016. She also founded and directed Wolkeland Design, collaborating with craftspeople to develop designs using various materials and modern technology, many of which have been exhibited and mass-produced. Early in her career, she worked with IT companies such as Gigabyte Technology and IPEVO, leading product development projects from concept to market. Her work consistently bridges traditional craft and modern technology, contributing to innovative design solutions and advancing research in systemic and behavioral design.

Research topics

• Genetics
• Organic chemistry
• Nanotechnology
• Chromatography
• Materials science
• Chemistry
• Biology
• Cancer research

Selected publications

• Discovering Nominal Constructions Through Behavioral Profiles: The Case of Akan

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Ultrasensitive, Rapid, and Incubation-Free Surface-Enhanced Raman Scattering Detection of Sulfasalazine with a GF@PDDA-Ag/rGO Functionalized Membrane and Machine Learning

  Analytical Chemistry · 2026-04-27

  article1st author

  Developing rapid detection methods with high sensitivity and real-time capabilities is a critical challenge for monitoring antibiotic pollution. This study presents a flexible surface-enhanced Raman scattering (SERS) sensing substrate (GF@PDDA-Ag/rGO) based on the poly dimethyl diallyl ammonium chloride (PDDA)-bridged assembly for real-time, ultrasensitive sulfasalazine detection. PDDA provided a positively charged polyelectrolyte layer, which directs AgNPs to assemble uniformly into hotspots, while reduced graphene oxide (rGO) enhances charge transfer and concentrates target molecules near these hotspots. The synergistic interaction among PDDA, Ag, and rGO endows this composite substrate with excellent performance. This is the first application of SERS for sulfasalazine detection, achieving a lowest detection concentration as low as 10–11 M, significantly outperforming other detection methods. No preprocessing or incubation is required, and the measurement time is within 1 s, enabling a highly sensitive response. In addition, this substrate demonstrated excellent uniformity (relative standard deviation, RSD = 2.67%) and long-term stability (>60 days). Furthermore, the addition of the Random Forest Regression (RFR) model for concentration prediction enhances both accuracy and convenience, with an R2 value of 0.9852. This efficient detection strategy has significant potential for on-site monitoring of antibiotic pollutants in food and environmental samples.

  PublisherDOI

• Discovering Nominal Constructions Through Behavioral Profiles: The Case of Akan

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Health Governance during the COVID-19 Pandemic

  2025-10-01

  book-chapterOpen access1st authorCorresponding

  Taiwan’s government was quick to act against COVID-19 with a quarantine decree, mask mandates, detailed tracking of confirmed cases, and the nearly 100% coverage rate of universal health insurance. This approach proved successful during 2020. However, at the same time, Taiwan’s ‘guest worker policy’ deprives migrant workers of the right to permanent residency/citizenship, freedom of employment, social security benefits, and residency arrangements. They lack access to health care and financial support, and they have faced wage cuts and dismissals during the COVID-19 pandemic, making them a vulnerable minority. This chapter argues that Taiwan needs to reconsider its ‘divide and exclude’ guest worker policy, not only for the sake of migrant workers themselves but also to safeguard public health.

  PublisherOA PDFDOI

• Chinese Verb Frames in Primary Education: From Basic Communication to Cognitive Complexity

  International Journal of Applied Linguistics · 2025-05-11

  articleOpen access

  ABSTRACT This article investigates text complexity in Chinese‐language textbooks for primary school students (Grades 1 to 6) in Hong Kong. Our analysis, based on verb frames in Mandarin VerbNet, shows a developmental shift in the linguistic input in first language (L1) education: Students begin with a focus on core frame elements in lower grades and progress to a greater emphasis on non‐core frame elements in higher grades. This shift reflects increasing linguistic complexity and cognitive demands. Our study highlights the need for curricula that evolve alongside students’ linguistic and cognitive development, offering new insights into Chinese language education and pedagogy.

  PublisherDOI

• High-precision and wide-range feature wavelength quantitative analysis (FWQA) method for fluorescent substances based on IFE-induced CDRS

  Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · 2025-08-15

  article
  PublisherDOI

• Exploring Semantic Variations of Happiness-Denoting Near-Synonyms with Behavioral Profiles

  Lecture notes in computer science · 2025-01-01

  book-chapterSenior author
  PublisherDOI

• A novel real-time detection SERS method for rapid detection of marine nanoplastics via size-dependent combination analysis of Au@Ag-Polystyrene

  Marine Pollution Bulletin · 2025-11-26 · 1 citations

  article
  PublisherDOI

• PROMOTING INCLUSIVE LEARNING FOR CROSS-CULTURAL DESIGN COLLABORATIONS

  2025-01-01

  articleOpen access1st authorCorresponding

  The imperative of creating inclusive learning environments remains a central focus in education, particularly within cross-cultural collaborations. While there is a broad consensus on its importance, approaches to achieving this goal vary across disciplines and contexts. This paper adopts a mixed-methods approach—including metacognitive surveys, field observations, and interviews—to explore the factors that contribute to fostering inclusive learning experiences in cross-cultural design collaborations among students from diverse backgrounds. Our research centers on international design workshops with students from universities in the USA and Taiwan, where participants collaborate to create product-service systems that emphasize sustainability, cultural inspiration, and targeted markets. Insights from these workshops reveal that, while cultural and language differences present significant challenges, these barriers can be mitigated through carefully structured instruction, clear communication of expectations, and the use of artifacts to aid design development. Moreover, design methods incorporating non-verbal and asynchronous communication strategies, such as the nominal group technique, effectively promote inclusivity, particularly for non-native speakers. The selection of topics and settings that encourage knowledge sharing and experiential exchange between students from different cultures further enhances the relevance, accessibility, and meaningfulness of the design and learning experiences. Although these findings are drawn from international design collaborations, we contend that similar strategies can benefit design education beyond the context of international workshops. We advocate for the broader application of these approaches and principles to foster inclusive and equitable learning environments across various design education settings and to create product-service systems that meet diverse cultural needs.

  PublisherOA PDFDOI

• SpecRecFormer: Deep Learning-Driven Adaptive Component Identification of PAH Mixtures Based on Single-Component Raman Spectra

  Analytical Chemistry · 2025-04-29 · 5 citations

  article

  The identification of components in mixed spectra is a fundamental challenge in spectral analysis, complicated by factors such as spectral peak overlap due to structural similarities, shifts in characteristic peaks from molecular interactions, and interferences caused by matrix effects. While deep learning offers robust feature extraction capabilities and notable advantages in addressing these challenges, it still faces significant obstacles, including the limited availability of labeled spectral data for effective training and the difficulty of applying fixed-threshold predictive models to spectra containing uncertain components. This paper established a deep learning model, SpecRecFormer, for the rapid identification of individual components in mixed polycyclic aromatic hydrocarbons (PAHs) based on their Raman spectra. The model integrates a dual-channel convolutional neural network (CNN) for local feature extraction with a Transformer module for global representation. It is trained on a reference database composed of single-component spectra, with simulated mixed spectra generated through data augmentation to expand and diversify the training set. This architecture enables the model to evaluate the similarity between unknown mixed spectra and known single-component references. To further enhance recognition accuracy, an adaptive threshold strategy is introduced, dynamically adjusting decision thresholds based on spectral characteristics to retain only components exceeding the threshold as candidate predictions. Experimental results demonstrate that with training data derived from only four single-component reference spectra, the model generalizes effectively to three real-world PAH data sets, achieving accuracies of 93.75%, 89.21%, and 93.63%, respectively, significantly outperforming conventional neural network models. These findings present an innovative and highly effective approach to mixed spectral analysis, with substantial potential for advancing applications in environmental science and chemical analysis.

  PublisherDOI

Frequent coauthors

• Chu‐Ren Huang

  Hong Kong Polytechnic University

  2215 shared

• Kathleen Ahrens

  Hong Kong Polytechnic University

  2211 shared

• Jin Wang

  2210 shared

• Qi Su

  Xiamen University of Technology

  2210 shared

• Mengxiang Wang

  Central South University

  2209 shared

• Jin Peng

  2209 shared

• Junping Zhang

  Fudan University

  2209 shared

• Maofu Liu

  Wuhan University of Science and Technology

  2209 shared

Labs

• School of Art + Art History + Design, University of WashingtonPI

Education

• PhD in Linguistics, Faculty of Linguistics

  University of Colorado Boulder

  1993

Awards & honors

• Over a dozen design and innovation awards, including iF, Red…
• Graduate Study Fulbright Grant