About

Jina Lee is an Assistant Professor of Sociology at the University of Illinois at Urbana-Champaign. She is a sociologist who studies how seemingly objective evaluation systems reproduce social hierarchies, with a particular focus on gender inequality in science and cultural fields. Her research employs computational text analysis, bibliometric analysis, and experimental methods to investigate whose contributions are recognized as valuable and whose are discounted. In the domain of science, she examines how knowledge claims and their reception are gendered, while in cultural markets, she explores how canonization processes embed gender biases. Across these contexts, her work reveals a consistent pattern: evaluation practices that appear meritocratic embed biases that disadvantage women and lower-status actors. Her research has been published in leading journals such as the American Sociological Review, Poetics, Socius, and the Journal of Social Entrepreneurship, with forthcoming work in Gender & Society. She teaches undergraduate courses in sociology of culture, sociology of gender, social statistics, and technology and society, emphasizing critical thinking and the application of sociological frameworks to contemporary empirical questions.

Research topics

• Computer Science
• Artificial Intelligence
• Sociology
• Political Science
• Knowledge management
• Economics
• Cartography
• Engineering
• Environmental economics
• Programming language
• Human–computer interaction
• Materials science
• Operations research
• Biomedical engineering
• Medicine
• Physics

Selected publications

• A Study on the Conceptual Expansion of Public Design Research through Text Mining

  korea soc pub des · 2025-03-31 · 1 citations

  article

  Public design has been actively discussed in South Korea since the early 2000s, with research expanding significantly after the Public Design Act in 2016. This study analyzes public design research trends from 2006 to 2024, examining 2,050 papers from the Research Information Sharing Service (RISS) using keyword frequency, TF-IDF, and N-gram analysis.Results show that public design research has grown since 2010, shifting from physical environment improvements to service design, urban regeneration, and universal design. TF-IDF analysis highlights these themes, while N-gram analysis reveals increasing connections to policy implementation and practical applications. This study provides empirical insights into public design trends, serving as a foundation for future research and policy development.

  PublisherDOI

• Total phosphorus trends in Mississippi and Atchafalaya River basin watersheds: Exploring the roles of streamflow and watershed features 2000–2020

  The Science of The Total Environment · 2025-08-22 · 4 citations

  article
  PublisherDOI

• Conjugate Addition of Chiral Allenylcopper Species to Vinyl Bis(sulfone) To Access an All-Carbon Quaternary Center

  Organic Letters · 2025-04-02 · 1 citations

  article

  Asymmetric formation of all-carbon quaternary centers has been recognized as a challenging area in organic synthesis. Herein, we report a copper-catalyzed approach toward coupling of two alkenyl substrates, resulting in the formation of a chiral all-carbon quaternary center. In the reaction, axially chiral tetrasubstituted allenylcopper species were generated catalytically and chemoselectively, in situ from the substituted enynes, and remarkably performed as an efficient organometallic nucleophile in conjugate addition to unsaturated sulfones.

  PublisherDOI

• Dynamic polyphenolic profiling of soybean seeds and leaves during developmental stages

  Scientific Reports · 2025-11-10

  articleOpen access

  Soybean (Glycine max L.) is a globally important crop recognized for its high protein content and bioactive compounds, such as flavonoids and isoflavones, which contribute to its nutritional and health benefits. However, the metabolic profiles of soybean leaves at various growth stages remain underexplored. We investigated the polyphenolic (flavonoid and isoflavone) metabolite profiles in 10 genetically diverse soybean varieties across 6 leaf developmental stages and seeds. Sixteen flavonoids and isoflavones were identified through ultra-performance liquid chromatography-mass spectrometry. Data analysis, including principal component analysis, revealed metabolic differences among seed profiles. Partial least squares discriminant analysis (PLS-DA) explored metabolite changes across stages, while orthogonal PLS-DA separated vegetative and reproductive stages. Variable importance in projection scores highlighted naringenin, genistin, and kaempferol as key discriminative metabolites. Heatmap analysis showed flavonoids, particularly kaempferol glycosides, were abundant in the leaves, whereas isoflavones such as daidzin and genistin dominated seeds. Cultivars Hoseo, Sohwang, and Kwangan had the highest kaempferol glycoside levels. Correlating these metabolites with six key polyphenolic biosynthesis genes, chalcone synthase, chalcone isomerase, flavonol synthase, 2-hydroxyisoflavanone dehydratase, isoflavone 7-o-methyl transferase, and isoflavone synthase, revealed variety-specific regulation. This study provides key insights into developmental metabolite dynamics in soybeans and supports strategies to enhance soybean nutritional quality.

  PublisherOA PDFDOI

• Temporal metabolomics of pea seedlings reveals primary and secondary metabolism dynamics under varying light intensity

  Chemical and Biological Technologies in Agriculture · 2025-07-11 · 2 citations

  articleOpen access1st authorCorresponding

  Carbon metabolism in plants involves biochemical processes such as photosynthesis, respiration, and carbon partitioning. This study aimed to elucidate the physiological and biochemical dynamics during the early development of pea seedlings (Pisum sativum) across five time points (6, 9, 12, 15, and 18 days) and five cultivars (Dacheong, Daehyeop 1ho, Sanghyeop, Sacheol, and Cheongmi) using semi-targeted metabolic profiling. Furthermore, we investigated the interplay between photosynthetic activity and secondary metabolite accumulation by profiling the metabolic responses of Dacheong seedlings exposed to varying photosynthetic photon flux densities (PPFDs). A total of 83 metabolites were identified. Multivariate analysis revealed similar biochemical dynamics among the five cultivars during the early seedling stage. Metabolic shifts occurred in three distinct phases: (1) an early nitrogen-rich storage metabolism phase characterized by the accumulation of asparagine and raffinose, (2) an intermediate phase marked by the accumulation of branched-chain amino acids, flavonoids, and carotenoids, and (3) a late phase characterized by increased monosaccharides and chlorophylls. These findings suggest that seedling growth relies on the mobilization and conversion of carbohydrates stored in seeds prior to the development of photosynthetic organs. Notably, significant correlations were observed between the photosynthetic pigments and secondary metabolites. Among the cultivars, Dacheong (12 days) exhibited the highest total flavonoid content (33.40 ± 1.29 mg/g). The metabolic changes in Dacheong seedlings under varying PPFD conditions indicated that higher light intensity enhanced sucrose synthesis and chloroplast component composition. Additionally, carotenoid and flavonoid levels peaking at PPFD 400 µmol/m2·s suggested that this light intensity is the optimal condition for maximizing secondary metabolite accumulation through enhanced photosynthetic activity. This study is the first to profile the transition from heterotrophic to autotrophic growth in pea seedlings and reveal significant correlations between photosynthetic pigments and secondary metabolites during the seedling period. These findings provide insights into metabolic reprogramming in pea seedlings and inform strategies for enhancing their growth and nutritional quality. Future studies should include hormone analyses to further understand the metabolic transition processes in seedlings.

  PublisherOA PDFDOI

• Nitrate-N trends in Mississippi and Atchafalaya River Basin Watersheds: Exploring correlations of watershed features with nutrient trends components 2000–2020

  The Science of The Total Environment · 2025-03-01 · 6 citations

  articleOpen accessSenior author

  Nutrient reduction strategies in the Mississippi and Atchafalaya River Basin (MARB) have been implemented to attenuate drinking water concerns and hypoxia in the northern Gulf of Mexico. Of all nutrients, nitrate has been identified as the principal cause of Gulf hypoxia, with loads coming disproportionately from the Upper Mississippi River Basin and the Corn Belt region. Identifying long-term changes of riverine nitrate would provide valuable information to evaluate the performance of reduction strategies. The objective of this study was to estimate the flow-normalized (FN) nitrate-N concentration and yield trends for the 2000–2020 period across the MARB at monitoring sites with adequate data. A harmonization and in-depth screening of paired nitrate-N and streamflow datasets resulted in a robust water quality monitoring network of 217 sites. Trends magnitude and likelihood were computed using the Weighted Regression on Time, Discharge, and Season (WRTDS) coupled to a bootstrap test, and trends results were correlated with basin features and initial values of concentrations and yields. The impact of streamflow long-term variations on trends was separated from all other factors through stationary and non-stationary flow normalizations. Results indicated that 59.4 % of the 217 sites had likely decreasing concentration trends, while 27.7 % likely increased, and the remaining 12.9 % had no likely change detected. Reductions in riverine FN nitrate-N were predominant at watersheds dominated by cultivated cropland areas having relatively high FN concentrations and yields in 2000 followed by likely downward trends. For the vast majority of sites, the non-streamflow component was more dominant, but the streamflow component was nonetheless influential. • Flow-normalized (FN) NO 3 -N concentration and yield trends were evaluated at 217 sites. • FN Nitrate-N concentration and yield likely decreased at 59.4 % and 47 % of the sites. • Most cropland basins had likely decreased NO 3 -N concentration and yield trends. • Trend increases caused by streamflow were frequently buffered by non-streamflow reductions. • Factors other than streamflow dominated trends at most sites.

  PublisherDOI

• 3 things to watch as South Koreans head toward the polls following turbulent political period

  2025-05-29

  article1st authorCorresponding
  PublisherDOI

• Metabolomics Reveals Lysinibacillus capsici TT41-Induced Metabolic Shifts Enhancing Drought Stress Tolerance in Kimchi Cabbage (Brassica rapa L. subsp. pekinensis)

  Metabolites · 2024-01-25 · 7 citations

  articleOpen access

  Climate change has increased variable weather patterns that affect plants. To address these issues, we developed a microbial biocontrol agent against drought stress in kimchi cabbage (Brassica rapa L. subsp. pekinensis). We selected three bacterial strains (Leifsonia sp. CS9, Bacillus toyonensis TSJ7, and Lysinibacillus capsici TT41) because they showed a survival rate of up to 50% and good growth rate when treated with 30% PEG 6000. The three strains were treated with kimchi cabbage to confirm their enhanced drought stress resistance under non-watering conditions. Among the three strains, the TT41 treated group showed a significant increase in various plant parameters compared with the negative control on the 7th day. We performed extensive profiling of primary and secondary metabolites from kimchi cabbage and the TT41 strain. Multivariate and pathway analyses revealed that only the TT41 group clustered with the well-watered group and showed almost the same metabolome on the 7th day. When treated with TT41, lactic acid was identified as an indicator metabolite that significantly improved drought stress tolerance. Furthermore, lactic acid treatment effectively induced drought stress tolerance in kimchi cabbage, similar to that achieved with the TT41 strain.

  PublisherOA PDFDOI

• On-Device Smishing Classifier Resistant to Text Evasion Attack

  IEEE Access · 2024-01-01 · 13 citations

  articleOpen access

  Smishing (SMS phishing) is a cybercrime in which criminals send fraudulent messages, including malicious links, to steal the victims’ private data or cause financial losses. The damage caused by smishing has become more severe, particularly with the proliferation of mobile devices. In smishing, a major difficulty faced by victims is discrimination between normal and smishing messages. To resolve this problem, we present an on-device smishing classifier based on a deep-learning model. In real-world scenarios, access to a substantial, authentic dataset is crucial. We trained and evaluated the classifier using real SMS datasets containing approximately 250,000 smishing messages and 950,000 normal messages obtained from victims in Korea. To ensure privacy, the classifier operates solely on mobile devices without externally transmitting any data. It utilizes a lightweight method that does not require significant computing power on mobile devices. We explored several models to determine a suitable model for mobile devices and optimized it using real datasets. Furthermore, our statistical analysis of actual smishing messages revealed that 98% of smishing messages are variants of previously sent messages. To address the prevalence of variant smishing messages, we propose a text evasion attack tool called EVA that is capable of generating pseudo-variant messages from a given message using an adversarial attack approach. We used this tool to evaluate and enhance the robustness of our classifier against various messages. Our classifier exhibited exceptional classification accuracy (0.99) while being lightweight (at 127 kB) and robust against variant smishing messages (attack success rate of 0.41).

  PublisherOA PDFDOI

• Reduction of Multipath Effect in GNSS Positioning by Applying Pseudorange Acceleration as Weight

  Sensors · 2024-10-26 · 5 citations

  articleOpen accessSenior author

  A novel approach is proposed to mitigate the multipath effect that is considered a major source of error in global navigation satellite system (GNSS) positioning in urban areas. We utilize code pseudorange acceleration measurements as a weight in a least squares estimation process. If GNSS signals are reflected off a surrounding surface, they cause large variations in the recorded pseudorange measurement. Accelerations computed at each epoch with three consecutive pseudoranges exhibit significant fluctuations in a multipath signal. As a result, positioning accuracy improved by 75% horizontally and 79% vertically compared to not applying any weight. Even when multipath errors exist, the range acceleration (RA) value is sometimes low at many epochs. When a threshold value for the signal-to-noise ratio was additionally applied besides RA, the positioning accuracy at two test sites (including a deep urban environment) improved by more than 80% in both horizontal and vertical directions.

  PublisherOA PDFDOI

Frequent coauthors

• Christopher M. Navarro

  University of Illinois Urbana-Champaign

  39 shared

• Luigi Marini

  37 shared

• Rob Kooper

  University of Illinois Urbana-Champaign

  36 shared

• James D. Myers

  22 shared

• Kenton McHenry

  National Center for Supercomputing Applications

  22 shared

• Barbara Minsker

  Southern Methodist University

  19 shared

• Chris Navarro

  National Center for Supercomputing Applications

  16 shared

• Richard Marciano

  16 shared

Education

• Ph.D., Urban & Regional Planning

  University of Illinois

  2005

• Mater of Urban Planning, Urban and Regional Planning

  University of Illinois

  1999

• Bachelor of Science, Urban Planning and Engineering

  Yonsei University

  1997