Junghye Lee

Assistant Professor

Seoul National University

South Korea

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Junghye Lee is an Assistant Professor at Seoul National University, South Korea. Her research focuses on machine learning applications in healthcare, predictive analytics, and time series classification. Recent work includes developing models for high-cost patient prediction and port throughput forecasting, as well as innovations in federated learning and convolutional neural networks.

Junghye Lee

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Articles (19)

Machine learning for disease-specific prediction of high-cost patients

Inter-country trade similarity graph-based long short-term memory for port throughput prediction

A graph convolutional network for time series classification using recurrence plots

An Interpersonal Dynamics Analysis Procedure With Accurate Voice Activity Detection Using Low-Cost Recording Sensors

CAFO: Feature-Centric Explanation on Time Series Classification

HarmoSATE: Harmonized embedding-based self-attentive encoder to improve accuracy of privacy-preserving federated predictive analysis

HarmoSATE: Harmonized embedding-based self-attentive encoder to improve accuracy of privacy-preserving federated predictive analysis

Multichannel convolution neural network for gas mixture classification

A product acceptance decision-making method based on process capability with considering gauge measurement errors

Chemistry-informed machine learning: Using chemical property features to improve gas classification performance

Household financial health: a machine learning approach for data-driven diagnosis and prescription

MMP Net: A feedforward neural network model with sequential inputs for representing continuous multistage manufacturing processes without intermediate outputs

Multitask Deep Learning for Human Activity, Speed, and Body Weight Estimation Using Commercial Smart Insoles

Prediction of type 2 diabetes using genome-wide polygenic risk score and metabolic profiles: A machine learning analysis of population-based 10-year prospective cohort study

Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph

An efficient multivariate feature ranking method for gene selection in high-dimensional microarray data

Secure and Differentially Private Logistic Regression for Horizontally Distributed Data

Markov blanket-based universal feature selection for classification and regression of mixed-type data

Word2vec-based latent semantic analysis (W2V-LSA) for topic modeling: A study on blockchain technology trend analysis

Collaborators (4)

Seulki Lee

Jihun Kang

Yongjae Lee

Miran Kim

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