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Kunpeng Liu

Assistant Professor

Clemson University

United States

Research Interests

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About

Kunpeng Liu is an Assistant Professor at Portland State University in the United States. His research areas include graph embedding and clustering, user perspectives in MOOC quality evaluation, spatiotemporal data modeling, and machine learning applications in healthcare. He has published recent articles exploring innovative techniques in feature selection and user profiling, contributing to advancements in these domains.

Articles (18)

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2026

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2023

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2022

Collaborators (8)

Khalid Osman

Assistant Professor

Stanford University

UNITED STATES

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Qian Lou

Assistant Professor

University of Central Florida

UNITED STATES

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Wan Du

City University of Hong Kong

HONG KONG

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Zijun Yao

Assistant Professor

University of Kansas

UNITED STATES

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Tamzidul Hoque

University of Kansas

UNITED STATES

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Jian Pei

Duke University

UNITED STATES

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Charles E. Hughes

Professor

University of Central Florida

UNITED STATES

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Huazhen Fang

Michigan State University

UNITED STATES

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Kunpeng Liu

Research Interests

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About

Articles (18)

Deep cut-informed graph embedding and clustering

A Comprehensive Survey on Data Augmentation

Heterogeneous Multi-Agent Reinforcement Learning with Attention for Cooperative and Scalable Feature Transformation

Toward Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation

Topology-aware Reinforcement Feature Space Reconstruction for Graph Data

Understanding User Perspectives for MOOC Quality Evaluation with Hypergraph Learning

Next Arrival and Destination Prediction via Spatiotemporal Embedding with Urban Geography and Human Mobility Data

AI4DE: The 1st International Workshop on AI for Data Editing

Adaptive Context-Infused Performance Evaluator for Iterative Feature Space Optimization

Imputation via Domain Adaptation: Rethinking Variable Subset Forecasting from Knowledge Transfer

Anchor-guided global view reconstruction for multi-view multi-label feature selection

Deep Adaptive Graph Clustering via von Mises-Fisher Distributions

Traceable Group-Wise Self-Optimizing Feature Transformation Learning: A Dual Optimization Perspective

T-SaS: Toward Shift-aware Dynamic Adaptation for Streaming Data

Interactive reinforced feature selection with traverse strategy

Reinforced Imitative Graph Learning for Mobile User Profiling

Reinforced Explainable Knowledge Concept Recommendation in MOOCs

A Simpler Machine Learning Model for Acute Kidney Injury Risk Stratification in Hospitalized Patients

Collaborators (8)

Khalid Osman

Qian Lou

Wan Du

Zijun Yao

Tamzidul Hoque

Jian Pei

Charles E. Hughes

Huazhen Fang

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