Taek Lee, Ph.D.

AI-Driven Predictive Analytics

For Software, Security, and Digital Healthcare

Transforming complex data into actionable insights through machine learning, behavioral analytics, and intelligent prediction.

Assistant Professor
Division of Computer Science and Engineering
Sun Moon University, South Korea

Research Vision

My research focuses on applying artificial intelligence, machine learning, and data analytics to understand complex systems and human behavioral patterns.

I am particularly interested in developing predictive models that identify potential risks, discover meaningful patterns, and support data-driven decision-making.

My work spans three application domains: Software Engineering, Cybersecurity, and Digital Healthcare, connected by a common methodological foundation in machine learning, behavioral data analytics, and risk prediction.

My long-term goal is to develop reliable, interpretable, and practical AI-driven solutions that improve software quality, system security, and human well-being.

Featured Publications

Selected publications highlighting my research in machine learning, predictive analytics, software engineering, cybersecurity, and digital healthcare.

Digital Healthcare

Smartphone-Based Digital Phenotyping for Identifying Elevated Depressive Symptom Levels Using Machine Learning

Taek Lee, Kihoon Choi, and Heon-Jeong Lee
Applied Sciences, 2026.

This study investigates smartphone-based behavioral features for identifying elevated depressive symptom risk and examines the challenges of generalizing machine learning models to unseen individuals.

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Software Engineering

Developer Micro Interaction Metrics for Software Defect Prediction

IEEE Transactions on Software Engineering, 2016.

Investigating developer interaction data as a source of predictive information for identifying software defects and improving software quality.

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Cybersecurity

Risk Prediction of Malicious Code-Infected Websites

Exploring predictive approaches to assessing website security risks using vulnerability-related information.

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Current Research Interests

My current research interests focus on advancing machine learning and predictive analytics for real-world applications, with particular attention to model reliability, interpretability, and practical deployment.

Machine Learning & Predictive Analytics

Developing robust predictive models for complex and heterogeneous real-world data.

Explainable and Trustworthy AI

Understanding model predictions and improving the reliability, transparency, and generalizability of AI systems.

Privacy-Aware Data Analytics

Exploring approaches to analyzing sensitive behavioral and organizational data while considering privacy and data protection requirements.

AI-Assisted Software and Security Analysis

Investigating the potential of AI-driven methods for software quality assessment, vulnerability analysis, and security testing.

About Me

I am Taek Lee, an Assistant Professor in the Division of Computer Science and Engineering at Sun Moon University, South Korea.

I received my Ph.D. and M.S. degrees in Computer Science from Korea University and my B.S. degree in Computer Science from Hannam University.

Before joining Sun Moon University, I served as an Assistant Professor in the Department of Convergence Security Engineering at Sungshin Women's University.

My research interests include machine learning, data mining, predictive analytics, software engineering, cybersecurity, and digital healthcare.

Through interdisciplinary research, I aim to bridge artificial intelligence and practical problems involving software systems, security risks, and human behavioral data.