C++ Software Developer LTE L1 at YADRO
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4G eNodeB Development using AVX and C++ for real-time telecommunications components
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4G eNodeB Development using AVX and C++ for real-time telecommunications components
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End-to-end generation of content for children with GUI and TUI support using Python and PyTorch
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Collection of Unsupervised Feature Selection algorithms and experiments used in recent publications
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Fine-tuning SmolLM2 and Phi-3 on GSM8K using complexity-based curriculum learning, achieving 1.37% accuracy improvement on difficult problems
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Implementing quantization and structural pruning for BLIP-2 and LLaVA, achieving 50% model size reduction with less than 2% accuracy loss
Published in Electrotechnical University "LETI" Bachelor Thesis, 2023
Bachelor thesis focusing on traffic sign recognition using deep learning approaches for autonomous driving applications.
Published in Institute of Electronics and Information Engineers (IEIE), 2024
This survey reviews and classifies object detection methods for autonomous driving, highlights state-of-the-art approaches, and presents a taxonomy diagram to capture current research trends and challenges, particularly in ensuring reliability under adverse weather conditions.
Recommended citation: Khairulov, Timur, Sanghyuck Lee, and Jaesung Lee. "Review of current approaches in the area of object detection for autonomous vehicles." 대한전자공학회 학술대회 (2024): 2836-2839.
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Published in IEEE International Conference on Consumer Electronics (ICCE), 2025
We present an approach that leverages natural language processing and diffusion-based generative models to automatically create music videos for children’s songs from lyrics, with experiments on 20 prompts showing that the Cascade SD model outperforms four alternatives across multiple evaluation metrics.
Recommended citation: Lee, Sanghyuck, Timur Khairulov, and Jaesung Lee. "Diffusion Model-Based Generative Pipeline for Children Song Video." 2025 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2025.
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Published in MDPI Symmetry, 2025
A neural network–based missing value imputation method that incrementally builds a cumulative feature set during training, avoiding reliance on naively imputed data and achieving superior performance across 25 benchmark datasets compared to conventional methods.
Recommended citation: Seo, Wangduk, et al. "ChainImputer: A Neural Network-Based Iterative Imputation Method Using Cumulative Features." Symmetry 17.6 (2025): 869.
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Published in Chung-Ang University Master Thesis, 2025
Master thesis exploring advanced methods for automated feature selection using joint entropy maximization and pattern discrimination techniques.
Undergraduate research assistance, Electrotechnical University 'LETI', Department of Computer Science, 2022
Assisted professor in assembling and launching Russia’s largest Robotarium facility. Gained hands-on experience with multi-robot systems, hardware integration, and remote laboratory infrastructure.
Graduate course, Chung-Ang University, Department of Artificial Intelligence, 2025
Teaching Assistant for Computer Vision course at Chung-Ang University. Prepared course materials, designed examinations, and supported students in understanding advanced computer vision concepts.