Institution profile

Seoul National University

Academic institutionasia · kr
Official website
Research library1,649linked papers
Opportunities0open roles
Selected work

Representative Papers

Chance-Constrained Trajectory Planning With Multimodal Environmental Uncertainty

Mar 09, 2025IEEE Control Systems Letters

This work addresses the safety-critical trajectory planning problem for autonomous driving under multimodal uncertainty in obstacle behavior. Methodologically, it proposes a novel chance-constrained optimization framework based on Gaussian Mixture Models (GMMs), wherein GMMs are explicitly embedded into chance constraints for the first time. Tight concentration bounds are derived via finite-sample statistical inference to guarantee confidence levels, and Conditional Value-at-Risk (CVaR) is innovatively adopted as a risk-averse surrogate to quantify and control constraint violation risk. The resulting formulation is cast as a tractable Mixed-Integer Conic Program (MICO). Extensive experiments on standard trajectory prediction benchmarks and real-world autonomous driving datasets demonstrate that the method significantly improves trajectory safety and computational feasibility in complex uncertain environments, while maintaining theoretical rigor and engineering practicality.

21 citationsRead paper

Examining Augmented Virtuality Impairment Simulation for Mobile App Accessibility Design

May 02, 2019International Conference on Human Factors in Computing Systems

This study addresses the limitations of existing mobile application accessibility evaluation methods for cataract users—namely, perceptual distortion and insufficient empathic engagement. To bridge this gap, we propose an Augmented Virtuality (AV)-driven design support methodology. We introduce Empath-D, the first system to employ AV for real-time, interactive simulation of cataract-induced visual impairment. Empath-D integrates eye-tracking and gesture interaction simulation, on-device real-time rendering, and an accessibility-oriented human factors evaluation framework, enabling embodied understanding of authentic usage challenges. Compared to conventional guideline-based tools, Empath-D increases defect detection count by 37% and improves identification accuracy by 29%. User interviews confirm its effectiveness in significantly enhancing designers’ empathic capacity and grounding design decisions in lived experience. This work establishes a novel application paradigm for AV in accessible human–computer interaction.

14 citationsRead paper

Leveraging Priors via Diffusion Bridge for Time Series Generation

Aug 13, 2024arXiv.org

Standard Gaussian diffusion priors struggle to capture temporal structures, scale sensitivity, and fixed-point constraints inherent in time series. To address this, we propose TimeBridge—a novel framework that systematically introduces data- and time-dependent priors alongside scale-preserving constraint priors, enabling a learnable diffusion bridge mechanism for probabilistic transport from adaptive priors to the target data distribution. TimeBridge unifies unconditional and conditional generation, offering both flexibility and precise controllability. Evaluated on multiple benchmark time-series datasets, it achieves state-of-the-art performance in generation diversity, fidelity, and temporal consistency—significantly outperforming existing diffusion-based baselines.

6 citations2 influentialRead paper

Speaking Without Sound: Multi-speaker Silent Speech Voicing with Facial Inputs Only

Apr 06, 2025IEEE International Conference on Acoustics, Speech, and Signal Processing

This study addresses the challenge of generating multi-speaker speech in the complete absence of audible input. To this end, the authors propose a silent speech synthesis method that fuses facial images with silent electromyography (EMG) signals: facial images are leveraged to match the target speaker’s vocal timbre, while linguistic content is extracted from EMG signals. A key innovation is the introduction of a pitch-disentangled content embedding mechanism, which effectively separates linguistic content from pitch information. This approach significantly enhances the naturalness and expressiveness of synthesized speech in multi-speaker scenarios and represents the first demonstration of high-quality multi-speaker silent speech synthesis using only facial images and EMG signals, thereby validating the efficacy of the proposed pitch-disentanglement strategy.

3 citations1 influentialRead paper

Semi-Autonomous Mathematics Discovery with Gemini: A Case Study on the Erd\H{o}s Problems

Jan 29, 2026

This work proposes a semi-autonomous discovery framework that integrates artificial intelligence with human expertise to investigate 700 mathematical conjectures labeled as “open” in Bloom’s Erdős Problem Database. Leveraging the Gemini large language model for natural language reasoning and automated literature comparison as an initial screening step, candidate solutions are subsequently evaluated by domain experts for correctness and novelty. The study reveals that many problems deemed “open” stem not from intrinsic difficulty but from challenges in literature retrieval—termed “information occlusion.” Among the 13 problems successfully resolved, five yielded novel AI-generated solutions, while eight were traced to previously published results. This research represents the first large-scale demonstration of human–AI collaborative verification in mathematical conjectures and highlights the risk of “unconscious plagiarism” inherent in AI-assisted scholarly discovery.

2 citationsRead paper
Recent publications

Latest Papers