통계학습 및 계산금융 연구실

Industrial & Physical AI systems often rely on sensitive visual, acoustic, behavioral, spatial, and industrial data. We develop privacy-preserving learning and inference methods based on differential privacy, private inference, and secure learning, together with fair AI methods that reduce group-level disparities. We develop mathematical and algorithmic methods for stable representations, robust learning, domain adaptation, and reliable generalization from limited, noisy, or partially labeled data. These foundations support dependable perception, prediction, and decision-making under real-world uncertainty. We develop data-centric methods for heterogeneous data analysis, multimodal integration, ensemble learning, clustering, recommendation, and large-scale data mining. These methods provide the data-engineering foundations for robust Industrial & Physical AI systems. We develop robust perception and state-estimation methods that integrate physical sensors and industrial data, including cameras, radar, LiDAR, acoustic and tactile sensors, process measurements, logs, and time-series signals, to estimate system, environment, and equipment states. Actionable intelligence from financial time series, market networks, macroeconomic signals, investor sentiment and blockchain data, supporting robust analysis of price dynamics, volatility, default risk, market impact and information spillovers under changing market conditions. Financial valuation and asset pricing through mathematical, stochastic, numerical and machine-learning methods, supporting reliable derivative pricing, volatility modeling, model calibration and pricing analysis. Digital Finance aims to advance intelligent and secure financial ecosystems by integrating Artificial intelligence, Blockchain, and Cybersecurity