Dongsoo Yang
Korea University · 情報科学
研究室紹介
Professor Dongsoo Yang's research lab specializes in the application of advanced control theory and machine learning to real-world engineering and financial systems. The lab focuses on stochastic optimal control, reinforcement learning, and approximate dynamic programming for solving complex decision-making problems under uncertainty. Key research directions include constrained index tracking in finance, adaptive power management in hybrid energy systems, and intelligent control of autonomous vehicles. The lab integrates theoretical rigor with practical implementation, often combining control theory with modern AI techniques such as natural gradient methods and evolution strategies.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
6Recently, the constrained index tracking problem, in which the task of trading a set of stocks is performed so as to closely follow an index value under some constraints, has often been considered as an important application domain for control theory. Because this problem can be conveniently viewed and formulated as an optimal decision-making problem in a highly uncertain and stochastic environment, approaches based on stochastic optimal control methods are particularly pertinent. Since stochast
최근들어, 확률론적 최적제어를 포함한 제어이론과 각종 기계학습 기반 인공지능 방법론은 금융공학 분야의 주요 도구로 자리를 잡아 가고 있다. 본 논문에서는 평균회귀 현상을 보이는 시장을 위한 페어 트레이딩 전략 분야와 추세 추종형 트레이딩 전략 분야에 대해 확률론적 최적제어 이론을 활용한 최신 논문 몇 편을 간단히 살펴보고, 보다 융통성 있고 접근성이 좋은 도구를 확보하기 위하여 확률론적 최적제어이론과 기계학습 기법을 동시에 응용하는 전략을 고려한다. 예시를 위하여 실시한 시뮬레이션은 본 논문에서 고려한 전략이 실제 금융시장 데이터를 대상으로 적용될 때 고무적인 결과를 제공할 수 있음을 보여준다. Recently, control theory including stochastic optimal control and various machine-learning-based artificial intelligence methods have become major tools in the fie
Recently, reinforcement learning and evolution strategy have become major tools in the field of machine learning, and have shown excellent performance in various engineering problems. In particular, the Natural Actor-Critic (NAC) approach and the Natural Evolution Strategies (NES) have led to considerable interests in the area of natural-gradient-based machine learning methods with many successful applications. In this paper, we apply the NAC and the NES to pathtracking control problems for auto
Recently, the optimization of power flows in portable hybrid power-supply systems (HPSSs) has become an important issue with the advent of a variety of mobile systems and hybrid energy technologies. In this paper, a control strategy is considered for dynamically managing power flows in portable HPSSs employing batteries and supercapacitors. Our dynamic power management strategy utilizes the concept of approximate dynamic programming (ADP). ADP methods are important tools in the fields of stochas
Recently, the constrained index tracking problem, in which the task of trading a set of stocks is performed so as to closely follow an index value under some constraints, has often been considered as an important application domain for control theory. Because this problem can be conveniently viewed and formulated as an optimal decision-making problem in a highly uncertain and stochastic environment, approaches based on stochastic optimal control methods are particularly pertinent. Since stochast