Uladzimir Charniauski

WORK HISTORY

EDUCATION

ABOUT ULADZIMIR CHARNIAUSKI

I am a graduate of University of Connecticut, where I received B.A. in Applied Mathematics with a minor in Computer Science and B.A. in Statistics with a minor in Economics.My research interest is machine learning methods for temporal data. The central theme of my research is to develop novel reinforcement learning (RL) and deep learning (DL) methods and frameworks for operating with temporal data that express long-range dependence, or long memory, across observations. Traditionally, fractionally-integrated time series models (ex. ARFIMA, FIGARCH, etc.) are widely used in modeling applications for data exhibiting long memory across the data entrees. On the other hand, many RL and DL methods often struggle to capture such complex temporal dynamics, posing new statistical and computational challenges. I aim to develop novel learning methods capable of modeling long memory patterns across observations in data, along with the supporting statistical theory to address this fundamental limitation.