My research focuses on machine learning, with an emphasis on developing data-driven models for understanding and analyzing complex systems. I am particularly interested in learning paradigms such as supervised, unsupervised, and reinforcement learning, as well as the design and evaluation of algorithms for classification, regression, and clustering tasks. My work also explores deep learning methods, including neural network architectures such as convolutional and recurrent networks, as well as emerging approaches like transformers. In addition, I am interested in time series modeling and its applications in real-world data analysis. From a methodological perspective, I focus on data preprocessing, model evaluation, and the mathematical foundations of machine learning, including probability, statistics, and optimization. More broadly, I am interested in applied machine learning problems in areas such as computer vision, intelligent systems, and data-driven decision support.
