I am a researcher in Stochastic Modeling and Data Science, with a solid academic background in Mathematics and Statistics. I am currently pursuing a Ph.D. in Statistical Methods for Dynamical Stochastic Models at the University of Porto, where I investigate online parameter and state estimation in partially observed hypoelliptic stochastic systems, combining tools from stochastic calculus, filtering theory, and simulation-based inference. I have experience in statistical modeling, time series analysis, stochastic differential equations, numerical optimization, and university teaching in Mathematics and Statistics. Research & Professional Interests: • Stochastic Modeling & Applied Statistics – Probabilistic methods, stochastic differential equations, simulation, and inference for Stochastic Differential Equations. • Online Inference & State Estimation – Expectation-Maximization (EM), stochastic gradient algorithms, and linear and nonlinear Filtering for partially observed systems. • Scientific Programming – R, Python, with focus on computational efficiency and reproducibility. •Systems Optimization and Statistical Process Control) • Data Visualization & Communication – Use of R, ggplot2, Shiny, and Power BI to translate complex models into actionable insights. • University Teaching & Mentoring; What Drives Me? I am passionate about applying mathematical and statistical modeling — in synergy with artificial intelligence — to understand and solve real-world problems in fields such as engineering, environmental science, and healthcare, with a strong focus on inference for stochastic dynamical systems, university education, and data-informed decision support.