Emmanuel Adara

My research focuses on the development of efficient computational methods for modeling the stochastic dynamics of biochemical systems. I work with the Chemical Master Equation (CME) and the Reaction-Diffusion Master Equation (RDME) to capture molecular-level fluctuations and spatial dynamics within reaction networks. To address the computational challenges posed by these systems, I employ techniques such as the method of moments combined with maximum entropy reconstruction and adaptive Quantized Tensor Train (QTT) decompositions for managing high-dimensional models. Additionally, I integrate machine learning approaches to emulate stochastic simulations, enabling accurate and reusable predictions of system behavior.