Experience

Design and extend stochastic model (Markov-process–based) with computational implementation.
Build and optimize algorithms for numerical experimentation.
Conduct model validation, comparing large-scale computational results against theoretical structure.
Apply machine learning with Python (TensorFlow/Keras) to capture nonlinear relationships and assess predictive performance of constructed model.
Lead seminars and collaborate across teams, communicating results to both technical and non-technical audiences.