Curriculum Vitae
My work sits at the intersection of numerical simulation, data assimilation, scientific machine learning, and software engineering. The consistent objective is to translate advanced methods into reliable computational tools that can support prediction, design, and decision-making.
Experience
Developing digital-twin and large-scale multiphysics simulation frameworks for offshore floating solar energy systems.
Developed reduced-order methods and scientific software for parameterized PDEs, with emphasis on computational efficiency and large-scale use.
Worked on topology optimization software for compliant aerospace mechanisms, designed under real compliance and stress constraints.
Skills
Data science and ML
Clustering, dimensionality reduction, feature extraction, supervised and unsupervised learning, time series, reservoir computing, RNNs, PINNs, and VAEs.
Data assimilation and UQ
Ensemble Kalman filters, 4D-Var, unscented transformations, MLMC, delayed acceptance, and multi-fidelity algorithms.
Simulation and modeling
PDE solvers, finite element methods, reduced-order models, computational fluid and structural mechanics, and optimization-based solvers.
Engineering and compute
MPI, GPU computing, Slurm deployment, profiling, benchmarking, CI/CD, testing, debugging, and reproducible workflows.
Technical interests
Additional details