Researcher and Engineer at Harvard University developing scalable simulation frameworks for active biological matter and physics-informed AI systems
Postdoctoral Fellow in Applied Mathematics at Harvard University with PhD in Computer Science (Summa Cum Laude) from International Max Planck Research School. Specializing in scalable 3D simulations of active biological matter, physics-informed AI, and high-performance computing for PDE solvers. I am interested in advancing the frontiers of digital twin technology through innovative simulation frameworks and machine learning techniques.
Scalable 3D simulations of active biological matter using provable Machine Learning techniques, achieving 5x performance improvements in complex geometries.
Distributed computing on heterogeneous architectures with expertise in C++, Python, and GPU acceleration using CUDA.
Reinforcement learning coupled with PDE solvers for control of active fluids, reducing algorithm complexity from O(n³) to O(n²).
Multi-physics modeling of biological systems including neural networks, active nematic droplets, and cytoskeletal materials.
Advancing scalable simulation technologies for humanity.