Projects
Code is on GitHub.
Ongoing
Learned Proximal Networks Python
Learning proximal operators directly with input-convex neural networks, and using the learned prior for reconstruction in inverse problems.
Learned Proximal Networks for High-Dimensional Hamilton–Jacobi PDEs Python
Building on Fang, Buchanan and Sulam (ICLR 2024). The method learns a convex potential whose gradient is the proximal operator, then recovers the prior either by inverting that gradient per query or by fitting a second network — the latter matching a fully tuned inversion baseline without ever inverting.
Other Projects
Nonlinear Optimization MATLAB
Implementations of line-search and trust-region methods for smooth nonlinear optimization — steepest descent, modified Newton, truncated Newton-CG, Cauchy-point, SR1, and Moré–Sorensen solvers — benchmarked on seven test problems with convergence plots, per-iteration logs, and a written report.
Scientific Computing in Python Python
Numerical methods across ordinary differential equations, numerical linear algebra, data assimilation, data-driven optimization, and financial mathematics.