Postdoc, Vector Institute, Toronto
2 papers at NeurIPS 2025
We introduce Woodbury's matrix identity, momentum-like SPRING and randomization to make energy natural gradient descent 75 times faster for PINNs.
We accelerate Taylor mode for practically relevant differential operators by collapsing Taylor coefficients; this can be done automatically with compute graph simplifications