Integrated upstream changes across the ML stack
Helped deliver updated Arm CPU framework builds by combining upstream work across PyTorch, oneDNN, Arm Compute Library, and OpenBLAS into shippable tooling.
Experience
See journeySelected impact
Improved model throughput on real workloads
Investigated PyTorch → oneDNN → Arm Compute execution paths and enabled missing dispatch routes to resolve practical bottlenecks.
Reduced cycle time for engineering teams
Improved build systems and contributed upstream changes that delivered notable CI speedups (including ~2.7× improvements in targeted flows).
Restored external Docker delivery
Worked through compliance requirements and automated repetitive release checks to re-enable external image publishing.
Research & Publications
I completed my PhD in applied mathematics at the University of Manchester, with a focus on fluid dynamics and the numerical solution of time-periodic solutions to partial differential equations (otherwise known as PDEs).
For modest Reynolds numbers (Re ≤ 100), a fixed cylinder sheds vortices in a classical 2S pattern, known widely as the Kármán vortex street. When the cylinder oscillates with a period close to the natural shedding frequency, increasing the oscillation amplitude leads to a transition to a different, asymmetric wake pattern (the P+S pattern). A central question of the thesis was whether this transition arises through a continuous (topological) evolution of the flow or via bifurcations of the Navier–Stokes equations.
ORCID: 0000-0001-9359-9814