Projects
What we're building and experimenting with.
Focus Areas
We work across the stack, from numerical methods down to the code generation and systems that make them fast.
Scientific Computing
Numerical methods and simulation for physics and the computational sciences, including Monte Carlo techniques and large-scale numerical workloads that have to run fast enough to be useful.
Machine Learning Infrastructure
The systems layer beneath the models: data and training pipelines, GPU utilization, and the throughput and memory limits that decide how quickly experiments can actually run.
Compilers
How source becomes fast machine code. Intermediate representations, optimization passes, code generation, and understanding compiler behaviour well enough to know when hand-tuning is worth it.
Distributed Systems
Splitting work across many machines with MPI and related tooling, and handling the coordination, communication, and failure behaviour that comes with it.
Performance Engineering
Profiling real workloads to find bottlenecks, then closing them with vectorization, cache-aware tuning, and low-level work in C, C++, and Rust.