Chris Rackauckas
where else to find them
what they talk about
where they keep going back
- PyData Global3 editions2020–2024
on a stage with
never shared a talk
who talks about the same things
- Artem Pelenitsyn5 talks
- Jeff Bezanson6 talks
- Jacob Laurel4 talks
- Fredrik Kjolstad14 talks
- Andrew Drakeford6 talks
- Viral B Shah9 talks
| title | conference | date |
|---|---|---|
| Open Source Component-Based Modeling with ModelingToolkit | PyData Global 2024 | 2024-12-03 |
ModelingToolkit composes equation-based components, performs symbolic structural simplification and generates Julia code that plugs into fast… | ||
| how compiler smarts can help improve the performance of numerical methods | PyData Global 2023 | 2023-12-06 |
A general solver called NonlinearSolve.jl integrates symbolic passes and compiler analyses to auto-generate Jacobians, adjoints, and sparsity so users need not… | ||
| Accelerating Differential Equations In R and Python | PyData Global 2020 | 2020-11-11 |
Claims real speedups: the Julia-backed diffeqpy and R wrapper diffeqr give concrete benchmarks (50–100× vs SciPy, 350× vs deSolve, a 175× Pfizer case) and… | ||
| Generalized Physics-Informed Learning through Language-Wide Differentiable Programming | AAAI-MLPS 2020 | 2020-03-23 |
Language-wide automatic differentiation for full Julia programs generates gradients of real scientific code so existing solvers become trainable: Zygote… | ||
| nothing matches | ||