Vaibhav Dixit
I’m a graduate student in Computational Science and Engineering at MIT. I work at the Julia Lab within MIT’s CSAIL, focusing on scientific machine learning and non-linear optimization.
Within the SciML organisation, I serve as member of the steering council and a maintainer for various packages, such as Optimization.jl and GlobalSensitivity.jl among others, focused on non-linear optimization, inverse problems and sensitivity analysis in the context of scientific machine learning. My passion for scientific machine learning and optimization continues to drive my research and development efforts.
Session
Two years ago, back in 2022, our Optimization.jl package took off. It's not just about the 600+ stars it's earned – it's how it's become a go-to tool for many users. What makes it stand out? It's the mix of solver options and automatic differentiation it offers, makes it a breeze to experiment with, more so than any other Julia package or those in other languages. Plus, we've been busy improving the interface, adding cool new features that make using it an even better experience.