Nick Korbit

Hi, I'm Nick! I am a PhD student at IMT Lucca, where I work in the DYSCO lab under the supervision of Mario Zanon and Alberto Bemporad. And DYSCO is what I do - studying DYnamical Systems, Control and Optimization, focusing on scalable second-order optimization algorithms for modern-day neural networks.

Prior to starting my PhD, I was working on autonomous delivery robots at Starship Technologies and modeling risk at CompatibL.

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I aim to make neural network training faster, more efficient, and highly scalable. My research focuses on second-order optimization, with a particular emphasis on the Gauss-Newton method and diagonal Hessian approximations.

We propose a stochastic second-order optimization algorithm - EGN - that efficiently computes the descent direction by using a low-rank Gauss-Newton Hessian approximation.

We propose IGND - a scale-invariant, easy-to-tune, fast-converging stochastic optimization algorithm based on approximate second-order information with nearly the same per-iteration complexity as Stochastic Gradient Descent.

As part of my thesis, I developed and currently maintain somax—a second-order complement to optax, a library primarily focused on first-order solvers.

Somax is a library of stochastic second-order methods for machine learning optimization written in JAX. Somax is based on the JAXopt StochasticSolver API, and can be used as a drop-in replacement for JAXopt as well as Optax solvers.

Machine Learning Experiments for Researchers (Winter 2025)
IMT School for Advanced Studies Lucca
Course Co-creator and Instructor
Practical companion to the Machine Learning course by Prof. Alberto Bemporad

Kudos to Jon Barron for this template!