Things I’ve been working on (also check out my Github profile):

• ProtoGrad. An experimental Julia package for gradient-based optimization of machine learning models. Essentially, a highly opinionated collection of design ideas of mine, on how deep learning frameworks should work.

• GluonTS. Python toolkit for probabilistic time series modeling, with a focus on deep learning architectures, built around PyTorch.

• ProximalAlgorithms.jl. Generic Julia implementations of first-order optimization algorithms for nonsmooth problems, based on operator splittings: forward-backward (proximal gradient method), Douglas-Rachford (ADMM), primal-dual, and Davis-Yin splitting algorithms. Also contains Newton-type extensions. Based on:

• ProximalOperators.jl. Julia package to compute the proximal operator of several functions commonly used in nonsmooth optimization problems. Useful as building block to implement large-scale optimization algorithms such as ADMM.

• libLBFGS. C library providing the structures and routines to implement the limited-memory BFGS algorithm (L-BFGS) for large-scale smooth unconstrained optimization. Contains a Mex interface to MATLAB.

• Matto. Simple chess player implemented in C. I started this when I was 17 and learning the C programming language, so there’s a lot of room for improvement. Yet it plays!

• podds. Multi-threaded Texas hold ’em poker odds evaluation tool, written in C, command line only.