Personal Project. Built with Python and numpy. MLflow is a computation graph executor for machine learning implemented in Python. It has built in automatic differentiation and optimizers. You can implement, train, and test your neural networks in MLflow. The models can also be serialized for later use. See the project on Github.

School team project, built with C++, Python and tvm. The dependency engine is built to automatically schedule operations in a computation graph. The engine works with both the declarative style and imperative style of building neural networks. It provides a high level interface in Python and implements multithreading in C++. See the project on Github.

Personal project. Built with Python and Numpy. Implementing a neural network without the help of a library is one undertaking one should go through at least once in their lifetime… here are my attempts. See the project on Github.

Side team project. Built with Arduino and C#. Mark V is an Ironman-like VR flying experience, enhanced with physical wind feedbacks using four arrays of compact fans. My role focuses on the hardware hacks with Arduino: coding the Arduino to communicate with VR while controlling the fans, and setting up the external frame.