I am an undergraduate student at UC Berkeley 🐻, where I simultaneously major in Electrical Engineering and Computer Sciences (EECS) 💻 and Mathematics 🧮. My focus lie in the intersection of High Performance Distributed Computing, Large-Scale Machine Learning, and High Dimensional Statistics.
Here are some of my work highlights:
- Nietzsche2000Map-Reduce-Distributed-Computing - Map Reduce using gPRC, a fully functioning map reduce system for clusters of computers.
- Nietzsche2000/TensorTrainALG - Advanced Tensor Operations: tensor train algorithms and tensor compression. For use in high dimensional data compression and speeding up inferences in Large Neural Network Models.
- Nietzsche2000/Day-Schedule - Day Scheduler is a Python application designed to organize and vocalize daily tasks.
🔭 Currently, I work on algorithms to compress large neural network models to reduce inference time and for efficient training. Previously, I worked in neural network theory--mathematical formalism of neural networks, and in optimization models.
Feel free to reach out to me at [email protected] if you want to discuss more about my interests or potential collaborations.
In the past, I've contributed to a variety of deep learning applications within the healthcare sector, which I've discussed in numerous international keynotes and TEDx talks.