Maximilian Du

Media Annotator

This simple Python-based program allows you to use fast keyboard shortcuts to annotate audio, video, and live events with timestamped comments. There exist fancy tools that do this, but my version is very bare-bones and fully customizable. The program will export your annotations to copy-and-paste text that you can add to any literature review notes. I rely heavily on this tool for my own literature review.

Recently, I've also made a book annotator that allows you to make page-specific notes. This allows you digitize your annotations for physical books.

Code

MidiStyle: Audio Style Transfer

CS 229 Final Project

Style transfer is pretty pervasive in visual tasks, using anything from Gram Matrix methods to CycleGAN. Can we try using established vision style transfer algorithms on audio? In this project, we show that this is indeed possible. Using a spectrogram representation, we change a piano into harp, harpsichord, electric guitar, and even timpanis. We generate our own data using MIDI, and we test on a real-world piano.

Website   /   Code

Coding Basics

As a researcher, I often find that I use the same code many times in different applications. I need to iterate quickly on certain codebases, so I've been working on a large repository of code basics. Think about this as a bunch of partially constructed code snippets that range from live PyTorch debugging to making a graphical Tkinter app. I'm also including easily runnable PyTorch implementations of common ML models.

Code

Thompson Sampling Simulator

CS 109 Winning Final Project

The Multi-Armed Bandit is a fascinating theoretical question, but it is also a heavy philosophical question: how do we balance exploration vs exploitation? We look at one algorithm, known as Thompson sampling. Here, we simulate ants finding a good location for a nest. We also implement Tandem Running, which allows ants to "persuade" other ants, resulting in faster convergence.

Simulation   /   Code

The Tricks, Goofs, and Whales of Machine Learning

Machine learning is the abstraction of real learning processes into computational ones. Therefore, we find that it often imitates nature. In this 90-minute talk, I explore this parallelism by looking at connections between robot-learning and animal training. How does classical conditioning map to the mathematical Bellman Backup? How do animal trainers help their animals achieve good exploration policies? What do animal superstitions tell us about domain adaptation?

This talk was originally made for Stanford Splash, but it works for any audience of high school or undergraduate college students. If you want the slide deck or a presentation, please email me.

Automatic Source Vetter

This webscraping software will search RSS, YouTube, and Twitter accounts of your choice for new content with relevant keywords. It saves these to a SQL database for later perusal. I've also included a slurry of specialty website parsers for my whale book research. While you probably won't need these, the specialty parsers demonstrate the modularity of this software.

Code (still actively updating)