Unsupervised Voice Conversion
Listen to some audio samples demonstrating my Keras implementation of the AutoVC voice conversion autoencoder.
Training a Neural Network for Word Separation
There are some 290k posts from social media site Hacker News in 2016. Training a deep learning model with a special loss function, the top 500 words used in these posts are projected into 2d space and then grouped using KMeans.
RRQR Factorization and Spanning Boxes
The famous QR factorization algorithm can be BLAS-3 optimized. Using the Woodbury matrix identity, we implement a block-based Rank-Revealing QR and point out a connection between pivot selection and object-spanning-boxes.
Solve a Substitution Cipher with a Markov Chain
There are k! substitution ciphers for an alphabet with k letters—too many for an exhaustive search. With a Markov Chain adapted to the graph of alphabetic ciphers, we redefine the act of deciphering as a sampling problem suitable for a Metropolis-Hastings random walk.
Plant Pairs on the Tufts Campus
In Spring of 2019 my Environmental Fieldwork class surveilled the herbaceous plants growing on and around the Tufts campus, recording their identities and locations into a GIS database. For a final project I created a simple Cartesian quadrature algorithm in Python to identify the distinct plant pairs most likely to share the same soil.
Latest posts
- Unraveling Culinary Secrets with a Restricted Boltzmann Machine: A Deep Dive into Ingredient Patterns
At the heart of our project lies the Restricted Boltzmann Machine, a type of neural network particularly adept at uncovering hidden patterns in data. In the culinary world, ingredients form complex relationships, creating a web of flavors and textures. Our goal is to untangle this web and discover how ingredients co-occur in recipes. We feed… Continue reading Unraveling Culinary Secrets with a Restricted Boltzmann Machine: A Deep Dive into Ingredient Patterns - Streamlining Data Handling in PyTorch: Building an Efficient Data PipelineIntroduction In the realm of machine learning, managing large datasets efficiently is often a critical task. PyTorch, known for its flexibility and ease of use, offers robust tools for this purpose. This article aims to guide you through constructing a data pipeline that not only manages memory efficiently by streaming data from the hard drive… Continue reading Streamlining Data Handling in PyTorch: Building an Efficient Data Pipeline
- The Food.com 2023 DatasetThe dataset is a rich compilation of recipes, spanning a wide range of cuisines and styles. It offers a unique perspective on what makes a recipe more than just a list of ingredients and steps. With over 500k recipes, it’s a deep dive into the culinary world, providing data enthusiasts, chefs, and food bloggers an opportunity to analyze and understand cooking trends on a macro scale.
- Keras-AutoVC Voice Conversion DemoVoice conversion with a Keras autoencoder model
- Block Matrix MultiplicationWhen dealing with large matrices, it helps to have an algorithm to break multiplication into operations on matrix subblocks. Tens of thousands of indices add up to gigabytes, and you’re going to have trouble calling X.matmul on a matrix library backend. Here’s a simple implementation that plays to the tune of Major->Minor->Major to reassemble the… Continue reading Block Matrix Multiplication
About Me
I’m Alexander Wei, so welcome to my website! I’ve collected a few of my favorite projects to showcase my recent work, and these are displayed here on the home page. My work involves analysis, statistics, and programming.
I hope to make a difference in people’s lives by bringing theory to the forefront of application.
I studied math for six years 2016–2022 at the undergraduate and graduate levels and have taken courses in Computer Science. In addition, I have been programming in Java, C++, and Python since 2010, and have years of experience administering and securing Linux servers.
Alexander Wei, B.A. M.S. Mathematics
Tufts University 2016–2022