Matthew M
- Research Program Mentor
PhD candidate at Stanford University
Expertise
machine learning, neural networks, image classification, text generation, generative modeling
Bio
My research develops novel generative modeling techniques and algorithms in order to improve sample quality, synthesis speed, and training efficiency. A significant part of my work involves improving variational inference approaches, where I often draw inspiration from diverse mathematical areas such as Markov processes. I've spearheaded several research projects aimed at improving diffusion models for image synthesis. Beyond this, the realm of text-to-image synthesis and its recent advancements hold great excitement for me. When I step away from my academic pursuits, I turn to physical activity to maintain a healthy balance. I enjoy running, playing soccer, and engaging in semi-regular gym workouts. My interest in sports also extends into the professional side, where I follow the latest developments in the NFL, NBA, and international soccer. I also love to read books from a variety of genres, which allows me to explore different perspectives.Project ideas
Text Generation Using Machine Learning
In this project, we'll explore how to generate text using machine learning. We'll start by picking a text dataset, such as freely available books or articles. The first step will be to clean and preprocess the data, a crucial part of any machine learning project. Next, we'll implement different machine learning methods, ranging from simple models like n-grams to more advanced approaches using neural networks. This will give us a chance to understand the strengths and weaknesses of each model. Finally, we'll evaluate our models, critiquing the quality of the text generated. Through this project, we'll gain practical experience with machine learning and develop a better understanding of the challenges involved in text generation.