Eric J Ma's Website

In this blog post, I discuss the importance of unit tests in research code. I share an experience from a code review with our intern, Matthieu, where we realized the need for rigorous testing of a non-standard splitting strategy in our ML model. We concluded that even research code, which might be discarded eventually, can benefit from thorough testing to ensure its correctness. This is particularly crucial when the code is used for comparisons or collaborations.

In this blog post, I explore the two flavours of data science work: service-oriented and product-oriented. Service-oriented data science serves others in a one-off fashion, while product-oriented data science builds a reusable tool for a well-defined problem. Both have their value depending on the situation. I discuss the challenges in navigating between the two and emphasize the importance of adopting a product-first orientation. As an individual contributor or team lead, it's crucial to shift from being mere consumers of tooling to makers of tools, enhancing efficiency and scalability!

In my latest blog post, I share my experience of hosting a Dokku server on DigitalOcean and how I've managed to automate the deployment process using GitHub Actions. I delve into the cost benefits of using Dokku on DigitalOcean over other services like Heroku and Fly.io. I also provide a step-by-step guide on how to configure GitHub Actions to deploy apps to DigitalOcean automatically. If you're interested in saving time and money on app deployment, this post is a must-read.

Today, I learned how about a hidden setting that's needed to enable GitHub Actions to push code to its associated repo, and wrote it out as a tutorial. As a bonus, I also share how to correctly configure git within a GitHub actions workflow. Discover the trick with me!

Today I learned how to execute arbitrary commands on startup on a Linux machine. It's pretty simple. Curious to hear more?

I've just explored a fascinating paper on differentiable search of evolutionary trees. It's a creative blend of math and biology, using mathematical structures to solve a biological problem. The authors have developed a way to infer both phylogenetic trees and ancestral protein sequences in a continuous, differentiable manner. This opens up exciting new avenues for protein engineering and design. Plus, the paper's figures are top-notch! 🧬🌳📊

In this post, I discuss the 'Wayne Gretzky move' in tech adoption, especially in data science. It's about foreseeing the future of tech and making bold moves to stay ahead of the game. It's risky but with the right team, it can lead to significant gains. 🏒💻🚀

In this post, I discuss how to decide which technological models to adopt in our work. I introduce two frameworks: the 'technological trinity' and the 'technology ladder'. The former helps us evaluate if a new technology is worth investing in, while the latter places it within a broader context. I illustrate these concepts using protein engineering as an example.

Just built LlamaBot, a chatbot using OpenAI GPT-4, and it's been a wild ride! 🎢 From dealing with rapid innovation and versioning issues to discovering the power of code ghostwriting, it's been a learning curve. 🧠💡 Also, I explored some cool LLM frameworks. Dive in to learn more! 🏊‍♂️

In today's blog post, we dive into setting the minimum and maximum Python versions in pyproject.toml 🐍. We explore how this impacts llamabot and discuss the new syntax for indicating the union of types in Python 3.10. This new syntax is visually easier to understand, making our coding journey a bit smoother! 🚀👩‍💻👨‍💻