Eric J Ma's Website

In this blog post, I explore how to tackle experimental noise and candidate ranking in protein screening using Bayesian methods. By employing R2D2 priors, we can decompose variance into interpretable components, helping us understand the true biological signal versus experimental artifacts. Additionally, Bayesian superiority calculation allows us to quantify the probability that one protein outperforms another, providing a more robust comparison than traditional methods. These techniques are not only applicable to protein screening but also to drug discovery, materials science, and more. Are you ready to enhance your experimental insights with Bayesian logic?

In this blog post, I share my transformative experience participating in the SciPy sprints, where I made my first open source contribution to Matplotlib. Through this journey, I gained confidence in git, improved my software skills, and learned the importance of effective communication with maintainers. Sprints offer invaluable networking opportunities and skill development, especially for students and data scientists. By contributing, you can make a lasting impact on projects you care about. Are you ready to join a sprint and potentially change the trajectory of your career? Read on to find out more!

Finesse in data science is the subtle skill that distinguishes exceptional practitioners from the merely competent. It involves recognizing when you're stuck in technical rabbit holes, creating tangible progress markers for stakeholders, working backwards from meaningful milestones, adapting with purpose when approaches aren't working, creatively overcoming technical roadblocks, and cultivating a network for timely assistance. These skills help data scientists navigate complex challenges while consistently delivering value, balancing persistence with adaptability, technical depth with clear communication, and planning with flexibility. How might developing your finesse transform your effectiveness as a data scientist?

In this blog post, I explore how cross-functional teams in biotech can accelerate molecule discovery using a strategic playbook. Through the story of a fictitious biotech, Catalyst Therapeutics, I highlight the importance of robust experimental design, integrating data science with human intuition, and balancing computational methods with practical insights. The team's journey reveals how better experiments lead to better models and ultimately, better molecules. Are you ready to discover how these principles can transform your biotech projects?

Encountering a PyPI upload error related to license metadata? The solution is straightforward - switch from setuptools to Hatchling as your build backend. In this post, I walk through how to fix the "license-file introduced in metadata version 2.4" error by updating your pyproject.toml configuration. Along the way, I learned some new things, including the fact that modern build backends like Hatchling provide better support for PEP 621 metadata features compared to older tools like setuptools.

When building machine learning models in biology, we often encounter data that's been heavily processed with statistical transformations like p-values and normalizations. This essay argues that this practice fundamentally undermines our ability to build reliable models and maintain interpretable datasets. Through a real-world example of protein binding experiments, it demonstrates why collecting physical quantities (like binding strength in nM) with proper replicates is vastly superior to statistical artifacts, and how Bayesian estimation can help us properly handle experimental variation while maintaining physical units. Are you tired of wrestling with hard-to-interpret biological data and ready to build more reliable experimental pipelines?

In this blog post, I explore how modern Python tooling is flipping the script on the age-old "which Python should I use?" question. Through my experience with uvx, marimo, and juv, I show how we're moving away from the traditional headache of environment setup and toward a world where tools automatically ship you the exact Python you need. No more environment setup puzzles – just specify your Python version and get straight to work. It's a liberating shift that's changing how I approach one-off Python work, and I think it's pretty exciting!

In this blog post, I share my journey of tackling dependency bloat in LlamaBot. What began as a simple LLM bot framework had grown into a monolithic system with an extensive dependency chain, leading to massive installation sizes. By mapping dependencies, refactoring the code, and organizing optional dependencies, I managed to reduce the container size significantly. This exercise taught me the importance of regular codebase maintenance and focusing on core functionalities. Now, LlamaBot is leaner and more efficient. Curious about the strategies I used to achieve this transformation?

In this blog post, I explore the concept of 'what makes an agent' by discussing various implementations of LlamaBot, a Python package for LLM exploration. I dissect the differences between SimpleBots, StructuredBots, and AgentBots, highlighting their capabilities and limitations in terms of agency and decision-making. Through audience discussions and examples, I aimed to provoke thought on the definition and design of agents, and together, we had an engaging discussion. Can we truly define an agent, or is it like the Turing Test, a concept that evolves with our understanding and technological advancements?

In this blog post, I explore the challenges biotech teams face when integrating public datasets with internal data for machine learning. Despite initial excitement, issues like data compatibility, missing variables, domain shifts, and biological complexity often arise. I suggest a shift from a machine learning perspective to a decision support approach, advocating for separate models and a decision fusion layer that incorporates human expertise. This method respects the complexity of biological systems and aids in effective decision-making. How can we better navigate these challenges to accelerate biotech discoveries?