I work on machine learning with a focus on practical applications. I enjoy reading, coding, and writing about programming and machine learning.
- Natural Language Processing - Language understanding and generation, agentic AI applications
- Problems on Graphs - Random walks, information diffusion, network analysis
- Computer Vision - Image matching, explainability, and improved feature extraction
- Explainable AI - Making models interpretable for real-world use cases
- Applied ML Systems - Building and deploying practical ML solutions
Working on graphs and some interesting applications of random processes on graphs. I'm particularly interested in information diffusion, network flows, and random walks on graphs.
I write about writing clean and better Python, machine learning concepts, and more
- Shortcuts for the Long Run: Automated Workflows for Aspiring Data Engineers
- The Case for Makefiles in Python Projects (And How to Get Started)
- Build ETL Pipelines for Data Science Workflows in About 30 Lines of Python
- Why Agentic AI Isnโt Pure Hype (And What Skeptics Arenโt Seeing Yet)
- Step-by-Step Guide to Deploying Machine Learning Models with FastAPI and Docker
- Build a Data Cleaning & Validation Pipeline in Under 50 Lines of Python
- The Art of Writing Readable Python Functions
- Stop Writing Messy Python: A Clean Code Crash Course
- Go vs. Python for Modern Data Workflows: Need Help Deciding?
- Why & How to Containerize Your Existing Python Apps
- A Gentle Introduction to Go for Python Programmers