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, graph algorithms and their applications. 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
- 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 Isnβt Pure Hype (And What Skeptics Arenβt 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