I build things because I want to understand why they work, not because a tutorial told me to.
Most of my work lives somewhere between research, engineering and data science. I'll read a paper until I find the unanswered question underneath it, then build the smallest experiment that can prove (or disprove) the idea. Sometimes the hypothesis survives. Sometimes it doesn't. Both outcomes are useful.
I'm naturally drawn to systems. Give me a single model and I'm already thinking about retrieval pipelines, evaluation frameworks, orchestration, observability, and the trade-offs that appear once something leaves a notebook and has to behave consistently.
My curiosity doesn't really stop at AI. One week it's representation learning or medical vision models. The next it's the psychology of why humans exist, designing a better Notion system for my life, or disappearing down a rabbit hole that absolutely wasn't part of the original plan.
I like repositories that explain themselves. Code matters, but so do the decisions behind it. If you've scrolled past the installation instructions and you're reading the README because you want to know why something was built a certain way, you're exactly the audience I had in mind.
- Grounded multimodal AI
- Evaluation for LLM systems
- Medical vision-language models
- Agentic workflows
- Whatever paper I read at 2 AM
Still chasing interesting questions.
• IEEE ASIANCON 2025 — Boosting LLM Performance on Boolean QA Using Cluster-Based Embeddings
• IEEE FMLDS 2025 — Evaluating the Impact of Input Format on LLM Performance
• IEEE iSSSC 2025 — ECG-based Biometric Encryption for Remote Patient Monitoring
Always happy to talk about research, engineering, or an interesting rabbit hole!