Where Science Meets AI Innovation
I’ve spent over a decade turning complex research questions into elegant software solutions. I’m always on the lookout for opportunities to pursue my passion for building AI systems that are dependable, adaptable, and impactful.
My unique perspective combines rigorous scientific methodology with modern ML engineering practices—creating solutions that researchers and industry professionals can trust with their most important data.
⭐ Featured Projects
Making knowledge accessible through conversational AI
Agentic retrieval for 10+ years of email archives, designed for deep research and continual data updates. Walkthrough video
AI alignment for detecting meaningful changes
This project leverages AI alignment with human-in-the-loop to build a practical solution for filtering signal from noise in document updates.
Combating inaccurate citations with AI
Taking citation verification analysis from research to production through model optimization, deployment, and feedback collection.
Building scientific software that lasts
Enabling scientific discovery through chemical data analysis and visualization workflows and a commitment to testing and long-term maintenance.
What Sets Me Apart
I don’t just implement algorithms—I solve meaningful problems:
Problem-first thinking: Academic training taught me to ask the right questions before building solutions.
Production-ready mindset: 15+ years maintaining production software means I build for reliability, scalability, and long-term sustainability from day one.
Community builder: Successfully grew and maintained global research communities.
Data storyteller: Authored 20+ peer-reviewed papers requiring clear communication of complex technical concepts to diverse audiences.
Continuous learner: From R packages to PyTorch models to LangChain applications—I adapt to new technologies while maintaining deep expertise.
Core Technical Skills
AI & machine learning: PyTorch • scikit-learn • NLP • Large Language Models • Fine-tuning • RAG Systems
MLOps & production: Docker • AWS • CI/CD • Testing • Monitoring • Model Deployment • Hugging Face
Data engineering: Python • SQL • R • Data Pipelines • Multi-source Integration • Quality Validation
Development: Git • Linux • Shell • Jupyter • API Design • Open Source Development
Academic Foundation Meets Industry Innovation
My academic background isn’t just about papers—it’s about transferable skills that make me a stronger ML engineer:
Research methodology: Hypothesis formation, experimental design, and rigorous evaluation
Technical communication: Translating complex concepts for diverse stakeholders
Project leadership: Managing long-term projects from conception to community adoption
Global collaboration: Working with international teams across time zones and cultures
Innovation under constraints: Creating solutions with limited resources and high quality standards
Let’s build AI that doesn’t just work today—but works reliably for years to come.