I'm an Applied Data Scientist, deeply passionate about advancing Artificial Intelligence. My work focuses on Geometric Deep Learning, Advanced Statistical Learning, Large Language Models (LLMs), and MLOps, with an emphasis on applied research, production AI systems, and decision intelligence.

• 🔭 Working on Applied AI Research and productifying AI prototypes
• 🌱 Exploring Agentic and Generative AI, including LLM-powered decision support, multi-agent systems, and human-in-the-loop AI
• 🤝 Open to collaboration across academia and industry in the AI/ML space
• 🧠 Experienced in designing and operationalizing ML pipelines and MLOps workflows with reproducibility, monitoring, and governance in mind
• 🧪 Interested in LLM evaluation, bias analysis, and robustness for real-world analytical and research workflows
• 💬 Open to conversations on anything - always happy to connect and exchange ideas!
• ⚡ Fun fact: Built a graph model to identify influencers - surprisingly, the quietest node was the most connected

• LLM Model Selection Lab
  Decision-centric evaluation lab for intelligent LLM model selection across real-world GenAI workflows.

• Databricks MLOps Lifecycle
  End-to-end ML lifecycle using Spark ML, MLflow, Delta Lake, orchestration, and drift monitoring.

• pgvector Semantic Search Demo
  Vector similarity search system using PostgreSQL pgvector for semantic retrieval and RAG-style workflows.

• Time Series Research Lab
  Forecasting, anomaly detection, and statistical modelling for large-scale temporal data analysis.

My interests sit at the intersection of Applied AI systems, MLOps, and decision intelligence.

Current focus areas include:

• Agentic AI architectures and multi-agent decision systems
• Evaluation, reliability, and robustness of large language models in analytical workflows
• AI-driven decision intelligence platforms for complex operational environments
• Scalable ML systems and reproducible experimentation