About the service
I build graph machine learning systems for tasks where the signal lives in connections, not individual table rows. When your users, accounts, devices, payments or products form a dense network of relationships, a flat table throws away the most valuable signal — who connects with whom. I design and train graph neural networks that read these connections directly, so the model reasons about a node's surroundings and paths between nodes instead of pretending each record exists alone.
I run the entire pipeline end-to-end. I start by building the graph: turning raw logs, transactions and catalogs into clean nodes and typed edges, deciding what a connection actually means and how to weight it. Then I assemble node and edge prediction models, train embeddings that place similar entities nearby and pick an architecture — GraphSAGE, GAT, message passing, heterogeneous graphs — matched to your data, not a textbook example. Honest evaluation matters to me as much as accuracy, so I set up leak-free splits, temporal validation and baseline models that honestly show whether the graph adds value.
Deployment is part of the work, not a nice bonus later. I package models for batch scoring or low-latency online inference, tune neighbor sampling for large graphs and document everything so your team can retrain and expand the system itself. Typical results: recommendation engines that understand broader context, fraud detection that catches whole schemes not just individual offenders and an enrichment layer serving graph embeddings to your existing models. If you have connected data and a feeling the answer is hidden in the connections themselves, I'll prove it and bring it to production.
I run the entire pipeline end-to-end. I start by building the graph: turning raw logs, transactions and catalogs into clean nodes and typed edges, deciding what a connection actually means and how to weight it. Then I assemble node and edge prediction models, train embeddings that place similar entities nearby and pick an architecture — GraphSAGE, GAT, message passing, heterogeneous graphs — matched to your data, not a textbook example. Honest evaluation matters to me as much as accuracy, so I set up leak-free splits, temporal validation and baseline models that honestly show whether the graph adds value.
Deployment is part of the work, not a nice bonus later. I package models for batch scoring or low-latency online inference, tune neighbor sampling for large graphs and document everything so your team can retrain and expand the system itself. Typical results: recommendation engines that understand broader context, fraud detection that catches whole schemes not just individual offenders and an enrichment layer serving graph embeddings to your existing models. If you have connected data and a feeling the answer is hidden in the connections themselves, I'll prove it and bring it to production.
