I work at the intersection of large networks – property graphs or knowledge graphs, representation learning with Graph Neural Networks (GNNs), Natural Language Processing (NLP) and Understanding (NLU), model explainability using network visualization and vector search to use the preceding technologies for information retrieval.
I am a startup product and engineering executive focused on building products driven by large networks, property graphs or knowledge graphs. I have worked at cool places like LinkedIn, Hortonworks. Recently I co-founded Deep Discovery to use networks, GNNs and visualizations to build an explainable risk score for KYC / AML. I am taking the lessons learned and building an open source project called Graphlet AI, a Knowledge Graph Factory at https://github.com/Graphlet-AI/graphlet. I have 120 citations on Google Scholar for being the first to write about agile development as applied to data science and AI.
I have a 15-year obsession with large networks spanning a dozen jobs, projects and datasets. I am a four-time O’Reilly author and proven leader of teams that build, ship and operate AI applications. I have extensive experience in all aspects of data science, data engineering, machine learning and ML operations that are part of building data-driven applications. I am an applied researcher and product manager with a broad range of skills from 17 years of experience building and shipping data-driven products.
I am currently interested in knowledge graph construction, graph representation learning, graph neural networks (GNNs), NLP/NLU techniques such as information extraction, named entity resolution (NER), coreference resolution, fact extraction, and entity linking.