Russell Jurney works at the intersection of Big Data, 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 for information retrieval and Large Language Models (LLMs).
He has a consulting firm called Graphlet AI focused on knowledge graphs and Large Language Models (LLMs).
He is a startup product and engineering executive focused on building products driven by billion node+ networks using Artificial Intelligence. He has worked at cool places like Ning, LinkedIn and Hortonworks. He co-founded Deep Discovery to use networks, GNNs and visualizations to build an explainable risk score for KYC/AML.
He has a four-time O’Reilly author with 120 citations on Google Scholar for being the first to write about Agile Data Science – Agile Development as applied to Data Science and Machine Learning. He has an applied researcher and product manager with 17 years of experience building and shipping data-driven products. He does Network Science, Machine Learning e NLP.
He is currently fascinated by knowledge graph/property 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. LLMs have exploded on the scene and he is working out how to use them to solve problems in the areas of cybersecurity and financial compliance such as KYC, AML and CFT.