Alicia is the Senior Director of Product Management, Graph Data Science at Neo4j (and still a data scientist at heart!).
She’s excited to build tools that empower users to solve critical problems. Her background is in the life sciences, and during the course of her career she’s always worked to develop models and dashboards to help scientists and end users answer business critical questions using data and predictive models. As a product manager at Neo4j, she sees her role as democratizing state of the art academic research so it can be leveraged by end users in a scaleable, easy to understand way.
We don’t take breaks at Neo4j – we’re following up GraphConnect with yet another awesome release of the Graph Data Science (GDS) library. Our engineers are constantly raising the bar, and some of the highlights in this release: ? New algorithms: K-means clustering and Leiden for... read more
It’s been a crazy journey going from our initial product launch two years ago to the fully featured graph data science platform we have today. When we started, we had a handful of your favorite graph algorithms, and a vision of making graph data science super simple. Today, we’re celebrating... read more
In today’s economic climate, we need deep, reliable insights and strong predictions. Data is our most valuable asset, but the relationships in and across data remain largely untapped. Graph embeddings are a new technology that learns the structure of your connected data, revealing new... read more
Editor’s note: This presentation was given by Alicia Frame at Connections: Accelerating Innovation with Graphs. As Director of Data Science for Neo4j, I am excited to share how you can accelerate AI and machine learning innovation by using graph data science. While you can use queries to... read more
At this point, we’re all back from our summer holidays – whether we want to be or not. One consolation prize, though, is that the team at Neo4j spent our summer putting together another fabulous update to the Graph Data Science Library! ? This release was really focused on making graph... read more
We’ve just released the 1.6 version of the Graph Data Science (GDS) Library, and we’ve spent this release focused on how to reduce friction of getting graph data science results into production. We’ve improved our machine learning procedures, added enhancements to our graph embeddings, and... read more
Last week, we officially released the 1.3 version of our Graph Data Science (GDS) library. I can’t believe 1.0 was only a few months ago, or how many new features and functionalities we’ve added into the last three releases! The GDS library now leverages Neo4j 4.0.1 and 4.1, and has... read more
At Neo4j, we’ve always been user-driven, and we know that many of you are dealing with increasingly complex questions and uncertain situations. One of our biggest user communities are developers and data scientists working for businesses that use advanced analytics and data science to... read more
