Machine Learning Engineer
Currently a Machine Learning Engineer at TigerGraph, working to build a better developer experience of building machine learning models with graph data. Developer of pyTigerGraph, an open-source Python package with 3,000+ downloads weekly. Formerly a part time software engineering intern at Optum, working on machine learning with graph databases. Patent-pending inventor for a fraud detection algorithm using Graph Neural Networks and other machine learning algorithms with knowledge graphs.
Talks and Events
Graph Embedding Techniques – Matrix Factorization to Deep Learning
Graph embeddings can be used for a variety of applications, including recommendation, fraud detection, and other machine learning tasks. In this work, we aim to walk through various different embedding techniques, starting with spectral approaches, moving towards graph neural networks, and finally newer, inductive techniques such as NodePiece. Throughout the tutorial, we will be implementing algorithms using the (open-source) TigerGraph Machine Learning Workbench, a tool for easily training machine learning algorithms on large-scale graph datasets. Using a variety of datasets, we will discuss the pros and cons of each technique, demonstrate them working, and examine future directions for the field of graph embedding research from the lens of industry.
Track: Deep Learning for and with Knowledge Graphs
- Business Use Cases
- Graph Data Science
- Deep Learning for and with Knowledge Graphs
- Graph Machine Learning