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Vassilis N. Ioannidis is a Member of Technical Staff at NTT DATA AIVista, where he builds AI-native enterprise products for regulated industries using large language models, knowledge graphs, GraphRAG, agentic workflows, neurosymbolic reasoning, and AI verification. His work focuses on combining neural models with structured knowledge, symbolic constraints, and verification mechanisms to build reliable AI systems for complex enterprise environments, including insurance, banking, and healthcare.

Previously, Vassilis was an Applied Science Leader at AWS AI and Amazon Search AI. He led teams working on GraphRAG, automatic prompt optimization, model routing, model migration, synthetic data generation, and LLM-as-a-Judge evaluation, with product launches across Amazon Bedrock, Amazon Q, AWS AI, and Amazon Search. Earlier at AWS and Amazon, he helped develop Neptune ML and GraphStorm, and led graph and language modeling initiatives for retrieval, recommendation, abuse detection, generative AI, and e-commerce search. His work supported large-scale Amazon Search, Ads, AWS AI, Amazon Bedrock, and Amazon Q initiatives, contributing to multi-billion-dollar business impact.

Vassilis holds a Ph.D. in “Robust Deep Learning on Graphs” from the University of Minnesota. He has published 60+ conference and journal papers, mentored and managed dozens of scientists, and delivered invited talks, tutorials, and workshops at leading universities and AI venues, including Stanford, the University of Chicago, NeurIPS, KDD, WSDM, and Learning on Graphs.

Bio sketch

Vassilis N. Ioannidis is a Member of Technical Staff at NTT DATA AIVista, building AI-native enterprise products for regulated industries with LLMs, knowledge graphs, GraphRAG, agentic workflows, neurosymbolic reasoning, and AI verification. Previously, he led applied science teams at AWS AI and Amazon Search AI, launching graph-language technologies across Amazon Bedrock, Amazon Q, Neptune ML, GraphStorm, and Amazon Search, contributing to multi-billion-dollar business impact. He holds a Ph.D. from the University of Minnesota, has published 60+ papers and was cited 2000+ times, and is a frequent invited speaker at leading AI venues and universities.

Email: vassilisnioannidis@gmail.com

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Welcome!

Since Feb. 2020 I work at the AWS  AI Research and Education as a Senior Applied Scientist in the AWS Graph ML/DGL team. We design, implement and apply graph neural networks and graph machine learning to solve customers problems in AWS and Amazon. I have worked in the Neptune ML services that employs GNNs a variety of problems for customers of the Amazon Neptune database. I also work in retrieval, fraud detection and recommendation problems at Amazon by applying distributed GNNs and distributed language models at trillion edge graphs. Besides these project we also perform high impact research, and publish at top-tier conferences. I have mentored more than 30 scientists performing fundamental core modeling, system and application-focused research in GNNs. Please reach out if you are looking an internship in the area of GNNs and/or language models.

I obtained my Doctorate degree in the Department of Electrical and Computer Engineering at the University of Minnesota (UMN) under the supervision of Prof. Georgios B. Giannakis on Aug. 2020. My research leverages advances in deep learning on graphs, optimization, data science,  tensor decomposition, and machine learning to address robust learning tasks over large-scale dynamic networks. Research outcomes include scalable and online algorithms for predicting time-series on graphs, coupled tensor and graph factorization techniques for recommender systems and community detection, and graph neural network architectures. I obtained my (5-year) Diploma from the School of Electrical and Computer Engineering of the National Technical University of Athens in 2015 with Major in Computer Science. I also obtained my M.Sc. degree from UMN in 2017. From Sep. 2014 to Aug. 2015 I worked at Oracle as a Software Engineer, in the design and development of a Machine to Machine platform for Vodafone Group. From June 2019 to Dec. 2019 I worked at Mitshubishi Electric Research Labs as a Research Scientist in the design of novel Graph Convolutional Networks with application to 3D point cloud processing. We also analyzed key stability properties of methods for deep learning on graphs. Find out more in our ICLR publication.