Call for Speakers is open up until Sept 10, 2020 at midnight PDT.

Gartner has estimated that the application of graph processing and graph DBMSs will grow at 100 percent annually through 2022. Graph analytics is poised to take AI to the next level across organizations of all sizes. The Graph + AI World conference is focused on accelerating AI and machine learning through graph algorithms and graph analytics and will be held virtually from September 28 through Sept 30, 2020. We are looking for knowledgeable and passionate speakers to share their thought leadership in this growing field. Whether you are a data scientist, engineer, architect or an IT or business stakeholder, we encourage you to consider sharing your insights at the conference.

Have you worked on combining AI or ML with graph algorithms for analyzing customer data? To predict health outcomes? To find fraudsters or cyber security threats? To recommend the most suitable products or services? To optimize supply chain and route planning? To balance the energy grid? Have you built a cool prototype to combine NLP, chatbot or other AI capabilities with a graph database? Have you connected a visualization tool with a graph platform? Then we want to hear from you! 

If selected, you will connect up with the attendees and speakers from UnitedHealth Group, JPMorgan Chase, Jaguar Land Rover, Intuit, Accenture, KPMG, Xilinx, and other Fortune 500 companies as well as most innovative startups in the internet, eCommerce, banking, insurance, fintech, media, manufacturing, transportation, government, and healthcare industries. 

Our audience will benefit from a mix of formal presentations and interactive panel participation. Case studies are particularly welcome.  The conference will include keynote as well as business and technology tracks, each covering beginner, intermediate and advanced level sessions. 

Here are some of the topics to consider for your submission: 

• Artificial intelligence use cases & case studies 
• Machine learning use cases & case studies 
• Graph Neural Networks
• First-of-a-kind solutions combining AI, machine learning and graph algorithms (example - graph + NLP, graph + chatbot, In-graph machine learning etc.)
• Predictive analytics
• Customer 360, customer journey 
• Hyper-personalized recommendation Engine 
• Fraud detection, anti-money laundering 
• Supply chain optimization
• Cybersecurity
• Industry-specific applications in internet, eCommerce, banking, insurance, fintech, media, manufacturing, transportation and healthcare industries.