More than 6,000 Attendees Expected at April 21-23 Event; TigerGraph Finalizes Speakers, Sessions and Workshops Focused on Accelerating Analytics, AI and Machine Learning with Graph

REDWOOD CITY, Calif., April 12 (Bernama-GLOBE NEWSWIRE) -- TigerGraph, provider of the leading graph analytics platform, today unveiled the complete agenda for Graph + AI Summit 2021, the industry’s only open conference devoted to democratizing and accelerating analytics, AI and machine learning with graph algorithms. The roster includes confirmed speakers from JPMorgan Chase, Intuit, NewDay, Jaguar Land Rover, Pinterest, Stanford University, Forrester Research, Accenture, Capgemini, KPMG, Intel, Dell, and Xilinx, as well as many innovative startups including John Snow Labs, Fintell, SaH Solutions and Sayari Labs. The virtual conference, set for April 21-23, offers keynotes, speakers, real-world customer case studies and hands-on workshops for data, analytics and AI professionals.

“The combination of analytics, AI, machine learning and graph is a powerful one that offers many ‘human benefits’ — and forward-looking companies in all industries have taken note,” said Dr. Yu Xu, founder and CEO of TigerGraph. “Graph + AI Summit is again bringing together industry luminaries, technical experts and business leaders from the world’s largest banks, fintechs, tech giants and manufacturers to share implementation best practices, lessons learned and more. We’re pleased to welcome back speakers from Jaguar Land Rover and Intuit, and welcome new participants from an impressive list of today’s top innovators driving the adoption of graph. Our goal is to make graph accessible, applicable and understandable for all, as more people grasp how graph-related technologies can improve our lives.”

Graph + AI Summit returns after a successful Graph + AI 2020; the inaugural event attracted more than 3,000 attendees from 56 countries, and welcomed data scientists, data engineers, architects and business and IT executives from 115 of the Fortune 500 companies. The latest conference will host over 6,000 attendees this year and again focus on accelerating analytics, AI and machine learning with graph algorithms — timely technologies that are on the minds of today’s business leaders. After 2020 accelerated enterprises’ shift to the cloud, businesses are realizing graph technologies are key to connecting, analyzing and helping glean insights from data.

Graph + AI Summit 2021 includes keynote presentations, executive roundtables, technical breakout sessions, industry tracks (“banking, insurance and fintech,” “healthcare, life sciences and government”) and live workshops for advanced analytics and machine learning.

Keynote speakers presenting during conference general sessions include:

• Brad Spiers, executive director at JPMorgan Chase, will share lessons learned from combining machine learning and graph at JPMorgan Chase during the opening day general session.
• Harry Powell, director data and analytics at Jaguar Land Rover, will discuss the next phase of Jaguar Land Rover’s graph-powered transformation journey with digital twins. So far, the company has accelerated supply chain planning from three weeks to 45 minutes and driven more than 100 million pounds annually in incremental profits with advanced analytics.   
• Noel Yuhanna, vice president and principal analyst at Forrester Research, will discuss the graph and AI industry landscape and share insights from Forrester clients driving business outcomes from graph-based analytics, AI and machine learning.
• Danny Clark, head of fraud strategy at NewDay, will explain how the company identifies and prevents fraudsters from joining their network faster than ever before by using graph and machine learning to check data against known and new fraud syndicates.
• Dr. Jure Leskovec, chief scientist at Pinterest and professor at Stanford University, will outline how graph technology powers AI and what’s next in terms of the evolution of graph neural networks for delivering the next-generation AI.

• “Improving the Treatment of Acute Lymphoblastic Leukemia Using Graph Analytics with AI and Machine Learning”: The Technical University of Denmark’s Jesper Vang will discuss how combining graph analytics with AI, machine learning and translational bioinformatics creates models that can predict the risk of relapse and toxicity within acute lymphoblastic leukemia treatments.
• “Increase Market Share and Profitability with Graph-Based Doctor-and-Product 360”: Exact Sciences’ Sharat Endapally and Expero’s Scott Heath share a case study and a live demonstration of a Doctor-and-Product 360 application built using TigerGraph’s graph database, algorithms and Customer 360 toolkit.
• “COVID-19 Contact Tracing and Smart Lockdown”: This session will outline how to devise a solution for contact tracing and smart lockdown using graph technologies— a solution that helps track COVID-19 patients, their potential contacts and identifies high-risk public places efficiently and precisely.
• “Accelerating Digital Transformation with Graph and AI”: Forbes’ Tom Taulli will moderate a panel featuring executives from Intuit, Ippen Digital, Jaguar Land Rover and IBM about the role graph and AI play in a company’s journey to be a digital-first organization.
• “Accelerating Graph-Based Analytics and Machine Learning with the Next-Generation Hardware”: eWeek’s Chris Preimesberger moderates a panel with executives from Intel, Dell and Xilinx about how hardware industry leaders are building the future of graph-based analytics and machine learning to deliver higher performance for enterprise workloads.
• “Using Graph to Boost AI”: Uri Lapidot, senior product manager, Intuit, shares best practices from the risk and fraud team to use graph-based technologies to prevent fraud at scale.
• “Open Data and Graph Analytics for Risk Management and Compliance”: Farley Mesko, CEO, Sayari Labs, shares how graph technology can help financial institutions combine their internal data with authoritative open data to increase corporate transparency and comply with key KYC, AML, sanctions and supply chain regulations.

• The integration of graph algorithms into your analytics and AI projects
• The future of graph query languages, including GQL and SQL PGQ standards
• Graph enrichment techniques that leverage NLP for enhanced knowledge retrieval
• Unsupervised and supervised machine learning with graph
• Popular graph use cases including fraud detection, anti-money laundering (AML), recommendations, data lineage, knowledge graph, supply chain, network analysis, patient and doctor 360, disease prediction and prevention

• “Building the Next Generation Customer Experience with Graph and Machine Learning”
• “Double the Performance of Your Fraud Detection System with Graph and Machine Learning”
• “Zero to Advanced Analytics and Machine Learning in 90 minutes with TigerGraph Cloud”

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