Explore featured sessions for accelerated data science.

See your life’s work realized with AI and data science. Join thousands of other practitioners, leaders, and innovators at GTC to learn data science from the world’s most advanced data teams.


Jensen Huang | NVIDIA | Founder and CEO

Watch the keynote replay to hear Jensen Huang's insight into how NVIDIA is driving the rapid pace of technology advancements to help solve the world's toughest challenges.

Featured Speakers

Accelerating Apache Spark

Sameer Raheja 
Senior Director, Engineering, NVIDIA

From CPU to GPU with Cloudera Machine Learning

Jake Bengtson
Senior Technical Product Marketing Manager, Cloudera

Confronting a Changing World: Using Ultralow Precision to Detect Spatiotemporal Environmental Changes with Impacts on Food, Energy, and Pandemics

Daniel Jacobson
Chief Scientist for Computational Systems Biology, Oak Ridge National Laboratory

GPUs for Biomedical Knowledge Graphs

Sebastian Nilsson, Michaël Ughetto
Machine Learning Engineer, Graph Data Scientist
AstraZeneca PLC

Featured Sessions by Topic


  • Large-Scale Graph Neural Networks Optimization for Financial Application

    • Venkatesh Ramanathan, Director of Data Science, PayPal

    To efficiently service its millions of global customers, PayPal has developed novel approaches to graph neural networks that operate at a massive scale. Hear about a variety of challenges PayPal encountered and addressed when developing and optimizing these graph neural networks, including compute, orchestration, and experimentation as key focus areas.

  • Digital Transformation in Credit Agricole via Document Automation

    • Gilles Moyse, CEO, ReciTAL

    Learn how Credit Agricole automated the processing and analysis of corporate documents, saving each of their 80,000 employees around two hours per week.

  • AI in Fintech: With Great Power comes Great Responsibility

    • Spiros Margaris, Founder, Margaris Ventures
    • Theodora (Theo) Lau, Industry Business Development, Financial Services, Unconventional Ventures

    AI is permeating every facet of financial services, including underwriting, customer service, fraud prevention, and cybersecurity. Business leaders across financial services are racing to AI-enable hundreds of applications to capture the market opportunity, while also to develop sound AI practices that enable social good. During this panel conversation, we’ll discuss all of these dynamics at play with influential thought leaders.

  • Constructing Cross-sectional Systematic Strategies by Learning to Rank

    • Daniel Poh, Ph.D. Researcher, Oxford-Man Institute of Quantitative Finance, University of Oxford
    • Stefan Zohren, Associate Professor - Oxford-Man Institute of Quantitative Finance, University of Oxford

    Recently, there has been increased interest in applying deep learning techniques in finance. After reviewing some of the latest advances in this field, we’ll focus on a case study that uses techniques from information retrieval in the context of cross-sectional systematic trading strategies. These strategies depend critically on ranking assets prior to portfolio construction.

AI Strategy for Business Leaders

  • Bridging the Last Mile Gap with AI Education

    • Hajar Mousannif, Associate Professor, Cadi Ayyad University
    • Natnael Kebede, Chief NERD & Co-ounder, NERD
    • Michael Young, Tech Community Builder, Python Ghana

    NVIDIA launched the Emerging Chapters Program five months ago to empower emerging market developers with the latest industry knowledge and skills. The program connects developers and technologists with diverse backgrounds, provides training to help developers solve their most challenging problems with NVIDIA technologies, cultivates growth in emerging sectors, nurtures NVIDIA ambassadors, and accelerates startups and researchers through NVIDIA’s Inception and academic programs. In this panel discussion, we invited a few community leads to share their experience with the Emerging Chapters Program and how NVIDIA helps their developers unlock innovation and new opportunities.

  • AI in Practice: Allianz Cuts Down on Time-to-insight with Out-of-Box NLP

    • Anshul Vikram Pandey, Co-Founder and CTO, Accern

    Typically, the accuracy and relevance of a machine learning model directly correlate with the amount of time spent iterating and training that model. Learn how Allianz implemented a no-code natural language processing (NLP) tool run on NVIDIA GPUs to incorporate sentiment data into their process and get portfolio insights more efficiently.

  • Disrupting Insurance Business Models with Data and AI

    • Robin Jose, Chief Data and Analytics Officer, wefox

    Wefox, the largest insurtech of Europe, looks forward to disrupting existing business models with the power of data and AI. How can insurance be the guardian angel that enables "risk prevention" for customers? We'll talk about how wefox uses data and AI to meet this vision.

Deep Learning

  • PyTorch Ecosystem: The State of the State 2021

    • Joseph Spisak, Product Manager, Facebook

    PyTorch continues to be a foundational component for AI research, and increasingly for production at scale, at companies like Facebook, Microsoft, and many others. As PyTorch continues to grow, so does the ecosystem around it. Joe Spisak, product lead for PyTorch at Facebook AI, will provide an update on the state of the AI community and ecosystem around PyTorch.

  • What's New in TensorFlow?

    • Josh Gordon, Developer Advocate, Google

    Learn about new and recently updated libraries for TensorFlow, including TensorFlow Decision Forests (which you can use to train random forests and gradient-boosted trees), new Keras examples, and new models on TensorFlow Hub. For each library we mention, we'll include links to complete, end-to-end code you can try at home.

  • Deep Learning Demystified

    • Ozzy Johnson, Director, Solutions Architecture, NVIDIA

    AI has evolved and improved methods for data analysis and complex computations, solving problems that seemed well beyond our reach. Learn the fundamentals of accelerated data analytics, high-level use cases, and problem-solving methods—as well as how deep learning is transforming every industry. We'll cover understanding the key challenges organizations face in adopting this new approach and how to address them; and learning about the latest tools and technologies, along with training resources, that can help deliver breakthrough results.

  • Constructing Cross-sectional Systematic Strategies by Learning to Rank

    • Mahantesh Halappanavar, Chief Scientist and Group Leader, Pacific Northwest National Laboratory

    Recently, there has been increased interest in applying deep learning techniques in finance. After reviewing some of the latest advances in this field, we will focus on a case study that uses techniques from information retrieval in the context of cross-sectional systematic trading strategies. We'll also introduce an extension where we explore incorporating a high-frequency market fragility measure to rankers.

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