Andrew Trask: AI Research Scientist
Andrew Trask is a leading researcher and educator advancing privacy-preserving AI—bridging open-source innovation, ethics, and accessibility to democratize the future of artificial intelligence.
Quick Facts:
- Senior Research Scientist at DeepMind
- Founder and leader of OpenMined community
- Expert in privacy-preserving AI and machine learning
- Author of Grokking Deep Learning
- Formats:
- Keynote Speaker and AI Research Expert
- Audiences:
- AI researchers, policymakers, technologists, data scientists, and organizations focused on ethical AI and data privacy
- Outcomes:
- Understand privacy-preserving AI and secure data practices
- Build ethical and responsible AI systems
- Navigate the intersection of innovation, policy and trust
- Travels from: Washington, DC
- **US Fee range: $10,000 - $15,000
Talent Services
Keynote Topics:
Building safe artificial intelligence.
Building safe artificial intelligence.
Andrew details the most important new techniques in secure, privacy-preserving, and multi-owner governed artificial intelligence. Andrew begins with a sober, up-to-date view of the current state of AI safety, user privacy, and AI governance before introducing some of the fundamental tools of technical AI safety: homomorphic encryption, secure multiparty computation, federated learning, and differential privacy.
He concludes with an exciting demo from the OpenMined open source project that illustrates how to train a deep neural network while both the training data and the model are in a safe, encrypted state during the entire process.
Privacy-preserving AI.
Andrew touches on ideas such as Differential Privacy, and Secure Multi-Party Computation, and how these ideas come into play.
Many industries are limited by regulations of private data, and in response to the need for greater privacy in AI three cutting edge techniques have been developed that have huge potential for the future of machine learning in healthcare: federated learning; differential privacy; encrypted computation.
These modern privacy techniques would allow us to train our models on encrypted data from multiple sources without sharing the data.
What is Meaningful Privacy?
Andrew covers how to solve the world’s biggest privacy concerns using various state-of-the-art privacy-preserving technologies, such as privacy-preserving machine learning.
Talent Short Bio
Andrew Trask is a Senior Research Scientist at DeepMind, where he focuses on the intersection of privacy and artificial intelligence, and the Founder and Leader of OpenMined, an open-source community of over 12,000 contributors building tools for privacy-preserving machine learning.
Andrew’s mission is clear: to make AI both powerful and responsible. Through OpenMined, he’s championed global collaboration to ensure that data privacy, security, and access are baked into the foundation of AI systems. His work has influenced research, policy, and enterprise adoption across industries—from finance and healthcare to government and intelligence.
An accomplished educator, Andrew is the author of Grokking Deep Learning and a widely followed AI instructor on Udacity, where he makes complex ideas accessible to learners worldwide. He also authors the influential blog I am Trask, demystifying cutting-edge AI concepts for technical and non-technical audiences alike.
As a member of the United Nations Privacy Task Force, Andrew helps shape global standards for privacy-preserving analytics, advocating for open collaboration and ethical innovation in both public and private sectors.
Videos
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Fee Range Information
All talent fees exclude travel and accommodation where required.