Events
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Previous Webinars
Discussing ‘When the Curious Abandon Honesty: Federated Learning Is Not Private’ with Franziska Boenisch
Past Speaking Engagements
Our co-founders are leading experts on privacy-preserving natural language processing, machine learning edge deployment, model optimization, and more. Check out some of their past speaking engagements below. Interested in booking for them for your event, podcast, or publication? Contact us.
Deploying Transformers at Scale: Addressing Challenges and Increasing Performance
S3E1: “Privacy-preserving Machine Learning and NLP” with Patricia Thaine (Private AI)
Episode 3 – Navigating Privacy in AI with Patricia Thaine
An Introduction to AI and its Place in the Work Place with CEO of Private AI Patricia Thaine
Episode 200: Patricia Thaine, Co-Founder & CEO of Private AI | Host: Julian Torres
ChatGPT & Privacy Risks with Patricia Thaine and Punit Bhatia in The FIT4Privacy Podcast E094 S4
Demystifying the De-Identification of Data – How to Protect your Organization’s Data
Deploying Transformers at Scale: Addressing Challenges and Increasing Performance
Big Data & Analytics Strategy at the Heart of Cybersecurity and Privacy
Additional talks and panels include:
The Importance of Privacy in NLP (May 5, 2022), World Summit AI Americas
An Overview of Privacy-Preserving NLP (February 17, 2022), guest lecture at the University of Washington.Dealing with Personal Data using AI (December 15, 2021), at the Better Ethics and Consumer Outcomes Network’s Fireside Chat.Privacy-Enhancing Technologies in AI Security (December 1, 2021), at O’Reilly Media’s AI Superstream Series: Securing AI.
Panel: Big Data and Analytics Strategy at the Heart of Cybersecurity and Privacy (November 18, 2021), at Toronto Machine Learning Summit.
Panel: Demystifying the De-identification of Data (November 16, 2021), at The Innovation Game: Adopting RegTech in a Digital Age, Canadian Regulatory Technology Association.
Panel: Can voices be anonymised? (November 2, 2021), Lorentz Workshop on Speech as Personable Identifiable Information.
The Latest Advances in Privacy-Preserving NLP (September 21, 2021), Toronto Machine Leaning Summit on NLP.
Privacy Preserving Synthetic Data in AI/ML – A Mirage. (June 2, 2021), Privacy Symposium 2021 (Infosys – IAPP).
Efficient Evaluation of Activation Functions over Encrypted Data (January 15, 2021), UofT AI Conference.
Private-Preserving Machine Learning (December 3, 2020), MLOps: Production and Engineering Vancouver 2020.
Cybersecurity and Privacy: Complements for a more secure Internet (November 25, 2020), Keynote talk at Vector Institute Endless Summer School (ESS).
Panel moderator for The Role of ML in Climate Change (November 18, 2020), at Toronto Machine Learning Summit.
Privacy in Deployment (October 16, 2020), 2020 USENIX Conference on Privacy Engineering Practice and Respect (PEPR’20).
A Practical Guide to Privacy-Preserving Machine Learning (November 12, 2020), EVOKE CASCON 2020.
Privacy in Production (June 30, 2020), Canada AI/ML, Data Science and Engineering Digital Meetup.
Privacy-Preserving Machine Learning (June 18, 2020), MLOps: Production and Engineering World.
Privacy-Preserving Machine Learning: A Practical Overview (June 10, 2020), Vector Institute Endless Summer School (ESS).
An Overview of the Problem of Perfectly Privacy-Preserving AI (June 8, 2020), Future of Privacy Forum AI Working Group.
Privacy-Preserving Natural Language Processing Using Homomorphic Encryption (2019), National Research Council of Canada, Ottawa, CA.
Privacy-Preserving Natural Language Processing Using Homomorphic Encryption (2019), Borealis AI, Toronto, CA.
Perfectly Privacy-Preserving AI: What is it and how do we achieve it? (2019), Identity, Privacy, and Security Institute, Toronto.
Privacy-Preserving Natural Language Processing (2018), Vector Institute for Artificial Intelligence, Toronto, CA.
Vowel and Consonant Classification through Spectral Decomposition (2017), National Research Council of Canada, Ottawa, CA.
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