About me

This is Dr Ali Shahin Shamsabadi (ashahinshamsabadi@brave.com)! I am a Senior Research Scientist (now expanding into product strategy and cross-functional leadership too) at Brave Software. I collaborate across disciplines and organizations to turn scientific insights into innovative, impactful products. Before joining Brave Software, I was a Research Scientist at The Alan Turing Institute (Safe and Ethical AI) under the supervision of Adrian Weller, and a Postdoctoral Fellow at Vector Institute under the supervision of Nicolas Papernot. During my PhD, I was very fortunate to work under Aurélien Bellet, Andrea Cavallaro, Adria Gascon, Hamed Haddadi, Matt Kusner and Emmanuel Vincent.

Research

My research initiates a fundamental question: How can we reliably verify the trustworthiness of AI-based services, given that: i) AI-based services are provided as "black-boxes" to protect intellectual property; ii) Institutions are materially disincentivized from trustworthy behavior.

Verifiable Trustworthiness of AI in Practice

Identifying failure modes for AI systems

Secure and privacy-preserving (by design) AI

Product

Privacy Preserving Product Analytics

Nebula: a novel, practical and best-in-class system for product usage analytics with differential privacy guarantees! Nebula puts users first in product analytics: i) Formal Differential Privacy Protection; ii) Auditability, Verifiability, and Transparency; and iii) Efficiency with Minimal Impact.

Privacy-Preserving Conversation Analytics

Coming soon.

Secure, Privacy-Preserving and Efficient Agents

Coming soon.

Recent Students

News

Selected Research Talks

Differentially Private Speaker Anonymization (PETS 2023)
Mnemonist: Locating Model Parameters (UAI 2023)
Losing Less: A Loss for DP Deep Learning (PETS 2023)
ColorFool: Semantic Adversarial Colorization (CVPR 2020)
EdgeFool: Adversarial Image Enhancement (ICASSP 2020)

Talks

  • 05/2024 - ICLR 2024 conference -- Confidential-DPproof: Confidential Proof of Differentially Private Training Video
  • 07/2023 - UAI 2023 conference -- Mnemonist: Locating Model Parameters that Memorize Training Examples Video
  • 06/2023 - PETS 2023 conference -- Losing Less: A Loss for Differentially Private Deep Learning Slides Video
  • 06/2023 - PETS 2023 conference -- Differentially Private Speaker Anonymization Slides Video
  • 05/2023 - ICLR 2023 conference -- Confidential-PROFITT: Confidential PROof of FaIr Training of Trees Video
  • 05/2023 - Workshop on Algorithmic Audits of Algorithms
  • 05/2023 - Intel
  • 04/2023 - Northwestern University -- How can we audit Fairness of AI-driven services provided by companies?
  • 03/2023 - AIUK 2023 -- Confidential-PROFITT: Confidential PROof of FaIr Training of Trees Video
  • 03/2023 - University of Cambridge -- An Overview of Differential Privacy, Membership Inference Attacks, and Federated Learning
  • 11/2022 - NeurIPS 2022 conference -- Washing The Unwashable : On The (Im)possibility of Fairwashing Detection Video
  • 11/2022 - University of Cambridge and Samsung
  • 10/2022 - Queen's University of Belfast
  • 09/2022 - Information Commissioner's Office
  • 09/2022 - Brave
  • 06/2020 - CVPR 2020 conference -- ColorFool: Semantic Adversarial Colorization Video
  • 05/2020 - ACM Multimedia 2020 -- A tutorial on Deep Learning for Privacy in Multimedia Slides
  • 05/2020 - ICASSP 2020 conference -- EdgeFool: An Adversarial Image Enhancement Filter Video
  • 06/2018 - The Alan Turing Institute -- Privacy-Aware Neural Network Classification & Training -- Video
  • 06/2018 - QMUL summer school -- Distribute One-Class Learning Video