Dmitrii Usynin

Dmitrii Usynin

PhD Student

Imperial College London

TU Munich

Biography

I am a PhD student at the Joint Academy of Doctoral Studies (JADS) launched between Imperial College London and Technical University of Munich. My research interests lie on the intersection of collaborative machine learning (CML) and trustworthy artificial intelligence (TAI). In particular, I am interested in topics such as privacy-preserving machine learning (PPML), attacks on CML, adversarial robustness, federated learning and memorisation in ML. Additionally, I am interested in applications of my research in the domain of collaborative biomedical imaging.

Some of my recent works include gradient-based model inversion attacks on collaboratively trained computer vision models (ACM TOPS 2023), low-cost empirical defences against privacy adversaries (PoPETS 2022), a framework for trustworthy collaborative medical image analysis (Nature Machine Intelligence 2021) and an overview of the current state of PPML and attacks on CML (Nature Machine Intelligence 2021).

Outside of my PhD, I am an Investment Partner at CreatorFund, leading early-stage deep tech investment in Europe. Previously I was also a privacy researcher at OpenMined, working on federated learning and differential privacy in healthcare. And outside of all that I am a rower and a WSET-certified expert in beer.

Interests

  • Privacy-preserving ML
  • Attacks on ML
  • Federated learning
  • Memorisation in ML

Education

  • PhD in Trustworthy Artificial Intelligence, 2020-2024

    Imperial College London, TU Munich

  • MEng in Computing, 2016-2020

    Imperial College London

Recent Publications

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(2021). A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index. arXiv preprint arXiv:2109.10528.

(2021). Adversarial interference and its mitigations in privacy-preserving collaborative machine learning. Nature Machine Intelligence.

(2021). An automatic differentiation system for the age of differential privacy. arXiv preprint arXiv:2109.10573.

(2021). Complex-valued deep learning with differential privacy. arXiv preprint arXiv:2110.03478.

(2021). Differentially private federated deep learning for multi-site medical image segmentation. arXiv preprint arXiv:2107.02586.

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