2024 Fellow Dmitrii Usynin

Dmitrii is a PhD student at a Joint Academy of Doctoral Studies (JADS) launched between Imperial College London and Technical University of Munich. His research interests lie in the domain of adversarial influence in collaborative machine learning, specializing in medical image analysis. Dmitrii is currently open for new collaborations on the topics of: Privacy attacks on AI systems, adversarial robustness of ML models, federated learning, differential privacy and collaborative learning for medical imaging. Dmitrii is also a privacy researcher at OpenMined, working on federated learning and differential privacy in healthcare. His recent works include “Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning” (PETS 2022), “Adversarial interference and its mitigations in privacy-preserving collaborative machine learning” (Nature Machine Intelligence), “Can collaborative learning be private, robust and scalable?" (MICCAI DeCaF 2022) and “End-to-end privacy preserving deep learning on multi-institutional medical imaging” (Nature Machine Intelligence). Dmitrii graduated from Imperial College London with an MEng in Computing and a distinguished project titled “Privacy-Preserving Machine Learning in a Medical Domain”.