Biography

Jonathan is a senior research scientist at Equideum Health.

He currently leads the R&D arm of Equideum, where the team focuses on privacy-preserving machine learning and decentralisation. Jonathan is exploring the potential of bringing together techniques such as Collaborative Learning and Blockchains to build a new generation of healthcare systems. He has grown a strong expertise in Secure Computing (Trusted Execution Environments - TEEs, FHE, …), Federated Learning and Verifiable Computing (TEEs, Zero-Knowledge Proofs, …) to enhance machine learning deployments with these Privacy Enhancing Technologies.

Jonathan is also a research fellow at Imperial College London (BioMedIA) and City, University of London (CitAI) where he co-supervises MEng, MSc and PhD students on the topics Privacy-Preserving Machine Learning and Federated Learning.

• 2CP: Decentralized Protocols to Transparently Evaluate Contributivity in Blockchain Federated Learning Environments

  Details PDF

• A Systematic Comparison of Encrypted Machine Learning Solutions for Image Classification

  Details PDF

• A blockchain-orchestrated Federated Learning architecture for healthcare consortia

  Details PDF

• A generic framework for privacy preserving deep learning

  Details PDF Code

• Reproducible large-scale neuroimaging studies with the OpenMOLE workflow management system

  Details PDF Code Dataset Project

• Where is Trust in the age of no Trust? - Hardware vs. Software-Based Trusted Compute Approaches

  Tue, Sep 7, 2021, EEA Trusted Compute WG Monthly Webinar

• No Country for Old Data - Data Valuation Considerations for AI/ML

  Wed, Aug 25, 2021, IEEE Healthcare: Blockchain & AI Virtual Series presents Healthcare Data Valuation – Aug 25, 2021

• Overcoming data security and privacy challenges with federated learning

  Wed, Oct 21, 2020, Digital Catapult masterclass

• Secure Computing solutions for Healthcare

  Wed, Oct 14, 2020, City University London, UK - CitAI seminar series

• Privacy-preserving AI in Healthcare

  Wed, Jul 22, 2020, AI for Health News

• Convergence of Blockchain and Secure Computing for Healthcare solutions

  Wed, Sep 4, 2019, EU Blockchain forum

• Building an ecosystem of functional libraries for the OpenMOLE scientific platform: from batch jobs to automatic model parameter tuning

  Mon, Jul 10, 2017, ScalaX bytes meetup

• GridScale: a Journey from Object-Oriented to (More) Functional Programming

  Fri, Dec 9, 2016, Scala eXchange 2016

Imperial College London (UK)

PhD student supervion

• Dmitrii Usynin (2020-) - co-supervised with Ben Glocker (ICL) and Georgios Kaissis (TU Munich)
• Vasilis Siomos (2022-) - co-supervised with Giacomo Tarroni (City, University of London)

Labs and tutorials

• Computer Graphics: OpenGL
• Functional programming in Haskell: Introduction to functional programming
• Java: Introduction to the language

Student projects supervised

• Various undergraduate and MSc projects supervised
• Corporate Partnership Programme Prize in 2020 the Department prize for excellence in project work to Dmitrii Usynin (MEng Computing 4 - now PhD student), Privacy-Preserving Machine Learning in a Medical Domain
• Corporate Partnership Programme Awards in 2019 the Department prize for excellence in project work to Cristian Matache (MEng Computing 4), Efficient Design of Machine Learning Hyperparameter Optimizers
• Corporate Partnership Programme Awards in 2019 the Department prize for excellence in project work to Maurizio Zen (MEng JMC 4), Improving Neural Architecture Search with Reinforcement Learning
• New Voice Media Prize in 2018 awarded for an outstanding individual project in the area of data science to Corentin Herbinet (MEng JMC 4), Using Machine Learning techniques to predict the outcome of professional football
• New Voice Media Prize in 2016 awarded for an outstanding individual project in the area of data science to Adrian Draghici (MEng Computing 4), Cloud computing for big data experiments

ISIMA (Engineering School, France)

Courses led

• Software Engineering 101: Git, Debugging with gdb
• C++: Unit Testing (gtest), Template Meta-Programming, Standard Library
• UML: Design Pattern, Class, State, Sequence Diagrams
• Java: Java 7, Swing
• Distributed computing: Cluster (PBS), Grid (EGI)
• GPU Computing: CUDA, SIMD architecture

Student projects supervised

• Various MEng projects supervised covering GPU parallelisation
• Some associated publications (1, 2)

MSc in Computer Science (Blaise Pascal University, France)

Courses led

• Introduction to parallel programming with CUDA

• Academic CV
• Resume

• j.passerat-palmbach@imperial.ac.uk
• https://meet.jit.si
• Huxley Building, 180 Queen's Gate, SW7 2RH