Biography

Jonathan is a research scientist at ConsenSys Health and Imperial College London.

His current research at ConsenSys Health focuses on privacy preserving machine learning and decentralisation. Jonathan is exploring the potential of bringing together techniques such as Federated Learning and Blockchains to build a new generation of healthcare systems. He is growing a strong expertise on Secure Computing (Trusted Execution Environments - TEEs, MPC, …) and Verifiable Computing (TEEs, Zero-Knowledge Proofs, …) with the goal to enhance machine learning deployments with these privacy preserving technologies.

At Imperial, Jonathan works in the BioMedIA group, under Prof. Daniel Rueckert, where he’s leveraging distributed computing for AutoML. This project encompasses model selection, neural architecture search and hyperparameter tuning using a various range of methods. This is done in collaboration with the Complex Systems Institute, Paris in the context of the OpenMOLE project.

• 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

• Group-wise parcellation of the cortex through multi-scale spectral clustering

  Details PDF Slides Code Dataset

• 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)

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 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