Biography

I am a research scientist at ConsenSys and Imperial College London.

My current research focuses on privacy preserving machine learning and decentralisation. I’m highly interested in leveraging the potential of P2P and Web3 technologies.

During my PhD I’ve studied the distribution of pseudorandom numbers on GPUs during my PhD in High Performance Computing, under the supervision of Prof. David R.C. Hill and Dr. Claude Mazel.

At Imperial, I’ve worked in the BioMedIA group, under Prof. Daniel Rueckert, in the developing Human Connectome Project. I’ve explored the application of distributed computing and software engineering medical image analysis and in particular large neuroscientific datasets. I’ve developed a strong interest for reproducible research and made contribution regarding data sharing and re-execution of complex scientific pipelines.

• 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

• 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
• 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