We are pleased to announce the following keynotes at EuroSciPy this year.
JAX and Flax: Function Transformations and Neural Networks
Andreas Steiner - Google
After his original studies in medicine, an MSc in bio-electronics, and MD with the Swiss Tropical Health Institute, Andreas has been working at Google since 2015. His main focus there has been on machine learning using Tensorflow and data mining, development of internal tools for data analysis.
Modern accelerators (graphics processing units and tensor processing units) allow for high performance computing at massive scale. JAX traces computation in Python programs through the familiar numpy API, and uses XLA to compile programs that run efficiently on these accelerators. A set of composable function transformations allows for expressing versatile scientific computing with an elegant syntax.
Flax provides abstractions on top of JAX that make it easy to handle weights and other states that is required for solving problems using neural networks.
This talk first presents the basic JAX API that allows for computing gradients, compiling functions, or vectorizing computation. It then proceeds to cover other parts of the JAX ecosystem commonly used for neural network programming, such as basic building blocks and optimizers.
Supercharging Open Data with Open Privacy
Katharine Jarmul - Thoughtworks
Katharine Jarmul is a Principal Data Scientist at Thoughtworks Germany focusing on privacy, ethics and security for data science workflows. Previously, she has held numerous roles at large companies and startups in the US and Germany, implementing data processing and machine learning systems with a focus on reliability, testability, privacy and security. She is a passionate and internationally recognized data scientist, programmer, and lecturer.
Privacy is becoming an increasingly pressing topic in data collection and data science. Thankfully, Privacy Enhancing Technologies (or PETs) are maturing alongside the growing demand and concern. In this keynote, we’ll explore what possibilities emerge when using Privacy Enhancing Technology like differential privacy, encrypted computation and federated learning and investigate how these technologies could change the face of data science today.