Tereza Iofciu

Tereza Iofciu is data leadership coach and a data practitioner She has more than 15 years of experience in Data Science, Data Engineering, Product Management and Team Management. Alongside that she spent most of those years volunteering in the Python Community and wears many hats: PyLadies Hamburg organizer, Python Software Verband board member, Python Software Foundation Code of Conduct team member, Diversity & Inclusion working group member, PyConDE & PyData Berlin organizer, Python Pizza Hamburg organizer, and PyPodcats co-leader. In 2021 Tereza was awarded the Python Software Foundation community service award.

Jesper Dramsch

Jesper Dramsch works at the intersection of machine learning and physical, real-world data. Currently, they're working as a scientist for machine learning in numerical weather prediction at the coordinated organisation ECMWF.

Jesper is a fellow of the Software Sustainability Institute, creating awareness and educational resources around the reproducibility of machine learning results in applied science. Before, they have worked on applied exploratory machine learning problems, e.g. satellites and Lidar imaging on trains, and defended a PhD in machine learning for geoscience. During the PhD, Jesper wrote multiple publications and often presented at workshops and conferences, eventually holding keynote presentations on the future of machine learning in geoscience.

Moreover, they worked as consultant machine learning and Python educator in international companies and the UK government. They create educational notebooks on Kaggle applying ML to different domains, reaching rank 81 worldwide out of over 100,000 participants and their video courses on Skillshare have been watched over 128 days by over 4500 students. Recently, Jesper was invited into the Youtube Partner programme creating videos around programming, machine learning, and tech.

Anastasia Karavdina

My background is particle physics, where I was completely spoiled by access to large amounts of data and the freedom to try out every hot ML algorithm on it. The experiments I participated in were so-called large scale experiments (e.g Large Hadron Collider) and had from 500+ up to 2.5k other people working on them. So in addition to physics, I was exposed to the best software development practices that helped us to avoid a complete mess and destroy the Universe.

Afterwards I was working as Data Scientist in various fields and recently became "Solution Architect ML/AI and BI" at big enterprise company.

During my free time, I like learning new tools and techniques and implementing them in end-to-end AI/ML and IoT projects. My experience has also been very helpful in guiding data analysts, data scientists, and machine learning engineers as a mentor and contributing to the growth of the next generation of data scientist elite.

Guadalupe Canas Herrera

Guadalupe is a Theoretical Cosmologist working in understanding how the Universe began, how it evolved and what its ultimate fate could be. In particular, she is interested in studying alternative cosmological models with state-of-the-art astrophysical data using advanced statistical techniques and data science algorithms. Furthermore, she is interested in forecasting the performance of new experiments or new observables, for instance, Gravitational Waves.

She holds a Bachelor's in Physics from the University of Cantabria, and Master's and PhD degrees in Cosmology from Leiden University. Currently, she is a Research Fellow in Space Science at the European Space Agency. Moreover, she is an active member of the Euclid Consortium: the scientific group behind the data explotaition of the ESA Euclid mission. In particular, she is the maintainer of the code "Cosmology Likelihood for Observables in Euclid" or simply, CLOE. This software is part of the official data anlysics pipeline that will be eventually used to extract cosmological constraints of the Euclid data. Within the consortium, she is also co-leading the responsible group in charge of testing models beyond-Standard Cosmological Models to discernish the nature of Dark Matter or Dark Energy, or to test alternative inflationary models.