Abel Carreras
  • PhonoLAMMPS: Phonopy with LAMMPS made easy
Alejandro Saucedo

Alejandro is currently leading the research and development at the Institute for Ethical AI & Machine Learning as their Chief Scientist. With over 10 years of software development experience Alejandro has held technical leadership positions across hyper-growth scale-ups and tech giants including Eigen Tchnologies, Bloomberg LP and Hack Partners. Alejandro has a strong track record building departments of machine learning engineers from scratch, and leading the delivery of large-scale machine learning system across the financial, insurance, legal, transport, manufacturing and construction sectors (in Europe, US and Latin America). Alejandro has given multiple talks at international scientific, technical and business conferences, and has chaired panels & events with government ministers, senior executives and domain experts.

  • A practical guide towards algorithmic bias and explainability in machine learning
Alexander CS Hendorf

Alexander' professional career was always about digitalization: starting from vinyl records in the nineties to advanced data analytics nowadays. He's a Python Software Foundation fellow, program chair of Europe's main Python conference EuroPython, PyConDE and the scientific Python conference EuroSciPy. He’s one of the 25 mongoDB masters and a regular contributor to the tech community. As regular speaker at international conferences in he love to talk about, discuss and train tech.
Being a partner at Königsweg - a boutique consultancy based in Mannheim, Germany - he's advising and training industry clients in Ai, data science and big data matters.

  • Best Coding Practices in Jupyterlab
Alexander Pitchford

Currently working as postgraduate researcher in quantum control theory and optimisation algorithms. I am employed by the Mathematics Dept of Aberystwyth University. I am also associated with the Controlled Quantum Dynamics Group at Imperial College.
I am part of the Administration Team for QuTiP - the Quantum Toolkit in Python. I introduced the quantum control sub-library into QuTiP. Through this I also have close ties with the Theoretical Quantum Physics Lab at RIKEN

I spent the last 9 years doing undergraduate, then PhD Physics at Aberystwyth University. Prior to that I worked as a software developer / consultant in manufacturing simulation and finance process automation.

  • QuTiP: the quantum toolbox in Python as an ecosystem for quantum physics exploration and quantum information science
Alexandre de Siqueira
  • 3D image processing with scikit-image
Andrii Gakhov

Andrii Gakhov is a mathematician and software engineer holding a Ph.D. in mathematical modeling and numerical methods. He has been a teacher in the School of Computer Science at V. Karazin Kharkiv National University in Ukraine for a number of years and currently works as a software practitioner for ferret go GmbH, the leading community moderation, automation, and analytics company in Germany. His fields of interests include machine learning, stream mining, and data analysis.

The author of "Probabilistic Data Structures and Algorithms for Big Data Applications" (ISBN: 9783748190486)

  • Exceeding Classical: Probabilistic Data Structures in Data Intensive Applications
Antònia Tugores

Mathematician by formation, she spent most of her life developing software. She started collaborating with the creation of an open source game engine and framework in Tragnarion Studios. Later on, she moved to GridSystems and got involved in the development of an open source grid middleware. Some years later, she started working at IFISC (CSIC-UIB), a research institute. First, she was working on a grid project, but she got interested in data mining and now she is a data specialist working on human mobility and social sciences.

  • Tracking migration flows with geolocated Twitter data
Chandrasekhar Ramakrishnan

Chandrasekhar studied mathematics at the University of California, Berkeley (B.A. 1997) and art and computer science at the University of California, Santa Barbara (M.A. 2003). He has worked as a software developer and consultant for companies, research institutions, and NGOs in the US, Germany, and Switzerland. Since 2009, he has been at ETH Zürich supporting projects by developing software solutions for data management, analysis, and visualization. In addition to his work at ETH, he teaches data visualization at Propulsion Academy and, as Illposed works on artistic projects that incorporate data as a central component.

  • Reproducible Data Science in Python
Darya Chyzhyk

I’m Darya, researcher in artificial intelligence and machine learning, in particular feature selection, clustering, pattern recognition, segmentation and statistical analysis. During the last years I have been working on computer aided diagnostic systems for brain diseases that allow identification of the anatomical location of image biomarkers, lesion segmentation and phenotype prediction.

  • Controlling a confounding effect in predictive analysis.
David Liu
  • HPC and Python: Intel’s work in enabling the scientific computing community
Devin Petersohn
  • Modin: Scaling the Capabilities of the Data Scientist, not the machine
Devin Petersohn

Devin Petersohn

  • Modin: Scaling the Capabilities of the Data Scientist, not the machine
  • ToFu - an open-source python/cython library for synthetic tomography diagnostics on Tokamaks
Dr. Rebecca Bilbro

Dr. Rebecca Bilbro is a data scientist, Python and Go programmer, teacher, speaker, and author in Washington, DC. She specializes in visual diagnostics for machine learning, from feature analysis to model selection and hyperparameter tuning, and has conducted research on natural language processing, semantic network extraction, entity resolution, and high dimensional information visualization. An active contributor to the open source software community, Rebecca enjoys collaborating with other developers on inclusive projects like Scikit-Yellowbrick - a pure Python visualization package for machine learning that extends scikit-learn and Matplotlib to support model selection and diagnostics. In her spare time, she can often be found either out-of-doors riding bicycles with her family or inside practicing the ukulele. Rebecca earned her doctorate from the University of Illinois, Urbana-Champaign, where her research centered on communication and visualization in engineering.

  • Visual Diagnostics at Scale
Dr. Tania Allard

Tania is a Research Engineer and developer advocate with vast experience in academic research and industrial environments. Her main areas of expertise are within data-intensive applications, scientific computing, and machine learning. One of her main areas of expertise is the improvement of processes, reproducibility and transparency in research, data science and artificial intelligence.
Over the last few years, she has trained hundreds of people on scientific computing reproducible workflows and ML models testing, monitoring and scaling and delivered talks on the topic worldwide.

She is passionate about mentoring, open source, and its community and is involved in a number of initiatives aimed to build more diverse and inclusive communities. She is also a contributor, maintainer, and developer of a number of open source projects and the Founder of Pyladies NorthWest UK.

Tania has vast experience providing both workshops and talks all over the world, from big conferences such as PyCon to smaller user groups or interest groups. She is interested in both technical talks as well as talks covering community aspects.

  • Building data pipelines in Python: Airflow vs scripts soup
Emanuele Ghelfi

Machine Learning and Computer Vision Engineer @ ZURU Tech Italy

Emanuele received the M.Sc. Degree in Computer Science and Engineering at Politecnico di Milano with 110L/110 in December 2018. In particular, he followed the Artificial Intelligence (AI) track. The AI track includes courses like Game Theory, Machine Learning, Robotics, Image Analysis and Computer Vision, Autonomous Agent and Multi-Agent Systems and Natural Language Processing.
His thesis is located in the Machine Learning field, and more precisely in the Reinforcement Learning field. The paper from his thesis has been accepted at the International Conference on Machine Learning (ICML) 2019.

Since November 2018, he has been working as a Machine Learning and Computer Vision Engineer at Zuru Tech Italy.
Currently, he's working on Generative Models (GANs) and on Recurrent Models (LSTM). In addition, he deals with Computer Vision tasks applied to complex industrial processes.

GitHub: https://github.com/EmanueleGhelfi
Website: emanueleghelfi.github.io

  • Deep Diving into GANs: From Theory to Production with TensorFlow 2.0
Federico Di Mattia

Machine Learning & Computer Vision Engineer.

He received his MSc in Computer Science and Engineering at the University of Modena and Reggio Emilia. He spent a period working with the Computer Vision team at the Queen Mary University in London where he worked on his research thesis on a cognitive people tracker.

He worked on different Computer Vision related projects regarding security systems and worked on image processing algorithms. With a passion for psychology and negotiation, he tries always to get the best from people and the work environment.

Recently, working in Zuru, he could go in-depth in the research and studies of deep-learning algorithms applied to numerous areas using Tensorflow and Keras. During the last year, he had the chance to work on multiple tasks such as classification, segmentation, and anomaly detection.

Currently, he is working on Generative Adversarial Networks and many different Computer Vision tasks.

Twitter: @iLeW Github: https://github.com/iLeW

  • Deep Diving into GANs: From Theory to Production with TensorFlow 2.0
Francesc Alted

After more than a decade working in developing different Data Oriented libraries (PyTables, Blosc, bcolz), and High Performance Computing (numexpr) I am offering consulting and developer services for all the skills that I have cumulated through the years. I can also act as a teacher in Python and data handling; my courses can be tailored to the needs of the customer.

I am also an open source developer and highly interested in Data Oriented Programming. Most of my current work in this area happens at Blosc2 (https://github.com/Blosc/c-blosc2), and Caterva (https://github.com/Blosc/Caterva) with some maintenance work on existing PyTables and Blosc packages.

Areas of expertise: C and Python programming, compression, large databases, optimization, SQL, NoSQL.

Formal resumé: http://www.blosc.org/pages/francesc-alted-resume/

  • Caterva: A Compressed And Multidimensional Container For Big Data
Francesco Bonazzi

MSc. in physics from the University of Milano, Italy (2012).
Software engineer in the industry (2012-2015).
Researcher at the Max Planck Institute of Colloids and Interfaces, Potsdam, Germany (2015-2018).
Data scientist (2018-2019).

  • Matrix calculus with SymPy
Francesco Pierfederici

Launched genomics data processing platform for the largest food company in the world. Helped shoot satellites in orbit at NASA. Optimised numerical weather forecast models on 200k cores. Asteroid 22435 Pierfederici named in recognition of his contributions to astronomy software development. Author of "Distributed Computing with Python", 2016 PACKT Publishing.

Loves Python.

  • Driving a 30m Radio Telescope with Python
Gert-Ludwig Ingold

Professor for theoretical physics

  • Introduction to SciPy
Giovanni De Gasperis

Assistant Professor of Computer Systems and Cognitive Robotics

Python user since 2001. Core developer of Python3.7-based TaLTaC italian tool for text mining

  • A Tour of the Data Visualization Ecosystem of Python
Guilherme Jenovencio

Ph.D. candidate at Applied Mechanics TUM focused on Computational Solid Mechanics. Strong experience in Structural analysis and Optimization.

  • PyFETI - An easy and massively Dual Domain Decomposition Solver for Python
Guillaume Lemaitre

I am an engineer working for the scikit-learn foundation @ Inria.

  • The Rapid Analytics and Model Prototyping (RAMP) framework: tools for collaborative data science challenges
  • Introduction to scikit-learn: from model fitting to model interpretation
Jakub M. Dzik

Since 2011 I am a Scientific Programmer in Laboratory of Neuroinformatics (Nencki Institute).


  • MSc in Computer Science (2011; University of Wroclaw)
  • PhD in Neuroinformatics (2019; Nancki Institute)
  • Really reproducible behavioural paper
  • kESI - a kernel-based method for reconstruction of sources of brain electric activity in realistic brain geometries
  • kCSD - a Python package for reconstruction of brain activity
Javier Álvarez

Javier Álvarez is a researcher at the Workflows and Distributed Computing group of the Barcelona Supercomputing Center. His research interests include parallel programming models for distributed infrastructures and large-scale distributed machine learning. Javier received his Ph.D. in computer science from the University of Adelaide in 2018.

  • High performance machine learning with dislib
Javier Conejero

Javier Conejero is a Senior Researcher at the Barcelona Supercomputing Center. He holds a PhD on
Advanced Computer Technologies (2014) from the University of Castilla-La Mancha (UCLM), Spain.
During his PhD, he was awarded by the Ministry of Economy and Competitiveness (MINECO) of the
Spanish Government with a FPI fellowship grant. Previously, he worked at CERN for one year
(2009) into WLCG software development and management. Since 2015, he is a Senior Researcher
of the Workflows and Distributed Computing research group at the Barcelona Supercomputing
Center (BSC). He is leading the efforts on the PyCOMPSs binding at BSC. In 2016 he was awarded
by the MINECO with the Juan de la Cierva grant.

Javier lectured and ran practical exercises on PyCOMPSs development within the PATC:
Programming Distributed Computing Platforms with COMPSs tutorial annually since 2016. He has
also participated in PyCOMPSs tutorials in various conferences and workshops: EuroPython 2017,
CCGrid 2017, EuroPar2017 and SIAM 2018.

  • Parallelizing Python applications with PyCOMPSs
Jérémie du Boisberranger

I'm a software engineer at INRIA, essentially involved in scikit-learn, an open source Python library for machine learning.

  • Speed up your python code
Jeremy Tuloup

Scientific Software Developer at QuantStack

  • Debugging in JupyterLab
Joan Massich

I am a research engineer in the Parietal team at INRIA-Saclay working on human neuro-physiological data and machine learning. Contributing to open-source projects like: MNE-Python, OpenMEEG, scikit-learn, and others.

I obtained my PhD in computer vision applied to medical imaging, jointly from the Universitat de Girona and the Universite de Bourgogne France-Comte in 2013. After my PhD, and before coming to Parietal as an engineer, I was a postdoctoral fellow and teaching assistant at Universite de Bourgogne France-Comte.

I enjoy following technology trends, learning new skills, and sharing them. I also care about pedagogy and education as I strongly believe that any skill can be acquired by anyone with ease if transferred properly. This is why I have been involved in organizing pedagogical activities such as underwater robotics workshops for kids and enthusiasts, First LEGO League competitions, and lately software carpentry workshops.

  • MNE-Python, a toolkit for neurophysiological data
Joris Van den Bossche

I am a core contributor to Pandas and maintainer of GeoPandas. I have given several tutorials at international conferences and a course on python for data analysis for PhD students at Ghent University. I did a PhD at Ghent University and VITO in air quality research, worked at the Paris-Saclay Center for Data Science, and, currently I am a freelance software developer and teacher.

  • Introduction to geospatial data analysis with GeoPandas and the PyData stack
  • The Rapid Analytics and Model Prototyping (RAMP) framework: tools for collaborative data science challenges
  • Apache Arrow: a cross-language development platform for in-memory data
Josh Gordon

Josh Gordon is a Developer Advocate at Google, and teaches Applied Deep Learning at Columbia University and Pace University. You can find him on Twitter at https://twitter.com/random_forests

  • Hands-on TensorFlow 2.0
Jovan Veljanoski

Jovan is a senior data scientists & researcher at XebiaLabs, where he creates predictive models related to DevOps pipelines. Working mostly with Python in the Jupyter ecosystem, he has considerable experience in clustering analysis and predictive modeling. Jovan has a PhD in Astrophysics, is a co-founder of vaex.io, and is interested in novel machine learning technologies and applications.

  • Modern Data Science: A new approach to DataFrames and pipelines
Juan Luis Cano Rodríguez

Juan Luis Cano is an Aerospace Engineer based in Barcelona, Spain working as a Software Engineer at Satellogic, where he develops Python tools for geospatial data processing and scheduling algorithms for satellite operations. He also freelances for R&D Aerospace companies and Business schools, and in his spare time he contributes to open source, chairs Python España non-profit, rides his bicycle, listens to '70s British Hard Rock, and pursues impossible dreams.

  • Can we make Python fast without sacrificing readability? numba for Astrodynamics
Laura Mendoza

I was born in Strasbourg, France, but was raised in Guatemala city where I went to a French school. I did my undergraduate degree in the University of Strasbourg, where I obtained a bachelor degree in Mathematics with minor in Computer Science followed by a Master degree in Applied Mathematics specialized in Scientific Computing and Computer Science Security. Then, I obtained my Ph.D. in Numerical Methods in Plasma's Physics at the Technical University of Munich while I was based at the Max-Planck Institute for Plasma Physics. I want back to Strasbourg for a 2-year post-doc. Currently, I am working on the development and optimization of the ToFu library as a Research Engineering at the INRIA institute. This research is being funded by a 3-year Engineering grant of EUROFUSION obtained in June 2018.

  • ToFu - an open-source python/cython library for synthetic tomography diagnostics on Tokamaks
Lena Oden

Lena Oden recently became a Junior Professor for Computer Architecture at the FernUniversität Hagen. Before that, she worked as a postdoctoral researcher at the Forschungszentrum Jülich and at Argonne National Laboratory in the USA. She received her PhD in Computer Science from the Ruprecht-Karls-Universität Heidelberg and a Diploma in Electrical Engineering from RWTH Aachen. During her PhD, she worked at the Fraunhofer Institute for Industrial Mathematics. Her main research areas are Computer Architectures and Runtime Systems for HPC.
Her interest in Python started when she worked with people from other scientific areas. She likes the simplicity of Python, and started to use it as her main programming language for teaching parallel programming.
She is interested in improving the performance of Python, to make it more usable in HPC.

  • Lessons learned from comparing Numba-CUDA and C-CUDA
Maarten Breddels

Maarten Breddels is an entrepreneur and freelance developer/consultant/data scientist working mostly with Python, C++ and Javascript in the Jupyter ecosystem. Creator of ipyvolume and vaex, founder of vaex.io. His expertise ranges from fast numerical computation, API design, to 3d visualization. He has a Bachelor in ICT, a Master and PhD in Astronomy, likes to code and solve problems.

  • Modern Data Science: A new approach to DataFrames and pipelines
  • Dashboarding with Jupyter notebooks, voila and widgets
Marc Garcia

Marc Garcia is a pandas core developer and Python fellow.

He has been working in Python for more than 12 years, and worked as data scientist and data engineer for different companies such as Bank of America, Tesco and Badoo.

He is a regular speaker at PyData and PyCon conferences, and a regular organizer of sprints.

  • Introduction to pandas
Marco Bertini
  • High quality video experience using deep neural networks
Marta Kowalska

I am a PhD student at the Laboratory of Neuroinformatics at Nencki Institute of Experimental Biology. I work with methods for current source density reconstruction in a brain tissue.

  • kESI - a kernel-based method for reconstruction of sources of brain electric activity in realistic brain geometries
  • kCSD - a Python package for reconstruction of brain activity
Martin Bauer

Martin Bauer is a PhD student at the chair for system simulation at the University Erlangen-Nuremberg.
His research interests are CFD simulations with the lattice Boltzmann method, meta-programming techniques and high performance computing. He is one of the core developers of the waLBerla lattice Boltzmann framework.

  • pystencils: Speeding up stencil computations on CPUs and GPUs
Martin Renou
  • Dashboarding with Jupyter notebooks, voila and widgets
Matti Eskelinen

PhD student working on computational methods for hyperspectral imaging.

Logio @ FreeNode, IRCNet etc.

  • How to process hyperspectral data from a prototype imager using Python
Matti Picus

Matti is a core developer of PyPy, contributing to the internal numpy implementation _micronumpy and to the layer that allows python c-extension modules to run on the PyPy python interpreter. He has been active in the open source community both as a contributor, teacher, and presenter at conferences. Since April 2018, he works full-time developing NumPy, employed by BIDS

  • CFFI, Ctypes, Cython, Cppyy: how to run C code from Python
  • Inside NumPy: preparing for the next decade
Michele "Ubik" De Simoni

Lover of 🐧🐧. Pythonista 🐍. Machine Learning Engineer 🤖. Mad Scientist. Evil Mastermind. Walking Beard. Tinkerer. Nerd. Tech junkie.

Programming turned the tide of a crippling, panic-attacks inducing depression caused by a profound unsatisfaction with my choice of an academic career. During 2016 and 2017 I developed a burning passion for Python, robotics, Linux, Open Source/Hardware/Data/Science, and all things Machine Learning, I devoted myself day and night to learn the craft. My passion never waned and my drive toward knowing more grows each day.

Currently employed as a Machine Learning Engineer at zuru.tech, there I lead the research effort on GANs (and everything else relating to either the generative or the adversarial world of Deep Learning), help with Computer Vision and act as the Supreme Overlord of the Data Pipeline that feeds our AIs.

  • Deep Diving into GANs: From Theory to Production with TensorFlow 2.0
Mike Müller

CEO of hydrocomputing.

  • From Modeler to Programmer
Mike Müller

Founder and trainer of Python Academy

  • Getting Started with JupyterLab
Mikołaj Rybiński

I have MSc degrees in Mathematics and Computer Science as well as a PhD degree in computational Mathematics, all from the University of Warsaw (Poland). My theoretical and research background is backed up with many years of experience in industrial and scientific software development.

  • High Voltage Lab Common Code Basis library: a uniform user-friendly object-oriented API for a high voltage engineering research.
Miren Urteaga Aldalur


  • How a voice assistant works
Nathan Shammah

I work for the development of open-source software for quantum physics research and its role in quantum technology transfer. I am also interested in the study of quantum information processing and light-matter interaction in solid-state cavity quantum electrodynamics (QED). My research focus is on open quantum systems dynamics, and the interplay between cooperative effects and dissipative mechanisms in many-body quantum systems. In particular, I investigate how fingerprints of the ultrastrong coupling regime between light and matter can be addressed. I am also interested in the characterization of the light-matter physics in physical devices, such as superconducting circuits and semiconductor quantum wells, for technology applications such quantum information processing and Terahertz light emission.

  • QuTiP: the quantum toolbox in Python as an ecosystem for quantum physics exploration and quantum information science
Nicholas Del Grosso

Nicholas Del Grosso is an American neuroscientist post-doc in Germany who is passionate about open, reproducible science. Besides teaching data analysis and programming to scientists in courses, workshops, and at PyData Munich, he builds scientific software to study the learning process itself--from understanding the brain's responses to exposure to machine-brain interfaces, rat's understanding of 3D virtual environments, and scientists's responses to the stress of managing their own experiments!

Note: Nick is currently looking for a post-doctoral position to work on problems related to reproducible science! If you're looking for someone like him, send him a message or come say hello!

  • Scientific DevOps: Designing Reproducible Data Analysis Pipelines with Containerized Workflow Managers
Nick Radcliffe

Nick is a practising data scientist with over 30 years experience, from neural networks and genetic algorithms on parallel systems in the late 1980s, through parallel machine learning and 3D visualisation software as a founder of Quadstone, from 1995, to novel modelling methods (e.g. uplift modelling) in the early 2000s. Since 2007 , he has run Edinburgh data science specialists Stochastic Solutions.

Nick enjoys using his deep knowledge of underlying algorithms to fashion tailored solutions to practical business problems for clients including Barclays, Sainsburys, T-Mobile and Skyscanner, and has a particular interest in testing and correctness in data science.

  • Constrained Data Synthesis
Nicolas Cellier

Postdoct working in the Alps, mostly doing numerical support for the research. Specialized into PDE solving, I also have a strong numerical analysis background, and can use stat and machine learning tools.
I mainly do Python (for the last 8 years), but I can switch on other tool if I need to : lower level language as C or Fortran, or specialized one like R and Julia.

  • scikit-fdiff, a new tool for PDE solving
Olav Vahtras

Professor of Theoretical Chemistry at KTH Royal Institute of Technology, Stockholm, Sweden.
PhD in Quantum Chemistry 1992
MSc in Engineering Physics 1988

Research interest, method development in quantum chemistry. Coauthor of the Dalton Program package.

Involvement in the Python community as co-editor of Scipy Lecture Notes and instructor for Software Carpentry/Data Carpentry.

  • VeloxChem: Python meets quantum chemistry and HPC
Oliver Zeigermann

Oliver Zeigermann is a developer and consultant from Hamburg, Germany. He has written several books and has recently published the "Deep Learning Crash Course" with Manning. More on http://zeigermann.eu/

  • The Magic of Neural Embeddings with TensorFlow 2
Olivier Grisel

Olivier is a Software Engineer at Inria working on scikit-learn and related projects of the Python Data ecosystem.

  • Histogram-based Gradient Boosting in scikit-learn 0.21
  • Introduction to scikit-learn: from model fitting to model interpretation
Paige Bailey
  • Deep Learning without a PhD
Paolo Galeone

Computer Engineer + Machine Learning & Computer Vision researcher + Google Developer Expert in Machine Learning.

He received his MSc in 2016 with a thesis on the application of convolutional neural networks to the object detection and classification problems. After this, he took up research as a career and became a research fellow at the Computer Vision Laboratory at the University of Bologna, Italy, where he worked on a broad range of topics such as object detection, classification, coordinate regression, and anomaly detection. Currently, he leads the computer vision and machine learning department at ZURU Tech, Italy.

While in school, university and at work, he developed several projects spanning a broad range of topics such as database abstraction layers, a complete social network covering both the back-end and front-end aspects, several tools for machine learning developers and researchers with the aim to simplify the machine learning pipeline.
All his computer vision and machine learning projects have been implemented using the framework he loves: Tensorflow.

All these projects were completely open-source and are available on his Github profile at https://github.com/galeone.
He also blogs about Computer Vision, Machine Learning and Linux system administration. You can find his blog at https://pgaleone.eu/

  • Deep Diving into GANs: From Theory to Production with TensorFlow 2.0
Peter Andreas Entschev

Peter Andreas Entschev is a senior system software engineer in the AI Infrastructure group at NVIDIA, where he works on the RAPIDS stack, building GPU-enabled distributed software. Before NVIDIA, he worked on real-time computer vision systems for various applications. He holds an MSc in electrical engineering and applied computer science from the Federal University of Technology - Paraná, Brazil.

  • Distributed GPU Computing with Dask
Pierre Augier

Researcher in fluid dynamics at LEGI (Grenoble). Use Python a lot, in particular for the FluidDyn project.

  • Make your Python code fly at transonic speeds!
Pierre Glaser

Hi! My name is Pierre. I currently work as a research engineer in the Parietal Team at a French research institute called INRIA. You may know my team as we created many machine-learning and scientific computing libraries among which scikit-learn, joblib, nilearn and others. I am currently improving Python's multiprocessing tools across the whole scientific computing ecosystem. I notably contributed to scikit-learn, joblib, numpy, cpython, cloudpickle and many other libraries. You can follow me on twitter (https://twitter.com/PierreGlaser) and github (https://github.com/pierreglaser).

  • Recent advances in python parallel computing
Ricardo Manhães Savii

I am a former hotel manager; nowadays I am a student pursuing degrees as Computer Engineer B.S. and Computer Science M.S. I am a researcher and developer working in the fashion industry with Dafiti Group and Udacity’s reviewer for Machine Learning and Deep Learning Nanodegrees.

  • Deep Learning for Understanding Human Multi-modal Behavior
Rok Roškar
  • Reproducible Data Science in Python
Roman Yurchak

Roman Yurchak has a background in computational physics, and is currently working
as an independent consultant for data science related projects. He is also an open
source contributor to several Open-Source projects, mostly in Python.

  • vtext: fast text processing in Python using Rust
Ronan Lamy

I'm an open-source developer and consultant. I've been working on PyPy since 2012, with particular focus on the RPython annotator, Python 3 features, and cpyext.

  • PyPy meets SciPy
  • Astronomical Image Processing
  • From Galaxies to Brains! - Image processing with Python
Sarah Diot-Girard
  • What about tests in Machine Learning projects?
Sara Issaoun
  • In the Shadow of the Black Hole
Sebastian M. Ernst

I am a free scientist without specialization. I have more than one and a half decades of experience in various scientific disciplines, related data analysis & computing from embedded systems to super computers as well as the development of instrumentation (hardware & sensors). A lot of my work has evolved around geophysics, [aero-] space engineering and (many) related disciplines. It can be broadly described as data science with strong ties to the mentioned domains. Python has been a critical part of my work for more than twelve years.

  • Enhancing & re-designing the QGIS user interface – a deep dive
Simon Cross

Some potentially relevant facts about me:

  • I've lectured mathematics and MATLAB at the University of Cape Town.
  • I've worked in bioinformatics and radio astronomy.
  • I've published some academic papers (and even a couple in vaguely respectable journals!)
  • I have an undergraduate degree in physics and a masters degree in applied mathematics.
  • I accidentally wrote a lot of games in pygame.
  • In 2012 I started PyConZA (PyCon South Africa).
  • I have a Python Community Service award (woot!).
  • I currently lead a small data science team at a financial startup.
  • Performing Quantum Measurements in QuTiP
Sylvain Corlay
  • Data sciences in a polyglot world with xtensor and xframe
Tiberio Uricchio
  • High quality video experience using deep neural networks
Tim Hoffmann

Tim Hoffmann has been involved in several open source projects over time. Almost two years ago, he joined the matplotlib core development team with the mission to make matplotlib easier to use.

  • Effectively using matplotlib
Uwe Schmitt
  • master in mathematics 1994 at University of Saarbrücken, Germany.
  • PHD in applied mathematics since 2001 at University of Saarbrücken, Germany.
  • Postdoc position until 2008 at University of Saarbrücken
  • 2008-2014 working as software developer and data scientist for mineway GmbH
  • since 2014 senior software developer at Scientific IT Services of ETH Zurich, Switzerland.
  • emzed: a Python based framework for analysis of mass-spectrometry data
Valentin Haenel

Valentin is a long-time "Python for Data" user and developer who still
remembers hearing Travis Oliphant's keynote at the EuroScipy 2007. This was
during a time where he first became aware of the nascent scientific Python
stack. He started using Python for simple modeling of spiking neurons and
evaluation of data from perception experiments during his Masters degree in
computational neuroscience. Since then he has been active as a contributor
across more than 75 open source projects. For example, within the Blosc
ecosystem where he still maintains and contributes to Python-Blosc and
Bloscpack. Furthermore, he has acquired significant experience as a Git
trainer and consultant and had published the first German language book about
the topic in 2011. In 2014 and 2015 he helped kickstart the PyData Berlin
community alongside a few other volunteers and co-organized the first two
editions of the PyData Berlin Conference. He now works for Anaconda as a
software engineer / open source developer on the Numba project.

  • Understanding Numba
  • Create CUDA kernels from Python using Numba and CuPy.
Valerio Maggio

Valerio Maggio is a Data Scientist and Post-doc Researcher.
He has a Ph.D. in Computer Science from the University of Naples “Federico II”, and he is currently enrolled as
Research/Cloud Software Engineer at FBK/MPBA.
His research interests focus on Reproducible Science and Machine/Deep Learning methods for Computational Biology and Precision Medicine.
Valerio is also a very active fellow in the Italian Python community and member of the organising committee of many
Python Conferences (i.e. EuroPython, PyCon/PyData Italy, EuroSciPy).

  • Never get in a battle of bits without ammunition
Wolf Vollprecht
  • Data sciences in a polyglot world with xtensor and xframe
Yaman Güçlü

I am a post-doctoral researcher at the Max Planck Institute for Plasma Physics (IPP) in Garching, Germany, since 2014. I work in the division of Numerical Methods for Plasma Physics, where my research has focused on semi-Lagrangian methods for the gyrokinetic description of strongly magnetized plasmas.

I graduated in Aerospace Engineering at the University of Padova (Italy) in 2007, where I also obtained a Ph.D. in Science, Technologies and Measurements for Space in 2011. From 2011 to 2014, year when I moved to Germany, I was a post-doc at the Department of Mathematics of the Michigan State University (USA).

In my career Python has always been an invaluable language for fast prototyping of numerical algorithms, as well as for data visualization. The final code was usually written in C, C++, or Fortran.

Lately I have been progressively more interested in using Python also for high-performance scientific computing. Together with my colleagues at IPP, in the last few years I investigated how to ease the transition from prototype to production in academic research codes. PSYDAC, our Python parallel environment for spline finite elements, is the product of our recent efforts.

  • PSYDAC: a parallel finite element solver with automatic code generation
yoann audouin

In charge of the architecture and environment of the open source code TELEMAC-MASCARET (www.opentelemac.org) since 2012.
Discovered Python in 2010.

  • TelApy a Python module to compute free surface flows and sediments transport in geosciences
Zac Hatfield-Dodds

Zac is a researcher at the Australian National University’s 3A Institute, which is building a new applied science to 'manage the machines' - AI, cyber-physical systems, and other new technologies.

He started using Python to analyse huge environmental datasets, and contributing to libraries like Xarray to make such analysis easier for all scientists. Now, as a maintainer of Hypothesis, Pytest, and Trio, Zac is still passionate about making it easy to write software you can understand and rely on.

When not at a computer he can usually be found surrounded by books of all kinds, the Australian bush, or both.

  • Sufficiently Advanced Testing with Hypothesis
  • Sufficiently Advanced Testing with Hypothesis