Abby is a Developer Advocate at Meta focused on supporting the Python Open Source community. She has worn a bunch of hats over the years - software developer, developer advocate, product manager, and people manager - but a consistent thread has been her passion for making technology approachable for all.

A mechanical engineer by training, I am now a software developer based in India, working as a project staff at IIT Bombay, Mumbai.

I have been a contributor to FreeCAD since 2016, where my focus has been mostly in the 2D constrained drawing workbench “Sketcher”, as well as the underlying solver “planegcs”. In further detail, my interest has been in adding support for general curved geometry.

I’m Akshita, a Computer Science undergraduate at IIIT Gwalior, and I’m really passionate about building systems that are both technically strong and meaningful in impact. A big part of my journey has been through open source. I also enjoy competitive programming and problem-solving. I’m particularly drawn to spaces where I can learn deeply, collaborate with others, and contribute meaningfully. Beyond tech, I’ve trained in Carnatic music for over 10 years and have won several competitions. Overall, I see myself as someone who takes initiative, brings clarity to a team, and is constantly looking to grow — not just as an engineer, but as someone who can create impact and uplift others along the way :)

Albert Dorador is an Adjunct Professor of Mathematics (Universitat Pompeu Fabra) and Statistics (BarcelonaTech), and leads a Research Lab focused on the development of cutting edge, inherently interpretable machine learning models for tabular data (Whitebox Lab). He holds a PhD in Statistics from the University of Wisconsin–Madison and previously served at the European Central Bank, specializing in financial risk management and machine learning applications. Albert is the creator of the TRUST and Renet algorithms and the maintainer of the trust-free Python library. His work focuses on the intersection of high-performance statisical modeling and auditable machine learning for high-stakes regulatory environments.

Currently working as Lecturer in the Mathematics Dept of Aberystwyth University. I am part of the Administration Team for QuTiP - the Quantum Toolkit in Python. I introduced the quantum control package into QuTiP. Through this I also have close ties with the Quantum Information Physics Theory Research Team

Prior to my current job, I completed a MPhys, then PhD Physics at Aberystwyth University. Before that I worked as a software developer / consultant in manufacturing simulation and finance process automation.

I got hooked on Python during my PhD in medical image analysis. I'm now an independent software engineer, taking an interest in (3D) visualization, bringing Python to the web, WebAssembly, and more. In the past few years my main focus is on growing wgpu and PyGfx.

I'm an open-source software developer with a background in computational linguistics and a contributor to scikit-learn.

Aris Nivorlis is a researcher geophysicist and data steward at Deltares, where he uses data and tooling to answer complex questions about the subsurface.
He is passionate about promoting good practices in data management and scientific coding, helping teams build sustainable and reproducible workflows.
Outside of work, Aris is actively involved in the European Python community, contributing to the organization and support of conferences and community initiatives. When he's not at his computer, you’ll likely find him dancing salsa.

Ashwin is a training coordinator for Mimer (https://mimer-ai.eu/), the AI Factory located in Sweden, which is part of the EuroHPC Joint Undertaking initiative. Ashwin has a background in Mechanical and Aerospace Engineering and during his Ph.D, he delved deep in to the ecosystem, by contributing to several active scientific Python projects such as FluidDyn, Transonic and Pythran. He has also participated as a speaker in PyCon Sweden and enjoys engaging with Python community through workshops, courses, conferences and meetups.

Bryce Adelstein Lelbach has spent over a decade developing programming languages, compilers, and libraries. He is passionate about parallel programming and strives to make it more accessible for everyone.

Bryce is a Principal Architect at NVIDIA, where he founded the Core C++ Compute Libraries team and now leads the Vanguard Programming group that drives NVIDIA's roadmap for programming languages, compilers, and core libraries.

He is a leader of the systems programming language community, having served as chair of the C++ Library Evolution and the US programming language standards committee. He has been an organizer and program chair for many conferences over the years. On the C++ committee, he has worked on concurrency primitives, parallel algorithms, senders, and multidimensional arrays.

He previously worked at Lawrence Berkeley National Laboratory and Louisiana State University. He is one of the founding developers of the HPX parallel runtime system.

Outside of work, Bryce is passionate about airplanes and watches. He lives in Midtown Manhattan with his girlfriend and dog.

After having a career as a Data Scientist and Developer Advocate, Cheuk dedicated her work to the open-source community. Currently, she is working as a developer advocate for JetBrains. She has co-founded Humble Data, a beginner Python workshop that has been happening around the world. Cheuk also started and hosted a Python podcast, PyPodCats, which highlights the achievements of underrepresented members in the community. She has served the EuroPython Society board for two years and is now a fellow and director of the Python Software Foundation.

Hi! I'm Daniel, a machine learning research engineer from Israel.

I love data and optimization, and had the opportunity to work on a variety of interesting problems, from analyzing brain signals to video-based 3D reconstruction.

I'm also fascinated with learning, and love teaching and creating interactive learning experiences.

I hold a B.Sc. in computer science from the Open University of Israel, and an M.Sc. in machine learning and data science from Reichman University, Israel.

I love music, the outdoors, and playing with my cat.

You can check out my blog to see what I'm passionate about.

Daniele is a data scientist with expertise in statistics, data science and AI, passionate about exploring the intersection of machine learning and financial markets. Since 2023, he is working at MDPI, one of the largest open-access publishers. A former national 400m sprinter.

Senior AI Engineer with 7+ years of experience architecting and deploying end-to-end ML solutions at scale. Specialized in NLP, Generative AI (LLM, RAG), Vector Search, and MLOps.

Research Data Scientist and Software Engineer at The Alan Turing Institute in London. I have worked for over a decade in scientific computing, in fields ranging from computational biology, environmental sciences, digital humanities and more.
I have also worked on a lot of Python packages!

Principal Scientist in the EPFL Center for Imaging
Core developer of spam, orientationpy, splinebox...

Elena Hernandez Martinez is an AI Researcher at the appliedAI Institute for Europe (TransferLab). She holds a Ph.D. from LMU Munich, where she worked on cosmological simulations of large-scale structure formation in the Computational Astrophysics Research Group. During a research stay at the Flatiron Institute (Simons Foundation), she developed machine learning methods for cosmological parameter inference from galaxy cluster data. Her interests span simulation-based inference, neural networks for scientific applications, and high-performance computing.

Research engineer at CNRS, France. Co-PI of the Fink Broker and co-founder of the SNAD collaboration and the Cosmostatistics Initiative (COIN). Work on the development of interdisciplinary science environments, machine learning applications to astronomy and adaptive learning techniques.

A computational physicist by training, Evgeni has been contributing to the SciPy ecosystem for over a decade.

Feichi Lu is a Data Scientist at MDPI in Basel, where she works on building data-driven analytics for scientific publishing. She holds a Master’s degree in Data Science from ETH Zürich. Her experience spans large-scale data analysis, semantic modeling, and applied AI.

Frank became a self-employed software developer and consultant while studying Physics in Freiburg. During his Masters, he specialized in data analysis for particle physics at CERN and obtained a doctoral degree in 2022 working with the ATLAS collaboration. Since 2023, he has been the AI Technical Leader and AI Engineer Lead at MDPI, one of the largest open-access publishers.

Third-year Applied Computer Science student at AGH University of Kraków. Interested in GPUs, HPC and Open-source software. Currently working as a C++ Developer @ digatus.

I'm napari (napari.org) core team member and Assistant Professor on University of Warsaw,

My background are Math and Computer Science.
As a daily task in napari project I focus on debug edge case, improving code efficiency and API to allow user focus on solving problems, not reinventing wheels.

Guillaume is an open-source software engineer working at :probabl. He is a core maintainer of the scikit-learn and imbalanced-learn libraries.

Inessa is building bridges between people, open source software, and open science. Over the years, she has launched and continues to support several educational initiatives focused on widening the open source contributor pipeline. Inessa is Open Source Program Manager at OpenTeams and guest faculty at University of Connecticut. She also serves on the NumPy Steering Council and the pyOpenSci Advisory Board. Inessa is perpetually fascinated by incentive design, collaborative intelligence, and jazz.

My name is Ishita, and I am a PhD student at the Technical University of Darmstadt. Alongside my doctoral research, I am independently developing BioCabinet as a personal project, separate from my PhD work.

I started this project with the goal of making genomic data analysis more accessible to wet-lab scientists, particularly those who face steep technical barriers when working with complex, multi-modal datasets. BioCabinet is designed to bring multiple analytical tools together in one place, offering users the flexibility to compare methods, explore results across modalities, and access integrated documentation directly within the analysis workflow.

A key motivation behind this project is to continuously evolve the platform alongside emerging technologies. This includes incorporating LLM-based chatbots trained on extensive biological and technical documentation, enabling researchers to ask questions, navigate tools, and better understand their data in an intuitive way.

Through this project, I aim to contribute a community-driven, up-to-date analysis framework that lowers the barrier to entry for advanced genomics research while remaining adaptable to future developments in the field.

Jacob Tomlinson is a senior Python software engineer at NVIDIA with a focus on deployment tooling for distributed systems. His work involves maintaining open source projects including RAPIDS and Dask. RAPIDS is a suite of GPU accelerated open source Python tools which mimic APIs from the PyData stack including those of Numpy, Pandas and SciKit-Learn. Dask provides advanced parallelism for analytics with out-of-core computation, lazy evaluation and distributed execution of the PyData stack. He also tinkers with the open source Kubernetes Python framework kr8s in his spare time. Jacob volunteers with the local tech community group Tech Exeter and lives in Exeter, UK.

Jan initially immersed himself in the realms of cognitive science and computational neuroscience. However, he couldn’t resist the siren call of Bayesian machine learning, and his PhD evolved into a mission to enhance the user-friendliness of this complex field. He set out to bridge cutting-edge methods with user-friendly software, making the world of simulation-based inference more accessible for practitioners. In 2024, he joined the appliedAI Institute for Europe, ready to continue his journey of making advanced methodologies approachable and transformative.

12-time award-winning AI lead and 'Sculpting Data For ML' author Jigyasa Grover drives rider personalization innovation at Uber after transforming Twitter/X, Facebook/Meta, Faire, and Bordo AI with large-scale ML systems. Handpicked by Google for their I/O 2024 keynote, she serves on Google's Developer Advisory Board while advising social search engine Diem and other Silicon Valley startups.

As a LinkedIn Learning instructor, Jigyasa educates thousands of professionals worldwide on cutting-edge AI-powered applications and agentic AI systems, solidifying her status as a thought leader in artificial intelligence education. As a Google Developer Expert, Women Techmaker Ambassador, and World Economic Forum Global Shaper, Jigyasa has also been featured in Forbes, Business Insider, VentureBeat, and International Business Times, and has elevated panels with Harvard University, Preston-Werner Ventures, Norwegian Business School, Humanitarian Frontier in AI, Women in Data, and more to her name.

The UC San Diego alumna has secured funding from the Canadian and Norwegian governments, the Linux Foundation, and multiple tech giants, enabling work that transcends geographical boundaries. With 200+ media features and contributions to open source recognized by Apache and Python Software Foundations, she mentors next-generation talent while shaping AI's future through advisory roles at Bezoku AI, Las Positas College, and various AI forums.

Jon is a Machine Learning Engineer specialized in IoT systems. He has a Master in Data Science and a Bachelor in Electronics Engineering, and has published several papers on applied Machine Learning.
He has been contributing to open-source software since 2010.

These days Jon is co-founder and Head of Data Science at Soundsensing, a leading provider of condition monitoring solutions for commercial buildings and HVAC systems.
He is also the creator and maintainer of emlearn, an open-source Machine Learning library for microcontrollers and embedded systems.

Jost is a Senior Research Software Engineer at King’s College London, where they work on software projects ranging from astrophysics to Trusted Research Environments for analysing medical data. They are a certified Carpentries instructor and regularly develop and deliver training courses, with a focus on Python and high-performance computing for researchers.

Jost received a PhD in astroparticle physics from the University of Sheffield and is a maintainer of several widely used supernova neutrino codes, including SNEWPY and sntools.

Dr. Katrina Riehl is a Principal Technical Product Manager at NVIDIA leading the CUDA Education program. For over two decades, Katrina has worked extensively in the fields of scientific computing, machine learning, data science, and visualization. Most notably, she has helped lead data initiatives at the University of Texas Austin Applied Research Laboratory, Anaconda, Apple, Expedia Group, Cloudflare, and Snowflake. She is an active volunteer in the Python open-source scientific software community and currently serves on the Advisory Council for NumFOCUS.

Following an Integrated Masters in Mathematical Physics at the University of Edinburgh I joined Blue Skies Space Ltd. (BSSL) as a Software Engineer. During my time I have mostly worked on simulation tools, including orbital analysis software and instrument performance simulators. Over the past year I have developed on-board software for our first satellite Mauve.

I joined the napari community during my PhD in structural biology, where I used and contributed to napari regularly, until I was invited to join the core team. I now work full time on napari as an independent contractor, improving and developing many of the features that I used or introduced during my PhD.

I'm a core maintainer of SciPy and Pixi, and a member of the Consortium for Python Data API Standards. I recently finished a master's degree in Computer Science and Philosophy at the University of Oxford.

Maciej Szymkowski is a Ph.D. in the field of Computer Science. From April 2025, he is working with Future Processing as a Senior Machine Learning Engineer. Previously he was with Bialystok University of Technology (Faculty of Computer Science, 2018-2026), Warsaw University of Technology (Faculty of Electronics and Information Technology, 2021-2022) and AGH in Cracow (Faculty of Physics and Applied Computer Science, 2021-2022). He gained his experiences also as a researcher in diversified projects and software developer/engineer in different companies (e.g., SoftServe, Symmetra, Transition Technologies, Hemolens Diagnostics). He was also with Łukasiewicz Research Network - Poznan Institute of Technology (2022-2024) where he was a Head of AI Development section. Maciej Szymkowski is an author or co-author of more than 45 research papers published in JCR journals, as chapters in the books or in international conference proceedings. His main research area is Computer Vision – especially in the field of medicine and transport. He is interested in Vision-Language Models (VLMs), Large Language Models (LLMs) and machine learning algorithms. In his free time, he loves to extend his knowledge, take a long walk, read a book or watch soccer, basketball or hockey. He is a fan of Legia Warsaw, Real Madrid (soccer), New York Knicks (basketball) and Pittsburgh Penguins (hockey).

Domaines science et structure de la matière, mesures physiques et micro-électronique informatique industrielle, ingénieur au CEA. Collaborations scientifiques dans le domaine des expériences plasmas laser-matière, instabilités paramétriques, instabilités hydrodynamiques, équations d’état. Contribution au programmes NIF et LMJ. Activités actuelles dans le cadre de la sécurité globale, radiographies à basse et haute énergies.

I'm a Software Engineer at Quansight, working on multitude of open source projects in the Scientific Python Ecosystem. You can find my GitHub profile here: https://github.com/mtsokol

• 2022, Bachelor in Physics, University of Vienna, Austria
• 2024, Master in Quantum Technology, Uppsala University, Sweden
• 2024 - present, active contributer to QuTiP
• 2025 - present, PhD candidate, Uni Luxembourg

Dr. Mike Müller has been working with Python since 1999 and teaching it professionally since 2004. As a trainer at Python Academy (https://www.python-academy.com), he has taught over 580 Python courses totaling more than 1,400 teaching days to thousands of participants worldwide.

Mike has taught more than 75 tutorials at Python conferences, including 29 tutorials at PyCon US over the years. He is known for his hands-on teaching approach, live coding demonstrations, and comprehensive course materials that participants can use as references long after the tutorial ends. His tutorials blend practical examples with solid theoretical foundations, making complex topics accessible and immediately applicable.

Beyond teaching, Mike is deeply involved in the Python community. He has organized conferences including PyCon DE, EuroSciPy, and numerous BarCamps. His contributions to the community have been recognized with the PSF Community Service Award and PSF Fellow status. He serves as chair of the German Python Software Verband.

Mike holds a doctorate in hydrology and brings a scientific perspective to programming education. He believes in learning by doing and creates supportive environments where participants feel comfortable asking questions and experimenting with code.

Dr. Mike Müller

Education

• German Diplom-Ingenieur Wasserwirtschaft (5 years) at University of
  Technology Dresden,
  Germany -- Wasserwirtschaft literal translation water management, engineering
  degree in water resources management with focus on groundwater hydrology and
  modelling
• MS in Hydrology and Water Resources at University of Arizona, Tucson, USA
• Ph.D. in Mining Hydrology at BTU Cottbus, Germany -- Development of a coupled
  surface water and groundwater model for open pit mine lakes (PITLAKQ)

Work Experience

• Combination of hydrology and software development
• Coupling of models
• Python teaching -- since 2004, >1500 full teaching days with focus on scientist and engineers

Model Coupling

I have experience in coupling different hydrological and hydraulic models, such as:

PITLAKQ

This is my Ph.D. work that couples a finite volume groundwater model (PCGEOFIM),
a hydrodynamic and water quality lake Model (CE-QUAL-W2), and a
hydro-geo-chemical model (PHREEQC).
PITLAKQ is open source.
It has been applied world wide.
I used it for pit lakes in Germany, Australia, and Canada.
Others have used it in many other countries of the world.

Coupling of a river flood model and a groundwater model

I have been involved in a research project that couples a river model, a sewer
pipeline model and groundwater model for the river Elbe in the city of Dresden,
Germany.
I was responsible for the coupling of the river model and the groundwater
model.

Rainfall runoff model -- groundwater model

I implemented a coupling of a rainfall runoff model (ArcEGMO) and a groundwater
model (PCGEOFIM) for a watershed in Germany.

Density-driven flow in groundwater and lake

I coupled a density-driven groundwater flow and transport model (MODMST) to
PITLAKQ.
This was used for a long-term simulation of a sub-aquatic landfill,
i.e. a lake over a deposit of mining waste.

MODFLOW with dynamic boundary conditions - pymf6

I am the developer of pymf6
that allows to interact with MODFLOW 6 via Python at runtime.
This can be used to implement dynamic boundary conditions.
Examples are:

• water-level-controlled wells that dynamically adjust their pumping rates
  based on simulated water levels in the aquifer
• dynamic values of the resistance of the colmation layer at the river bottom
  that depend on the flow direction between river and aquifer
• technical heat boundary conditions in urban settings such as building
  basements and tunnels

MODFLOW 6 with AEM

I coupled an Analytic Element Model (AEM)
TTim with MODFLOW 6
via pymf6.
This allows to combine the grid-based approach of MODFLOW with the analytic,
grid-less approach of AEMs.

MODFLOW 6 with PHREEQC -- rtmf6

I coupled MODFLOW 6 with the geochemical model PHREEQC via
PhreeqPyusing
PhreeqcRM.
I am the author of PhreeqPy.
The result is rtmf6.

I hold a Joint Masters Degree in Advanced Solid Mechanics from the National Technical University of Athens, Università della Calabria, École Centrale de Lille, and Université de Lille.

I currently work as an R&D Software Engineer for Synopsys and I currently contribute to multiple projects within the PyAnsys OSS ecosystem.

Niels is a software engineer, currently working for a treasury startup in Amsterdam. Simon is a data scientist and engineer, currently working as tech lead at ING Bank. Both have experience at the intersection of software engineering and data science within the fintech domain.

Nitish Agarwal is a Senior Engineering Manager at GoDaddy with 14+ years of experience in cloud architecture and AI. He has previously led engineering teams at Skyscanner, Expedia, and Balena, delivering high-availability systems for 80M+ users. Nitish specializes in scaling teams, optimizing distributed systems, and currently leads GoDaddy's AI transformation strategies for customer care.

Paweł Tokaj is a staff software engineer at Splunk and a PMC member of the Apache Sedona project who enjoys writing reliable, efficient software that helps others. His love for geospatial data started at the Warsaw University of Technology, where he graduated in geodesy and cartography.

Paweł’s primary focus areas are distributed databases and systems, cloud computing, and geospatial data processing. He believes that open source projects make knowledge more accessible; he has contributed to Apache Sedona, Open Lineage, and Airbyte. He attends various conferences or meetups where he shares his knowledge as a speaker or participant. He is a technology nerd, spending a lot of his spare time reading books and articles and developing open source software.

Pierre Raybaut is a physicist and software engineer — with a background in optics and photonics engineering and a PhD in femtosecond lasers — known for creating Spyder, the Scientific Python IDE, as well as Python(x,y) and WinPython, tools that helped establish Python as a first-class language for scientific computing.

He started his career as a research engineer at THALES Avionics, then spent over a decade at the French Alternative Energies and Atomic Energy Commission (CEA) as lead software developer, project manager for the Laser Mégajoule timing and fiducial system, and head of a research laboratory. Since 2018, he has been at CODRA, an industrial software company based in France, where he serves as Executive Vice President.

Pierre remains an active open-source contributor. Beyond Spyder, he created guidata, PlotPy, the PlotPyStack ecosystem, and DataLab, an open-source platform for scientific and technical data processing and visualization.

Hello! I'm Ramon, a systems engineer and educator living in Sydney. I currently work at Canva on the AI Ops team within the Content and Delivery division. Previously, I was a research engineer at Menlo Labs building tools to run AI models on robots and constrained devices, and before that a Senior Product Developer at Decoded, a technology education company based in the UK where I created custom data science tools, workshops, and training programs for clients in industries ranging from retail to finance. Prior to that, I held roles at the intersection of education, data science, and research in the areas of entrepreneurship and strategy. On the personal side, I enjoy giving talks and technical workshops and have had the privilege of participating in several conferences such as PyCon, SciPy, CppNow, PyData, and countless meetup events. In my spare time, I spend as much time as possible mountain biking and exploring many of the outdoor wonders Australia has to offer.

I am a research engineer at Inria, part of P16 and of the SODA research team. I am one of the maintainers of the skrub Python package. I hold a PhD in Computer Science and I am also interested in research on tabular learning and tabular foundational models.

Ukrainian Software and Platform Engineer based in Poland. Former Lead Engineer and Architect at Fortune 500 companies. Core areas: software development and cloud computing. Public speaker at EuroPython, PyCon CZ, PyCon LT, PyCon PL, Python Summit, Pytech Summit, and PyCode.

Ryan C. Cooper is an Associate Professor-in-Residence at the University of Connecticut. His background is in mechanics and materials science with an emphasis on numerical simulations and engineering education. He has been using Jupyter and GitHub to enhance the classroom experience for over six years. Prof. Cooper has developed and free open source materials for computational work in engineering and volunteered with the NumPy documentation team SciPy track chair. Ryan is an integral part of the AI in the School of Engineering committee. He has a Ph.D. from Columbia University and spent two and a half years at Oak Ridge National Laboratory as a Postdoctoral researcher.

Dr. Ryan Curtin is an independent researcher and open-source software developer, leading the development and maintenance of several packages in the C++ scientific software ecosystem. During his Ph.D. at Georgia Tech he focused on the formalization of dual-tree algorithms, a class of geometric branch-and-bound algorithms that can be used to solve subproblems relevant to machine learning techniques. These algorithms underlie the efficient mlpack C++ machine learning library, which he has
led since 2010. In his free time, he races go-karts, so he never escapes from trying to go fast in one way or another.

Shubham Sharma is a Senior Data Scientist with more than ten years of experience at the convergence of Remote Sensing, Image Processing, Computer Vision, and Deep Learning. He has been an active contributor to the open-source scientific computing ecosystem, engaging with communities through conferences such as SciPy and as a past speaker at PyCon. His work includes significant experience in Synthetic Aperture Radar (SAR) image processing, and he is deeply committed to advancing knowledge of satellite image analysis using Python within the open source community.

Stefanie is an open-source developer and maintainer of scikit-learn, contributing also to related libraries. She trained and taught at LeWagon (2022–2023), interned with scikit-learn (2023), and worked at muffintech (2023) before joining probabl’s open-source team in 2024. She holds a PhD in History from the University of Potsdam (2021) and was active on Wikipedia as a writer and mentor (2011–2014).

I lead the open-atmos-krk research software engineering team at AGH University in Krakow, where we develop and maintain several Python packages, including Numba-MPI, PySDM, PyMPDATA, and PyPartMC. Before joining AGH, I worked as a postdoc at the University of Illinois Urbana-Champaign and at Jagiellonian University. During a three-year break from academia, I worked as a quant-dev in the financial sector. I completed my MSc and PhD in physics at the University of Warsaw.

I am a full-time maintainer and community manager of napari, an interactive multi-dimensional Python image and data viewer, and its plugin ecosystem. I work to extend the plugin ecosystem and help scientists achieve their goals with image analysis.

• Currently interested in AI Safety and Alignment Research | Writes at https://permutedsense.substack.com/
• Open source and privacy-preserving tools enthusiast

Previous experience working as a data scientist on varied business propositions ranging from detecting scientific fraud in publishing, supply chain optimization, customer attrition, upselling/cross-selling card products, web personalization and customer-merchant affinity.

Wolf Vollprecht has been active in the Python open source community for the past 5 years. He is a core member of conda-forge and the conda steering council, and the original author of the mamba package manager. He also has extensive experience in high-performance C++ and Rust. 2 years ago he started prefix.dev where the team is focusing all efforts on making cross-platform, language independent package management great (on top of the conda ecosystem).