AI Software Evangelist at Intel. Adrian graduated from the Gdansk University of Technology in the field of Computer Science 7 years ago. After that, he started his career in computer vision and deep learning. As a team leader of data scientists and Android developers for the previous two years, Adrian was responsible for an application to take a professional photo (for an ID card or passport) without leaving home. He is a co-author of the LandCover.ai dataset, creator of the OpenCV Image Viewer Plugin, and a Deep Learning lecturer occasionally. His current role is to educate people about OpenVINO Toolkit. In his free time, he’s a traveler. You can also talk with him about finance, especially investments.

Alyona Galyeva is a Principal Engineer at Thoughtworks, Microsoft AI MVP, PyLadies Amsterdam organizer, MLOps and Crafts co-organizer.
Observe - Optimize - Learn - Repeat
Passionate about encouraging others to see different perspectives and constructively break the rules.
I found my joy in building, optimizing, and deploying end-to-end AI and Data Engineering Solutions.

Data Scientist at Printify

Bart Steverink holds a bachelor's degree in aeronautical engineering and a master’s degree in engineering and policy analysis. Driven by a deep desire to better understand how the world works, Bart started his career as an independent contractor, developing simulation models for use in the transportation, healthcare and renewable energy domains. Writing code had always been a major aspect of the development of simulation models and ultimately led to a career switch towards information architecture and software development. Over the past decade Bart founded multiple software oriented startups and is currently engaged as CTO and co-owner at Healthplus.ai. Bart lives in the east of The Netherlands with his wife and two sons.

Bart van Erp is one of the co-founders of Lazy Dynamics. His ambition is to bring theory into practice and to develop next-generation engineering systems.

Bart is also working as a PhD-student in the BIASlab research group at the Eindhoven University of Technology, where he works on creating novel Bayesian algorithms for audio processing, alongside his teaching activities.

Data Scientist at Royal FloraHolland

See chrislaffra.com

Dr. Rackauckas is a Research Affiliate and Co-PI of the Julia Lab at the Massachusetts Institute of Technology, VP of Modeling and Simulation at JuliaHub and Creator / Lead Developer of JuliaSim. He's also the Director of Scientific Research at Pumas-AI and Creator / Lead Developer of Pumas, and Lead Developer of the SciML Open Source Software Organization.

Dr. Rackauckas's research and software is focused on Scientific Machine Learning (SciML): the integration of domain models with artificial intelligence techniques like machine learning. By utilizing the structured scientific (differential equation) models together with the unstructured data-driven models of machine learning, our simulators can be accelerated, our science can better approximate the true systems, all while enjoying the robustness and explainability of mechanistic dynamical models.

PhD in Eindhoven Technical University

Dorian is a Machine Learning Engineer at Dataroots, leveraging his background in Computer Science Engineering and over 5 years of industry experience across various client projects. His forte lies in implementing machine learning models in production while adhering to robust software practices.

Outside the realm of tech, Dorian finds solace and joy in diverse interests. He was a driving force of the AI music band Beatroots, where he explored the fusion of AI and musical creativity. Additionally, he enjoys expressing himself through punk rock songs on his guitar and occasionally blitzes chess moves online.

Sebastian is a Principal Data Scientist at Thoughtworks, with over 20 years of experience on the intersection of data, IT, and complex use cases. He has a background in Chemical Engineering and has used Python since the late 90s to automate tedious tasks (or just replace bash / Perl scripts).
Sebastian has led teams of modeling experts and architected machine learning solutions for a variety of clients, including Fortune 500 companies. He is also the founder and former CTO of a spin-off company that developed a SaaS product for data-driven optimization of continuous manufacturing processes. Sebastian exited the company in 2021 and is now passionate about helping other organizations leverage data and technology to solve their most challenging problems.

Machine Learning Engineer at Xebia Data.

At Dexter Energy, Inge, a data scientist, is developing machine learning-powered products for short-term power trading optimization. Involved since the start of the product, she contributes to probabilistic time series forecasts and overall product development.

Ionut is lead of data science at Bright Cape, a data solution consultancy company in Eindhoven. He is passionate about leveraging data and AI technologies to help companies optimize their operations.

Before joining Bright Cape, Ionut was with McKinsey & Company where he leveraged his data science expertise to serve multinational organizations in various industries and geographies. Back in the Brainport region, he is excited to collaborate with companies to help them advance on their data and analytics journey.

Jeroen is a Machine Learning Engineer at Xebia Data (formerly GoDataDriven), in The Netherlands. Jeroen has a background in Software Engineering and Data Science and helps companies take their Machine Learning solutions into production.
Besides his usual work, Jeroen has been active in the Open Source community. Jeroen published several PyPi modules, npm modules, and has contributed to several large open source projects (Hydra from Facebook and Emberfire from Google). Jeroen also authored two chrome extensions, which are published on the web store.

Hope to see you at PyData Eindhoven 🇳🇱! 👋🏻

Laurens Schinkelshoek completed his bachelor in Physics and afterwards started working in IT. While working for the IT department in a hospital he got familiar with IT systems and data structures in healthcare. When he learned about machine learning he saw the huge potential this technology could offer to decrease the workload of healthcare practitioners and improve patient experience. At the same time the hurdles to get this new technology ready for use were huge. Now he works as a Data Scientist for Healthplus.ai on besting these hurdles and making machine learning based products available in the clinic.

Maarten Breddels is an entrepreneur and ex-scientist mainly working with Python, C++, and Javascript in the Jupyter ecosystem. He is the creator of Solara, ipyvolume, and Vaex and Co-founder of Widgetti. His expertise includes fast numerical computation, API design, 3D visualization, and building data apps. He has a Bachelor's in ICT, a Master's, and Ph.D. in Astronomy, and he likes to solve real problems.

Marco Gorelli is a Senior Software Engineer at Quansight Labs, where he works on the Consortium for Python Data API Standards, pandas, and Polars

Hi there, I'm Max 👋,

I’m a Data Science & Engineering practitioner from Hamburg, Germany. I’m an avid open source contributor, author of machine learning & technology books, speaker and Coursera instructor.

I specialize in Deep Learning and its applications and can build machine learning solutions and data products from first prototype to production. As Ray contributor, DL4J core developer, Keras contributor, Hyperopt maintainer and author of a range of open source libraries, I have a distinct view on ML engineering and the data science ecosystem.

https://www.linkedin.com/in/mccmessmer/

I'm Mickey Beurskens, an AI engineer with a strong foundation in machine learning, robotics and software engineering. I have a free-thinking and creatively inspired approach, and I'm especially interested in deepening my understanding of autonomous agents with a focus on AI safety. Currently I work independently through through Forge Fire AI Engineering.

Patrick de Oude is a Senior Data Scientist at Albert Heijn, a leading Dutch supermarket chain, where he focuses on operation data science projects with a specific emphasis on reducing food waste. He has a strong background in artificial intelligence and has used these skills to drive data-driven initiatives at Albert Heijn.

Patrick De Oude received his Master's degree from the University of Amsterdam in 2006 and his PhD in 2010. His academic background and industry experience has equipped him with a strong foundation in machine learning, with specific interest in causal inference, experimention, reinforcement learning, probabilistic modelling and inference, which he applies to real-world problems at Albert Heijn.

Patrick De Oude's work at Albert Heijn aligns with the company's broader sustainability strategy. Albert Heijn aims to eliminate at least half of the waste across the food chain by 2030. Patrick De Oude's work is a critical part of this effort, using data science and artificial intelligence to drive food waste reduction initiatives.

I studied Mechatronics/Robotics and thus started of as a robotics engineer but soon found out I get much more energy out of creating the best developer experience for my colleagues. This focus let me to switch to prefix.dev where the is my only focus for the foreseeable future.

Stephan Sahm is working as a data scientist and engineer since 7 years, after studying Applied Stochastics and Cognitive Science.

Recently he founded Jolin.io, a company dedicated to support businesses with green data science tools and respective consulting.
For more prudence and care about resource consumption in data science.

My drive is to build robust end-to-end ML systems that can help take efficiency to the next level. In my role as a software architect for over 9 years at Prodrive Technologies, I have gathered experience developing industrial automation applications in C#/.NET, such as a fully automated robotic production line. This includes building software that communicates with both hardware (PLCs, equipment APIs) and other software platforms (MES, PLM). My specialization is in computer/machine vision, where I am responsible for new applications in the industrial automation domain.

In the past 3 years, I have been focusing more on the infrastructure and core SW applications required for ML applications using Python. We are building an AI framework consisting of both open-source and in-house developed code that can structure and automate ML application development (the MLOps process). The main application is automated visual inspection, where we want to provide technicians on the factory floor with explainable deep learning tools to automate the inspection themselves, with minimal need of data scientists and ML or software engineers.

Finally, I am responsible for developing the ML competence both within the company, with workshops, trainings, and pilot applications with students, and outside it with knowledge sharing sessions at conferences, cross-company work groups, and educational institutions like Fontys and Eindhoven University of Technology.

For the past 5 years I have been working as a Data Science consultant at Pipple. Since Pipple is active in multiple different sectors, I have had the opportunity to do many different projects. During this time I learned that I enjoy logistic projects the most, since they are often complex and require customized solutions every time. However, the most challenging project I did was not in logistics, but in the field of 3D modelling using Python. I would love to tell you more about this project and our solution method during the 2023 edition of Pydata Eindhoven.

Vincent D. Warmerdam is a software developer and senior data person. He’s currently works over at Explosion to work on data quality tools for developers. He’s also known for creating calmcode.io as well as a bunch of open source projects. You can check out his blog over at koaning.io to learn more about those.

As an Engineering Lead with 7 years of experience, I specialize in projects that involve machine learning, software engineering, and cloud infrastructure. I am known for my practical and result-oriented approach, guiding customers through the entire lifecycle of data-driven projects, from requirement definition to production deployment and operations. My expertise lies in bridging the gap between innovative technologies and real-world applications, ensuring successful project outcomes. With a strong focus on delivering tangible results, I am skilled in leading end-to-end project execution, leveraging my deep understanding of machine learning, software engineering, and cloud technologies. My dedication to driving innovation and my ability to effectively collaborate with cross-functional teams make me an asset in delivering data-driven solutions that meet business goals.