Conference & event organisation

PyData Amsterdam 2024 (as co-chair) [Aftermovie] [Youtube playlist]
PyData Amsterdam 2023 (as co-chair) [Aftermovie] [Youtube playlist]
PyData Global 2021 (as co-chair) [YouTube playlist]
PyData Festival 2020 (as co-chair) [YouTube playlist]
PyData Amsterdam 2019 (as co-chair) [YouTube playlist]

Now involved in an advisory role (2025–present).

Competitions (selection)

Data-Centric AI competition, winner in ‘Most Innovative’ category hosted by deeplearning.ai [write-up] [NeurIPS talk]
Kaggle Data Science Bowl 2017, 3rd place with Aidence on Lung Cancer Detection in CT scans hosted by Kaggle

Talks (selection)

Interview w/ Guido van Rossem, interviewer @ PyData Festival [link]
Amsterdam AI Impact Festival, panelist
Help! There Are Humans in My Data!, talk @ PyData Amsterdam 2025 [link]
The Data That Shapes Foundational LLMs, talk @ PyData Global 2024
Introduction to Data Storytelling, tutorial session @ PyLadiesCon 2024
How to build a Large Language Model from scratch, Women in Data 2024
Data For Social Good, talk @ PyData Amsterdam 2024
Data Storytelling through Visualization, talk @ PyData London 2023, Global 2022, Eindhoven 2022 [link] [slides]
Tech Talk 101, talk @ PyData Global 2022 – Impact Scholarship Programme Session [slides]
The Why of Data-Centric AI, talk @ PyData London 2022 [link] [slides]
Data-centric AI competition, talk @ NeurIPS 2021 [link]
Serious Time for Time Series (it’s time to take time series seriously!), tutorial @ PyCon US 2021, PyData Berlin 2022, Applied Machine Learning Days 2022 [link]
Unpacking the “Black Box”: How to Interpret your Machine Learning Model, tutorial @ Applied Machine Learning Days 2022 [link]
Equivariance in CNNs, talk @ PyData Berlin/PyCon DE 2019 [link]
An Alternative to Data Augmentation, talk @ PyData Amsterdam 2019
Data-efficiency in Medical AI, talk @ PyData Amsterdam 2019 (meetup) [slides]
Towards learning with limited labels: Equivariance, Invariance, and Beyond, talk @ International Conference on Machine Learning (ICML) 2019

Research

From my time at Cohere:

  • Team Cohere, Command A: An Enterprise-Ready Large Language Model Technical Report, 2025. [link]

From my studies (MSc Artificial Intelligence @ University of Amsterdam):

  • M. Winkels & T.S. Cohen, Pulmonary Nodule Detection with 3D G-CNNs. Medical Image Analysis Journal, 2019. [arxiv]
  • M. Winkels & T. S. Cohen, 3D Group-Equivariant Neural Networks for Octahedral and Square Prism Symmetry Groups. ICML Workshop, 2018.
  • M. Winkels & T. S. Cohen, 3D G-CNNs for Pulmonary Nodule Detection. International Conference on Medical Imaging with Deep Learning (MIDL), 2018. [arxiv]
  • M. Winkels et al., Challenge balancing for a kanji e-tutoring system. BNAIC, 2018.

Trainings (selection)

As a data science educator at Xebia Data Academy, I delivered >1000 hours of training and designed various curricula. Including, but not limited to, Python for Data Analysis, Data Science with Python, Advanced Data Science with Python, Advanced Python, Production-Ready Machine Learning, Clean Coding, Version Control & CI/CD, MLOps, Unsupervised Learning, Explainability, Data Visualisation & Storytelling, Deep Learning for Computer Vision, Deep Learning for Natural Language Processing.



PyData Amsterdam committee at the 2019 conference in the conference ballpit!