Events Calendar
Welcome to your calendar for data science events! Dive into a curated list of courses, conferences, seminars, workshops, and key deadlines. Tailor your search to match your interests by adjusting the event category filters. For those specifically looking for PhD courses, simply modify the filter settings to include these events as well. Stay connected and up-to-date with the latest in data science, all in one place.
Applied AI Lunch Talks at DTU Skylab & Online
For this last Applied AI Lunch Talk session before Christmas, we meet up in DTU Skylab’s Auditorium (Centrifugevej 373D, 2800 Kongens Lyngby) or online. You can bring your own lunch (or buy it before the talk at the Skylab cafe). Or you can sign up to receive the link for the online live-streaming: https://dtu.events/appliedailunchtalks/signup
Talk: Unlocking data-driven innovation: how Lean Startup meets Agile to drive AI success at Danish Crown.
AI and machine learning hold huge business potential — but adoption often creates complexity across R&D, quality assurance, and beyond. In this session, Danish Crown’s Head of Data Science shares how blending Lean Startup principles with Agile methods can unlock data-driven innovation from the bottom up. Discover how to balance structure with creativity, foster experimentation, and turn challenges into opportunities. With real-world lessons from Danish Crown’s journey, this talk offers practical insights on building an innovative culture that drives impact across industries.
Speaker: Francesc Joan Riera, Head of Data Science, Danish Crown
Francesc Joan Riera has more than a decade worth of applied experience in computer vision and machine learning, as well as 7+ years in cloud development and project management. He has developed algorithms, ML models, and established MLOPs practices at Terma A/S, the LEGO Group, and Danish Crown A/S.
Talk: AI Before Birth: Rethinking Prenatal Care
Talk description coming soon
Speaker: Aasa Feragen, Professor at DTU Compute
Professor Aasa Feragen is at DTU Compute and co-lead of the Causality & Explainability collaboratory within the Pioneer Centre for AI. Her research integrates medical imaging, fairness, and explainability. She co-founded Prenaital, a spin-out leveraging AI to detect high-risk pregnancies earlier and improve prenatal diagnostics.
