Keynote Speakers

At Danish Data Science 2022 we are happy to welcome the following line-up of confirmed Keynote Speakers. We will keep updating this page as we receive more confirmations.


Mihaela van der Schaar
John Humphrey Plummer Professor of Machine Learning, AI and Medicine at the University of Cambridge

Talk title: Machine learning for healthcare: Opportunities, Current Solutions and Next Frontiers

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London. In addition to leading the van der Schaar Lab, Mihaela is founder and director of the Cambrige Centre for AI in Medicine (CCAIM).

Mihaela was elected IEEE Fellow in 2009. She has received numerous awards, including the Oon Prize on PReventive Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

Personal webpage


Sara Magliacane

Assistant Professor at University of Amsterdam & Research Scientist at MIT-IBM Watson AI Lab

Talk title: Causality-inspired ML: What can causality do for ML?
Sara Magliacane is an assistant professor in the Informatics Institute at the University of Amsterdam and a Research Scientist at the MIT-IBM Watson AI Lab. She received her PhD at the VU Amsterdam on logics for causal inference under uncertainty in 2017, focusing on learning causal relations jointly from different experimental settings, especially in the case of latent confounders and small samples. She joined the MIT-IBM Watson AI Lab in 2019, where she has been working on methods to design experiments that would allow one to learn causal relations in a sample-efficient and intervention-efficient way. Her current focus is on causality-inspired machine learning, i.e. applications of causal inference to machine learning and especially transfer learning, and formally safe reinforcement learning.

Personal webpage

Sune Lehmann

Professor at the Technical University of Denmark and Adjunct Professor at the University of Copenhagen

Talk title: What do we do when our work might break society?

Sune’s work focuses on quantitative understanding of social systems based on massive data sets. A physicist by training, his research draws on approaches from the physics of complex systems, machine learning, and statistical analysis. He works on large-scale behavioral data and while Sune’s primary focus is on modeling complex networks, his research has made substantial contributions on topics such as human mobility, sleep, academic performance, complex contagion, epidemic spreading, and behavior on Twitter. He is the author of multiple high impact papers and his research has won various prizes. Since the start of the COVID-19 pandemic, he has served as a member of the task force established by the Danish government to model the spread in Denmark, and Sune is a newly appointed member of the Expert Group appointed by the Danish government to advise on their handling of tech giants, such as Google, Facebook, etc.

Personal webpage

Theofanis Karaletsos
VP of Data Science/Machine Learning at Insitro 

Talk title: From probabilistic modeling to drug discovery

Organised in collaboration with Pioneer Center for AI

Personal webpage


Mine Çetinkaya-Rundel

Professor of the Practice and Director of Undergraduate Studies, Department of Statistical Science, Duke University

Talk title: Welcoming learners to data science with the tidyverse

Mine Çetinkaya-Rundel is Professor of the Practice at Duke University and Developer Educator at RStudio. Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. Mine works on integrating computation into the undergraduate statistics curriculum, using reproducible research methodologies and analysis of real and complex datasets. Mine works on the OpenIntro project, whose mission is to make educational products that are free, transparent, and lower barriers to education. As part of this project she co-authored four open-source introductory statistics textbooks. She is also the creator and maintainer of <style=”color: #e9506e;”> and she teaches the popular Statistics with R MOOC on Coursera. Mine is a Fellow of the ASA and Elected Member of the ISI as well as the winner of the 2021 Robert V. Hogg Award for For Excellence in Teaching Introductory Statistics.

Personal webpage

Julien Simon

Chief Evangelist, Hugging Face

Organised in collaboration with Danish Data Science Community

Talk title: Hyperproductive Machine Learning with Transformers and Hugging Face

Julien is currently Chief Evangelist at Hugging Face. He’s recently spent 6 years at Amazon Web Services where he was the Global Technical Evangelist for AI & Machine Learning. Prior to joining AWS, Julien served for 10 years as CTO/VP Engineering in large-scale startups.

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