Interview with Speaker Lukas Widmer

Lukas Widmer is Senior Principal Statistical Consultant at Novartis Pharma AG.

Could you tell us a little bit about your professional background?

From young age I had an interest in computer technology. Initially that led me to work in software engineering while studying Computer Science at ETH Zürich, after which it became clear to me that some of the most challenging and impactful applications where I could contribute were in life science and healthcare – here some exposure to the field of medicine through my family definitely had an influence. This led to me to pursue an MSc degree in Computational Bioinformatics at ETH Zurich and UC Santa Barbara and a PhD degree at ETH Zürich in Basel. In my PhD I focused on more interdisciplinary work; interfacing statistical / computational modelling and simulation to further understanding of basic biology, with the long-term goal to improve treatments. The desire to have more immediate impact in life science brought me to the Advanced Exploratory Analytics group – part of Advanced Methodology and Data Science at Novartis – which I joined in 2019. I joined Novartis with the double mission to drive the use of innovative methodology – such as data science, modelling and machine learning – across drug development, to deliver science-based progress to our patients and to re-imagine medicine.

What will you be speaking about at the SDS2021?

I will be discussing the importance of developing, propagating and applying Good Data Science Practice in the Data Science community in general, and in healthcare and pharma in particular. There have been several recent examples that highlighted the need for this, such as introduction of unwanted (and potentially unnoticed) bias which could impact patients in an unintended manner in Covid risk prediction or bias introduced into melanoma recognition using deep learning when not accounting for surgical skin markings. That any holistic approach to human research must be built on a solid ethical foundation has also been a current point of discussion in Computer Science, through the banning of University of Minnesota from making any further Linux Kernel contributions and the following apology, highlighting our duty to protect human subjects in research. We will discuss the need for Good Data Science Practice from multiple perspectives in the pharmaceutical industry and beyond, and we look forward to your thoughts and questions.

Why is the SDS conference important?

The Swiss Conference on Data Science is a great platform for exchange on current developments and issues in the data science space in industry and academia across Switzerland. There is a lot of excellent research and development going on both at universities and companies, and I find that having a good and critical discussion and dialogue (for example at SDS) is a good seed for collaboration and innovation. Having a direct line to people on the ground – subject matter experts – seems to be one of the key success factors, so I am looking forward to a diverse conference program.

data innovation alliance provides a significant contribution to make Switzerland an internationally recognized hub for data-driven value creation.