Keynote Speaker: Daniel Stekhoven, M.Sc., Ph.D.

Daniel Stekhoven, M.Sc., Ph.D.
Managing Director Core Facility
ETH Zürich

Stekhoven has been with NEXUS Personalized Health Technologies, an ETH core facility for personalized medicine for nearly a decade. Currently, he is the managing director of the core facility and a senior scientist at ETH Zürich.

After completing his Ph.D. in mathematics and statistics from ETH Zürich, Stekhoven launched his own statistical consulting company Quantik AG, which offers consulting and services within the statistics domain.  

Stekhoven's interests include digital transformation, turning research into applicable tools and the future health ecosystem. He's also invested in improving clinical diagnostics, harmonizing global knowledge about molecular makeups of diseases and general statistical representation of results, be it visualization or numbers.

Presenting

Scripted Science: Translating Computational Reproducibility to the Laboratory Frontier

The foundation of robust science lies in its reproducibility. In the computational realm, open-source code and transparent methodologies have championed the cause of reproducibility, ensuring that data analyses are consistent, verifiable, and reproducible. But how do we infuse these principles into the tangible world of laboratory experiments? Enter lab automation and high-throughput screening. By adopting the logic and spirit of computational reproducibility, these technologies are scripting the once manual and nuanced processes of the lab into systematic, standardised procedures.

This evolution does not merely enhance the accuracy and consistency of experimental outcomes; it also paves the way for generating homogeneous data sets, essential fodder for AI and machine learning applications. This talk will illuminate how the principles that revolutionised computational analyses are now reshaping the laboratory landscape, ensuring that the science we conduct not only stands the test of time but also serves as a sturdy scaffold for future innovations in machine learning. Join us in exploring how the tenets of reproducibility in the digital domain are echoing in the halls of physical experimentation.