Speaker
Description
Joachim M. Buhmann is a professor emeritus for Information Science &
Engineering at ETH Zurich (2003-‘24). He studied physics at TU Munich,
spent postdoctoral time in California at USC and LLNL and taught
practical computer science at the Univ. Bonn (1992-2003). His research
focusses on theory and applications of machine learning and artificial
intelligence, e.g., in life science and engineering. He is a member of
the Swiss Academy of Engineering Sciences (SATW), a fellow of IAPR and
an honorary member of the German Pattern Recognition Society.
Summary
Data are everywhere these days and they amount to an unimaginable
variety of views on the world. MRF imagery, digital biopsies, Omics
data in personalized medicine, the microwave background radiation of
the universe, social network traces, the finacial flow around the
globe, etc., digitalization enables us humans to perceive facets of
reality with an unprecedented complexity. With modern computing
machinery and i.e., high performance algorithmics we can create models
that are too complex to be memorized or even understood by humans but
we can control such models and validate their predictions. An
automated scientific method with algorithms in experimental design,
analysis and theory building arises on the methodological horizon and
it will transform empirical sciences with many heterogeneous degrees
of freedom from a descriptive enterprise to predictive inference. The
foremost challenge will confront us with validating these algorithms,
especially determining their statistical correctness. This machine
learning disruption will also transform computer science in a
fundamental way. We present design choices for algorithm selection and
sketch a theory of validation. The concept is demonstrated in
neuroscience applications of diffusion tensor imaging for
tractography, brain parcellation and image motion compensation.