Data Science & Scientific Computing (DSSC) at ISTA comprises the most interdisciplinary track in the PhD program.

Data Science & Scientific Computing

Faculty in this track work on a diverse set of problems, ranging from mathematical models of evolution (Barton), medical genomics (Robinson), bioinformatics (Vicoso), systems biology (Guet), theoretical biophysics (Hannezo, Tkačik), and computational neuroscience (Vogels) to machine learning (Lampert), data science and information theory (Mondelli), distributed systems (Alistarh), and physics simulation (Wojtan, Ren).

Astronomy

The Astronomy groups aim at understanding the evolution of stars as well as astrophysical processes by using various modelling techniques (Bugnet, Caiazzo, Götberg, Haiman, Matthee).

Earth Science

Topics of the Earth Science groups are geophysical fluid dynamics (Muller) and mountain hydrology and mass movements (Pellicciotti).

Common to these topics — and emphasized as the focus of the track — is the development and use of advanced data analysis methods, numerical simulation, and statistical inference to address complex and data-intensive problems in sciences and engineering.

COMPLETE DSSC RESEARCH GROUP DETAILS ON ISTA’S MAIN SITE:

Watch our video presenting the Data Science & Scientific Computing study track here.

 

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Featured Project:

Cells Respond to Waves in Wound Healing

Hannezo Group

PhD students Daniela Boocock and Natalia Ruzickova, along with colleagues from Kyoto University, discover the biophysical mechanism that underpins long-range cell migration towards a site of wound healing, signalled via out-of-phase mechano-chemical waves.

Read a summary of their research on Phys.org: “Research reveals how wound heals in ‘waves'”. The original publication can be found in the journal Nature Physics: “Theory of mechanochemical patterning and optimal migration in cell monolayers”.

Mechano-chemical propagation of waves encode cell signalling information about site of wound healing.  Credit: Tsuyoshi Hirashima, Kyoto University

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