Data Science & Scientific Computing (DSSC) at IST Austria comprises the most interdisciplinary track in the PhD program.
Topically, 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) and theoretical biophysics (Hannezo, Tkačik), to machine learning (Lampert), data science and information theory (Mondelli), computational fabrication (Bickel) and physics simulation (Wojtan). 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 IST AUSTRIA’S MAIN SITE:
- Distributed Algorithms and Systems
- Evolutionary Genetics
- Computer Graphics and Digital Fabrication
- Systems and Synthetic Biology of Genetic Networks
- Physical Principles in Biological Systems
- Machine Learning and Computer Vision
- Data Science, Machine Learning, and Information Theory
Here is a video presenting the Data Science & Scientific Computing study track:
In case you cannot access YouTube, this video is also available here.
Cells Respond to Waves in Wound Healing
PhD students Daniela Boocock (Hannezo Group) and Natalia Ruzickova (unaffiliated Graduate School student), 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 propogation of waves encode cell signalling information about site of wound healing. Credit: Tsuyoshi Hirashima, Kyoto University