Data Science & Scientific Computing (DSSC) at ISTA 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), computational neuroscience (Vogels), to machine learning (Lampert), data science and information theory (Mondelli), distributed systems (Alistarh), computational fabrication (Bickel), and physics simulation (Wojtan), and computational material sciences (Cheng).
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:
- Distributed Algorithms and Systems
- Evolutionary Genetics
- Computer Graphics and Digital Fabrication
- Stellar Dynamics and Astroseismology
- Stellar Evolution in Star Clusters
- Computational Materials Science
- Massive Binary Stars
- Systems and Synthetic Biology of Genetic Networks
- Physical Principles in Biological Systems
- Machine Learning and Computer Vision
- Causal Learning and AIFRANCESCO LOCATELLO
- Data Science, Machine Learning, and Information Theory
- Astrophysics of Galaxies
- Atmosphere and Ocean Dynamics
- Cryosphere and Mountain Hydrosphere
- Medical Genomics
- Information Processing in Biological Systems
- Sex-Chromosome Biology and Evolution
- Computational Neuroscience and Neurotheory
- Computer Graphics and Physics Simulation
You’ll find a video presenting the Data Science & Scientific Computing study track here.
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Get to know Alexander Kolesnikov and how our Graduate School shaped its successful career!
Cells Respond to Waves in Wound Healing
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