The recent progress in high throughput measurement technologies for molecular biology, such as high throughput sequencing, microarrays, multi-dimensional proteomics, glycomics and metabolomics is driving modern biology to heavily overlap data and information sciences. Computational biology and bioinformatics are research fields that focus on developing and efficiently applying computational data analysis and design algorithmics to address data science challenges presented by the fast progress of modern molecular biology.
Design and data analysis methods are playing a central role in enabling molecular based personalized medicine. They also play a role in the development of synthetic biology, driving our ability to efficiently design and utilize molecular devices.
Mining healthcare related data in various levels and from different organizations is fast emerging as a tool for improving patient care and human wellbeing.
The algorithmic and statistical aspects of these developing fields of science present diverse, deep and fascinating challenges.
Our group develops statistical and algorithmic methods for analyzing medical and molecular measurement data. We also develop optimization and design algorithmics for measurement systems or for assays and reagents to increase efficiency and effectiveness. For example: DNA storage …
We often implement the methods into software tools that serve a broad scientific community. We are particularly interested in the efficient and effective use of synthetic nucleic acids in several contexts.
In collaboration with the FACT center at IDC we are working on cryptographic techniques to enable privacy preserving machine learning and inference performed on data from several independent parties.
We also apply data science, machine learning and statistical techniques to data from other domains.
Current and past projects include:
- Synthetic biology.
We are interested in the design of reagents, application development and data analysis related to synthetic biology and genetic engineering in general. More specifically, we are using synthetic DNA libraries for studying and optimizing biological processes.
- Statistics in ranked lists
Methods and tools to enable inference in noisy datasets. Related projects include:
- Privacy preserving machine learning
Design of reagents, application development and data analysis as related to various aspects of molecular regulation in living cells.