ML-Med: Machine Learning with Relational Background Knowledge for Biomedical Applications
ML-Med develops machine-leanring methods that exploit relational background knowledge. These methods are applied in precision medicine, where our goal is to predict the response of specific cell lines to specific cancer drugs.
- Max Planck Institute for Molecular Genetics, Department Bioinformatics.
- MicroDisvovery GmbH,
Collaborative Research Center on Data Assimilation
CRC 1294 addresses the seamless integration of large data sets into sophisticated computational models. Tobias Scheffer is (Co-)PI for projects B05 and Z03.
Malware Detection by Analyzing HTTPS Logs
Principal Investigator: Tobias Scheffer
Funding: Cisco Systems
Duration: Since 2016.
In this project, we have developed technology that can identify malicious software by analyzing patterns in encrypted network communication.
Model Building from Experimental Data: Machine Learning and Model Evaluation with Non-IID Data
Principal Investigator: Niels Landwehr
Funding: German Science Foundation DFG, Emmy Noether Program (LA-3270/1-1)
The project studies machine learning approaches for building predictive models from observational data in the sciences. Because of the way observational data is collected -- by longitudinal studies, by pooling different data sources, or by experimental protocols that influence what we can observe -- these data have specific statistical characteristics that often violate assuptions made in standard machine learning methods. We study how such characteristics affect the theoretical analysis and empirical results of machine learning methods, and how we can derive methods that better account for them.
We also practically study the inference of predictive models from observational data in collaboration with researchers from cognitive psychology and geophysics.
For more details see the research group on Machine Learning and Scientific Data Analysis.