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Kernel Methods, Pattern Analysis and Computational Metabolomics (KEPACO)

The KEPACO group develops machine learning methods, models and tools for data science, in particular computational metabolomics. The methodological backbone of the group is formed by kernel methods and regularized learning. The group particularly focusses in learning with multiple and structured targets, multiple views and ensembles. Applications of interest include metabolomics, biomedicine, pharmacology and synthetic biology.

See overview of KEPACO research (in PDF)


Personnel and contact information

The KEPACO group is located at the Department of Computer Science at the School of Science of Aalto University. We also belong to the Helsinki Institute for Information Technology.

Contact information and how to get to CS department in Aalto University Otaniemi Campus


Research projects

Teaching 2019-2020


Check out the CSI:FingerID server for metabolite identification from MS/MS data, running the methods we developed with Sebastian Boecker's group in Friedrich-Schiller-Universitat Jena. You may also check the clip from YLE Popular science program Prisma Studio (In Finnish only!), showcasing how metabolites are identified using machine learning in the CSI:FingerID search engine. See the clip here! (.mp4, 50MB

Please find our software page here and our GitHub page at github.com/aalto-ics-kepaco.


Selected and recent publications

Visitors and Alumni

Visitors to the group


Former students and interns