Kernel Methods, Pattern Analysis and Computational Biology (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)
News
- February 24, 2026. Linus Lind joins our group as a Visiting PhD student. Welcome Linus!
- January 1, 2026. Juho starts as the Director of Helsinki Institute for Information Technology HIIT
- December 16, 2025. Maryam Astero defends her PhD thesis Deep learning for chemical reactions.
- October 22, 2025. Maryam Astero gives a presentation at the Molecular Machine Learning conference
- September 13 - December 14, 2025. Juho visits Tufts University (USA), hosted by Prof. Soha Hassoun.
- September 1, 2025. Juho gives an invited talk "Predicting drug combination response surfaces" at University of Luxembourg.
- August 27 - September 2, 2025. Juho visits University of Luxembourg, hosted by Prof. Emma Schymanski.
- April 29, 2025. Prof. Lin Yang presented a collaboration work on Machine learning-based combination prediction for Wee1 inhibitor at AACR.
- March 4, 2025. Dr. Ragini Kihlman starts as a post-doctoral researcher in KEPACO group. Welcome Ragini!
- February - June, 2025. Prof. Elena Casiraghi from University of Milan visits KEPACO. Welcome back Elena!
- February 11, 2025. Ilari Tulkki starts an MSc thesis in drug-target prediction at KEPACO. Welcome Ilari!
- January 31, 2025. Heli Julkunen defends her PhD thesis Machine Learning for Precision Medicine.
Personnel and contact information
- Juho Rousu, Professor, group leader
- Sandor Szedmak, PhD, visiting senior research scientist
- Heli Julkunen, PhD, post-doctoral researcher
- Anchen Li, PhD, post-doctoral researcher
- Ragini Kihlman, PhD, post-doctoral researcher
- Emily Bennett, PhD, visiting post-doctoral researcher (VTT)
- Robert Armah-Sekum, MSc, doctoral researcher
- Linus Lind, MSc (Tech), visiting doctoral researcher
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
%Activities
Research projects
- PatientTherapy - Patient-tailored, effective and safe combinatorial therapies. Research Council of Finland, Proof-of-concept grant, 2026--2027
- Biodesign - Virtual Laboratory for Enzyme Design, Jane and Aatos Erkko Foundation grant 2023-2027
- MASF - Machine Learning for Systems Pharmacology. Academy of Finland grant 2021-2025
Software
Please find our software in GitHub page github.com/aalto-ics-kepaco.
Publications
- Julkunen, H. and Rousu, J., 2025. Comprehensive interaction modeling with machine learning improves prediction of disease risk in the UK Biobank. Nature Communications, 16(1), p.6620.
- Ankit, Bhadra, S. and Rousu, J., 2025. Efficient 3D kernels for molecular property prediction. Bioinformatics, 41(Supplement_1), pp.i58-i67.
- Li, A., Casiraghi, E. and Rousu, J., 2025. CSGL: Chemical Synthesis Graph Learning for Molecule Representation. Bioinformatics, p.btaf355.
- Astero, M. and Rousu, J., 2025. Enhancing atom mapping with multitask learning and symmetry-aware deep graph matching. Journal of Cheminformatics, 17(1), pp.1-17.
- Huusari, R., Wang, T., Szedmak, S., Dias, D., Aittokallio, T. and Rousu, J., 2025. Scaling up drug combination surface prediction. Briefings in Bioinformatics, Volume 26, Issue 2.
- Huusari, R., Wang, T., Szedmak, S., Aittokallio, T. and Rousu, J., 2025. Predicting drug combination response surfaces. npj Drug Discovery, 2(1), p.2.
- Li, A., Yang, B., Huo, H., Hussain, F. and Xu, G., 2025, July. Hypercomplex knowledge graph-aware recommendation. In Proceedings of the 48th international ACM SIGIR conference on research and development in information retrieval (pp. 2017-2026).
- Li, A. and Yang, B., 2025, April. Dual graph denoising model for social recommendation. In Proceedings of the ACM on Web Conference 2025 (pp. 347-356).
- Bauvin, B., Godon, T., Bachelot, G., Carpentier, C., Huusaari, R., Deraspe, M., Rousu, J., Quach, C. and Corbeil, J., 2025. Extracting a COVID-19 signature from a multi-omic dataset. Frontiers in bioinformatics, 5, p.1645785.
- Julkunen, H., 2025. Machine Learning for Precision Medicine. PhD thesis. Aalto University.
- Bushuiev, R., Bushuiev, A., de Jonge, N.F., Young, A., Kretschmer, F., Samusevich, R., Heirman, J., Wang, F., Zhang, L., Dührkop, K. and Ludwig, M., 2024. MassSpecGym: A benchmark for the discovery and identification of molecules. Advances in Neural Information Processing Systems, 37, pp.110010-110027.
- Wang, T., 2024. Deconvoluting complex disease mechanisms via machine learning methods for targeted therapeutics. PhD thesis. University of Helsinki.
- Li, A., Casiraghi, E. and Rousu, J., 2024. Chemical Reaction Enhanced Graph Learning for Molecule Representation. Bioinformatics, p.btae558.
- Pusa, T. and Rousu, J., 2024. Stable biomarker discovery in multi-omics data via canonical correlation analysis. PloS one, 19(9), p.e0309921.
- Schulman, A., Rousu, J., Aittokallio, T. and Tanoli, Z., 2024. Attention-based approach to predict drug-target interactions across seven target superfamilies. Bioinformatics, 40(8), p.btae496.
- Abbasi, F. and Rousu, J., 2024. New methods for drug synergy prediction: A mini-review. Current Opinion in Structural Biology, 86, p.102827.
- Armah-Sekum, R.E., Szedmak, S. and Rousu, J., 2024. Protein function prediction through multi-view multi-label latent tensor reconstruction. BMC bioinformatics, 25(1), p.174.
- Astero, M. and Rousu, J., 2024. Learning symmetry-aware atom mapping in chemical reactions through deep graph matching. Journal of Cheminformatics, 16(1), p.46.
- Li, A., Yang, B., Huo, H., Hussain, F.K. and Xu, G., 2024, May. Structure-and logic-aware heterogeneous graph learning for recommendation. In 2024 IEEE 40th international conference on data engineering (ICDE) (pp. 544-556). IEEE.
- Sandström, H., Rissanen, M., Rousu, J. and Rinke, P., 2024. Data-Driven Compound Identification in Atmospheric Mass Spectrometry. Advanced Science, 11(8), p.2306235.
Visitors and Alumni
Visitors to the group
- 2023-26 (Spring): Prof. Elena Casiraghi, Università degli Studi di Milano
- 2023: Elina Francovic-Fontaine, PhD student, Laval University
- 2023: Indumathi P, PhD student, IIT Madras
- 2023: Golsar Fatemeh Abbasi, PhD student, University of Tehran
- 2020: Prof. Cecile Capponi, Aix-Marseille Universite
- 2019: Prof. Sahely Bhadra, IIT Palakkad, India, Dr. Kai Dührkop, Friedrich-Schiller University Jena
- 2019: Dr. Luc Motte, Telecom Paris
- 2018: Dr. Christina Leslie, Memorial Sloan Kettering Cancer Center, USA
- 2017: Prof. Francois Laviolette, Laval University, Canada; Prof. Sebastian Boecker, Friedrich-Schiller University of Jena
- 2016: Prof. Sebastian Boecker, Dr. Tim White, Marcus Ludwig, Kai Duehrkop, Friedrich-Schiller University Jena
- 2015: Prof. Giorgio Valentini, Università degli Studi di Milano
- 2013: Prof. Sebastian Boecker, Friedrich-Schiller University Jena
Doctoral Alumni
- Dr. Maryam Astero, PhD 2025 (next: AI Research Engineer, Genomenon)
- Dr. Riikka Huusari, post-doc 2020-2024 (next: data scientist at Readpeak)
- Dr. Tianduanyi Wang, PhD 2024 at FIMM/HIIT (next: bioinformatician at FIMM - University of Helsinki)
- Dr. Taneli Pusa, post-doc 2021-2024
- Dr. Luc Brogat-Motte, PhD 2023
- Dr. Eric Bach, PhD 2023 (next: data scientist at Elisa)
- Dr. Maryam Sabzevari, post-doc 2018-2022 (next: machine learning research scientist at Nokia Bell Labs)
- Dr. Viivi Uurtio, PhD 2020 (next: data scientist at Elisa)
- Dr. Anna Cichonska, PhD 2018 (next: postdoctoral researcher at FIMM - University of Helsinki and senior data scientist at Nightingale Health)
- Dr. Celine Brouard, post-doc 2014-2018 (next: researcher at INRA Tolouse)
- Dr. Huibin Shen, PhD 2017 (next: machine learning scientist at Amazon Berlin)
- Dr. Sahely Bhadra, post-doc 2014-2016 (next: assistant professor at IIT Palakkad)
- Dr. Elena Czeizler, research fellow 2013-16 (next: research scientist at Varian Medical Systems)
- Dr. Hongyu Su, PhD 2015 (next: lead machine learning engineer at Nordea Bank)
- Dr. Jana Kludas, post-doc 2012-2015 (next: lead data scientist at msg)
- Dr. Jefrey Lijffijt, PhD 2013 (next: research associate in data science at University of Bristol)
- Dr. Markus Heinonen, PhD 2013 (next: postdoctoral researcher at Universite d'Evry-Val d'Essonne)
- Dr. Esa Pitkänen, PhD 2010 (next: postdoctoral researcher at University of Helsinki)
- Dr. Ari Rantanen, PhD 2006 (next: data scientist at Tieto Corp)
Former students and interns
- Ilari Tulkki, MSc 2026
- Gianmarco Midena
- Ellimari Paunio, MSc 2023 (next: junior bioinformatician at CSC - IT Center for Science)
- Amandine Grosfils, MSc 2021
- Minna Oksanen, MSc 2021
- Wen Xiang, intern 2020
- Santeri Mentu, MSc 2020
- Antoine Moulin, intern 2019
- Bugra Aker Yilmaz, intern 2019
- Jane Douat, intern 2019
- Heli Julkunen, MSc 2019
- Vilma Jägerroos, MSc 2019
- Tolou Shadbahr, MSc 2019
- Antoine Basse, intern 2018 (with Telecom ParisTech)
- Fabio Colella, research assistant 2018
- Parisa Mapar, MSc 2018
- Zheyang Shen, research assistant 2017
- Anton Mattsson, intern 2017
- Linh Nguyen, MSc 2017
- Mohamed Jabri, MSc 2017
- Jinmin Lei, MSc 2016
- Maja Ilievska, MSc 2016
- Nicole Althermeler, MSc 2016
- Iitu Kuittinen, MSc 2015
- Clemens Westrup, intern 2013-15
- Jian Hou, MSc 2014
- Carlos Maycas Nadal, BSc 2014
- Fitsum Tamene, MSc 2013
- Yvonne Herrmann, MSc 2012