Eero Siivola

M.Sc. (Tech.), Doctoral student
Aalto University
Helsinki Institute for Information Technology HIIT
Department of Computer Science
Probabilistic Machine Learning Group

Room: Konemiehentie 2, 3rd floor, A338
Email: Eero.Siivola(at)aalto.fi
Curriculum Vitae
GitHub
Twitter

Research Activities

I work as a doctoral candidate in Probabilistic Machine Learning –group. My research is related to improving the quality of medical decision making that is based on probabilistic models. The main emphasis of the research is in improving modelling techniques and interpretativity of Bayesian models of medical data. My supervisor is Prof. Aki Vehtari

Publications

  • Iiris Sundin, Peter Schulam*, Eero Siivola*, Aki Vehtari, Suchi Saria, and Samuel Kaski (2019). Active learning for decision-making from imbalanced observational data. Thirty-sixth International Conference on Machine Learning, PMLR 97:6046-6055. Online. arXiv preprint arXiv:1904.05268.*Equal contribution.
  • Eero Siivola, Aki Vehtari, Jarno Vanhatalo, Javier González and Michael Riis Andersen (2018). Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations. 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP) (pdf). (Best student paper award)
  • Simon Holmbacka, Jarno Niemelä, Henri Karikallio, Karri Sunila, Ilkka Raiskinen, Eero Siivola, Juho Piironen, Tuomas Sivula (2018). Alarm Prediction in LTE Networks. 2018 IEEE 25th International Conference on Telecommunications (ICT) (pdf).
  • Techincal reports

  • Michael Riis Andersen, Eero Siivola, Gabriel Riutort-Mayol, and Aki Vehtari (2018). A non-parametric probabilistic model for monotonic functions. BNP@NeurIPS 2018 workshop: "All of Bayesian Nonparametrics (Especially the Useful Bits)". (pdf).
  • Michael Riis Andersen, Eero Siivola and Aki Vehtari (2017). Bayesian Optimization of Unimodal Functions. NIPS Workshop in Bayesian optimization for Science and Engineering (pdf).
  • Eero Siivola, Juho Piironen and Aki Vehtari (2016). Automatic monotonicity detection for Gaussian Processes. arXiv.
  • Talks

  • August 30, 2018: Solving ODEs in the wild: Scalable pharmacometrics with Stan StanCon, Helsinki, Finland. Youtube.
  • May 30, 2017: Advances in using GPs with derivative observations. GPa17, Berlin, Germany. Slides.
  • Teaching

  • Bayesian Data Analysis, autumns 2016 - 2019, teaching assistant. Lectured by Prof. Aki Vehtari.

  • Last modified: November 7, 2019