Current positions

• Academy Professor 2016-2020
• Director, Finnish Centre of Excellence in Computational Inference Research COIN, -2017
• Director, Finnish Center for Artificial Intelligence FCAI, 2018-

Current activities

• My research group Aalto Probabilistic Machine Learning Group #AaltoPML
• NIPS 2017: Area and Tutorials Chair; AISTATS 2018, ICML 2018, AAAI 2019 Area Chair
• JMLR Action Editor, IEEE TPAMI Associate Editor
• Finnish Center for Artificial Intelligence FCAI #FCAI
• Finland's AI Programme, steering group #aiera #tekoälyaika
• Macadamia Major (Machine Learning, Data Science and Artificial Intelligence), responsible professor
• Scientific Advisory Board, NOMAD, BigInsight, Statistics Finland
• Machine Learning Coffee seminar
• Publications on my research group's pages
• Come study or work with me!

I work on probabilistic machine learning, meaning probabilistic modelling and Bayesian inference, applied to difficult problems that are interesting and societally important. At the moment I work on the inter-related topics of analysis of multiple data sources, human-in-the-loop machine learning, simulator-based inference (likelihood-free inference with ABC), and privacy-preserving learning. I have had the chance to collaborate with amazing people in interdisciplinary research projects on applications in biology and medicine, brain signal analysis and user interaction (see list of publications), and in the Aalto Probabilistic Machine Learning Group which I lead with Prof. Aki Vehtari, and the Finnish Center of Excellence in Computational Inference Research which I lead until end of 2017.

To further boost the already strong artificial intelligence research and its impact, we are setting up a new Finnish Artificial Intelligence Center FCAI, which I will lead 2018-. It is a partnership of Aalto University, University of Helsinki, and a number of company members, networking further to key international and national partners. We aim to solve three bottleneck questions in AI research: data scarcity, dependability of AI, and ability of AI to understand its users (more details here). The research will be done with company partners, including new startups and spin-offs, which will take the results directly to use. We will also pay special attention to AI education.

Some recent papers (Full list on my research group's pages):

Human-in-the-loop machine learning, human-computer interaction:

• Pedram Daee, Tomi Peltola, Marta Soare, Samuel Kaski (2017). Knowledge elicitation via sequential probabilistic inference for high-dimensional prediction. Machine Learning, p. 1-22. [Code and data] [Link]
• Antti Kangasrääsiö, Kumaripaba Athukorala, Andrew Howes, Jukka Corander, Samuel Kaski, Antti Oulasvirta (2017). Inferring Cognitive Models from Data using Approximate Bayesian Computation. ACM SIGCHI annual conference on human factors in computing systems, Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, p. 1295-1306. [Link] [Press release]
• Tuukka Ruotsalo, Giulio Jacucci, Petri Myllymäki, and Samuel Kaski. Interactive intent modeling: Information discovery beyond search. Communications of the ACM, 58(1):86–92, 2015. [dl.acm.org ...]

Multiple data sources:

• Paul Blomstedt, Ritabrata Dutta, Sohan Seth, Alvis Brazma and Samuel Kaski (2016). Modelling-based experiment retrieval: A case study with gene expression clustering. Bioinformatics, 32(9):1388-1394. [Link] [Preprint]
• Arto Klami, Seppo Virtanen, Eemeli Leppäaho, and Samuel Kaski. Group factor analysis. IEEE Transactions on Neural Networks and Learning Systems, 26(9):2136-2147, 2015. [Code] [PDF] [Preprint]
• Eemeli Leppäaho, Muhammad Ammad-ud-din, Samuel Kaski (2017). GFA Exploratory Analysis of Multiple Data Sources with Group Factor Analysis. Journal of Machine Learning Research, 18:1-5. [Link]
• James C Costello, Laura M Heiser, Elisabeth Georgii, Mehmet Gönen, Michael P Menden, Nicholas J Wang, Mukesh Bansal, Muhammad Ammad-ud-din, Petteri Hintsanen, Suleiman A Khan, John-Patrick Mpindi, Olli Kallioniemi, Antti Honkela, Tero Aittokallio, Krister Wennerberg, NCI DREAM Community, James J Collins, Dan Gallahan, Dinah Singer, Julio Saez-Rodriguez, Samuel Kaski, Joe W Gray, and Gustavo Stolovitzky. A community effort to assess and improve drug sensitivity prediction algorithms. Nature Biotechnology, 32:1202–1212, 2014. [dx.doi.org ...]

Simulator-based inference / likelihood-free inference:

• Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skyten, Marko Järvenpää, Michael Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski (2017). ELFI: Engine for Likelihood-Free Inference. [Code] [Documentation] [Preprint][Press release]
• Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander (2017). Likelihood-free inference via classification. STATISTICS AND COMPUTING. [Link]
• Jarno Lintusaari, Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander (2016). Fundamentals and Recent Developments in Approximate Bayesian Computation. Systematic Biology, 66 (1): e66-e82. [Link]

Privacy-preserving machine learning:

• Antti Honkela, Mrinal Das, Arttu Nieminen, Onur Dikmen, Samuel Kaski (2017). Efficient differentially private learning improves drug sensitivity prediction. [Preprint]
• Mikko Heikkilä, Eemil Lagerspetz, Samuel Kaski, Kana Shimizu, Sasu Tarkoma, Antti Honkela (2017). Differentially Private Bayesian Learning on Distributed Data. NIPS 2017 [Preprint] [Press release]

Other:

• Sami Remes, Markus Heinonen, Samuel Kaski (2017). Non-Stationary Spectral Kernels. NIPS 2017 [Preprint]
• Makoto Yamada, Takeuchi Koh, Tomoharu Iwata, John Shawe-Taylor, Samuel Kaski (2017). Localized Lasso for High-Dimensional Regression. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 54:325-333. [Link]