Love the problems, not the solutions.
We want to see diverse views in problem solving.

– Peter sarlin, CEO


Silo.AI is a research and researcher driven organisation that values scientific efforts, be they published papers or internal research endeavours. We aim at not only to establish Silo.AI as a high-quality research organisation, but also and especially to enable our researchers to establish themselves and develop as top researchers.

At the same time, we are a customer oriented private AI lab, meaning that our work runs parallel to customer projects, and applies their real life cases whenever possible.

Meet our

AI Scientist

Sami Remes

Sami Remes began his career in AI at the Aalto University in Helsinki. Already during his BSc degree, he began working in Professor Samuel Kaski’s Statistical Machine Learning and Bioinformatics Group, which is nowadays part of Kaski’s Probabilistic Machine Learning Group.

Sami completed both his BSc and MSc theses on extensions of the Group Factor Analysis model, developed by his research group, publishing his findings in the workshops of the world’s leading AI conference NIPS in 2013 and 2015. He then moved on to research Gaussian Processes and developing new kernels that model non-stationary changes in e.g. dependencies of different output variables. This research led to various publications in forums such as ACML (Asian Conference on Machine Learning) and the main conference of NIPS in 2017.

Upon joining Silo.AI, Sami has finished most of his PhD research and will receive his degree in early 2019.

Outside the office, Sami enjoys a married life, barbecue, and the occasional game on PC or

Learn about Sami’s research on non-stationary spectral kernels.

Emil Eirola is one of the first expert hires in Silo.AI, bringing Machine Learning experience to the company. With a Master’s thesis (2009) on feature selection and PhD thesis (2014) on Machine Learning for data with missing values, his research has focused on how to deal with the practical issues of applying ML to real-world problems. An undergraduate major in mathematics ensures a strong understanding of the theoretical foundations behind the algorithms.

As a researcher in the Applications of Machine Learning group in Aalto University, and later the Department of Business Management and Analytics at the Arcada University of Applied Sciences, Emil has worked on finding Machine Learning solutions to all sorts of different use cases in fields such as finance, security, and healthcare.
When not in the office, Emil can be found travelling the world with any one of his several music projects.

Learn more about Emil’s applied Machine Learning research.

AI Scientist

Emil Eirola


Kaj-Mikael Björk

Kaj-Mikael Björk is a top senior researcher with unique capability to lead research teams. Kaj-Mikael is Research Director at Arcada and Head of Research of Silo.AI. During his time as the Head of Department in Arcada, he initiated the AI track and Risklab Finland. Within the research projects he has participated in, he has completed more than 70 scientific peer reviewed articles with an H-index of 13 (Google scholar). He was also listed as the top 6 most published AI researcher in Finland in the Digibarometri survey by the Research Institute of the Finnish Economy ETLA.

Previously, Kaj-Mikael has been a visiting professor at UC Berkeley and Carnegie Mellon University as well as working as an Assistant Professor in Information Systems (Åbo Akademi University) and Senior Lecturer in Logistics (Arcada). The borderline between economics and IT has long fascinated him, as well as inspired him to pursue achievements in both education and research. He has held approx. 15 different courses in the fields of Machine Learning, Logistics, Management Science and Engineering. As Head of Department, Kaj-Mikael has also participated actively in many administrative tasks, such as a member of the university’s steering group and other task forces. His research interests are in optimization, Machine Learning, analytics, supply chain management and fuzzy logic.

Learn more about the monthly Silo.AI Research Club that Kaj-Mikael is hosting.

Publication highlights

Nickisch, H., Solin, A., Grigorevskiy, A., 2018. State Space Gaussian Processes with Non-Gaussian Likelihood.
ICML 2018.

Parviainen, P., Kaski, S., 2017. Learning structures of Bayesian networks for variable groups.
IJAR 88.

Remes, S., Heinonen, M., Kaski, S., 2017. Non-Stationary Spectral Kernels.
NIPS 2017

Holopainen, M., Sarlin, P., 2017. Toward robust early-warning models: A horse race, ensembles and model uncertainty. Quantitative Finance 17.

Research resources




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