Tackling real-world problems with the latest methods

Silo AI is the best place for machine learning experts to work with real-world industry problems. We value scientific efforts in machine learning, computer vision and natural language processing applied to our clients’ business cases.
Our team consists of more than 110 people, 90 AI experts out of which more than 60 hold a PhD in machine learning, computer vision or relevant fields. Through our own R&D work and customer projects, we aim to stay true to our nature as a private AI lab.

Our researchers. Your researchers.

Tomi Peltola

Tomi Peltola, PhD

Experienced machine learning researcher with publications in leading outlets like NeurIPS, IJCAI, AISTATS, ECML, IUI, and UMAP. He has a focus on applied deep learning, Bayesian and probabilistic modelling, and interactive human-in-the-loop ML.
Susanna Pirttikangas

Susanna Pirttikangas, PhD

Experienced AI scientist with 20+ years at the intersection of academia and industry. Has led several large research programs on industrial and edge applications of AI and is an author of more than 70 publications.
Hedvig Kjellström

Hedvig Kjellström, PhD

Professor in the Division of Robotics, Perception and Learning at KTH Royal Institute of Technology, Sweden, focusing on computer vision and machine learning methods for enabling artificial agents to interpret the behavior of humans and other animals.
Kaj-Mikael Björk

Kaj-Mikael Björk, PhD

Top senior researcher with unique capability to lead research teams. A former visiting professor at UC Berkeley and Carnegie Mellon, listed as the top 6 most published AI researchers in Finland in the Digibarometri survey by the Research Institute of the Finnish Economy ETLA.

Publications by our AI experts

PUBLICATION
State space gaussian processes with non-gaussian likelihood
Nickisch, H., Solin, A., Grigorevskiy, A., 2018. State Space Gaussian Processes with Non-Gaussian Likelihood. ICML 2018.
Peter Sarlin
PUBLICATION
Learning structures of Bayesian networks for variable groups
Parviainen, P., Kaski, S., 2017. Learning structures of Bayesian networks for variable groups. IJAR 88.
PUBLICATION
Non-stationary spectral kernels

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

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

Technical videos

VIDEO
silo.ai video
Play Video
Graph data in AI - Graph embedding and convolutional networks on graphs
VIDEO
Computer vision meetup, AI interpretability
VIDEO
Computer vision meetup, Face AI – applications and concerns
VIDEO
Computer vision meetup, Human action
VIDEO
Machine learning for real-time inference
VIDEO
Personal data processing principles for AI
VIDEO
AI model interpretability
VIDEO
State space gaussian processes with non-gaussian likelihood
Alexander Grigorevskiy

Research related articles

Rolls of paper at Kemira

Kemira and Silo AI leverage machine learning to predict and prevent process disturbances and quality issues in paper and board production

The future of the papermaking process is data-driven. This can already be seen today in the collaboration between Kemira, the global chemicals company serving customers in the pulp and paper industry, and Silo AI, the largest private AI lab in the Nordics. The two companies are today announcing their collaboration, which has already resulted in a predictive AI-driven solution taken into production in board and paper production.

Read more

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ARTICLE
silo.ai
April 2nd, 2020
Improving situational awareness with flight delay prediction
ARTICLE
silo.ai
April 2nd, 2020
Improving situational awareness with flight delay prediction
ARTICLE
silo.ai
April 2nd, 2020
Improving situational awareness with flight delay prediction
ARTICLE
silo.ai
April 2nd, 2020
Improving situational awareness with flight delay prediction

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