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 150 AI experts out of which more than 80 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
Play Video
Graph data in AI - Graph embedding and convolutional networks on graphs
VIDEO
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Computer vision meetup, AI interpretability
VIDEO
Play Video
Computer vision meetup, Face AI – applications and concerns
VIDEO
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Computer vision meetup, Human action
VIDEO
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Machine learning for real-time inference
VIDEO
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Personal data processing principles for AI
VIDEO
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AI model interpretability
VIDEO
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State space gaussian processes with non-gaussian likelihood
Alexander Grigorevskiy

Research related articles

AI on the edge: machine learning in restricted environments

At Silo AI we have worked on customer projects from 8-bit and 32-bit microcontrollers to advanced SoCs with dedicated machine learning accelerators. While each of the customers’ case is different and platforms have different toolchains for optimal deployment, the basic AI development flow follows similar steps:

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Episode 19: DeepRacer, Jouni Luoma

Jouni is a senior AI engineer at Silo AI and the Nordic champion of the globally renowned DeepRacer competition. An enthusiast of signal processing, cloud + local data management, Jouni jumped

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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

Silo AI research community

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