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silo.ai

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 220 AI experts out of which more than 100 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.
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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.
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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.
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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.
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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
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PUBLICATION
Learning structures of Bayesian networks for variable groups
Parviainen, P., Kaski, S., 2017. Learning structures of Bayesian networks for variable groups. IJAR 88.
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PUBLICATION
Non-stationary spectral kernels

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

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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.
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Technical videos

VIDEO
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Graph data in AI - Graph embedding and convolutional networks on graphs
VIDEO
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Computer vision meetup, AI interpretability
VIDEO
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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

Welcome 2022 & highlights of the past year

Peter Sarlin 20.12.2021

In line with past years, this year has been a period of accelerated learning, execution and growth. I want to express my deepest gratitude to all of our clients, partners and the entire Silo AI team for the great collaboration in 2021! Despite yet another year impacted by the pandemic, we’ve been able to progress with business as usual: taking care of each other and our customers. In times of growth, what makes me most proud, and depicts our strength as a team, is to see how we’re embracing the core of Silo AI, our values: be good, build bonds, ask why and keep learning.

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AI on the edge: machine learning in restricted environments

Jukka Yrjänäinen 26.11.2021

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

Silo AI research community

Tomasto Aste

Tomasto Aste

Professor
University College London
Samuel Kaski

Samuel Kaski

Professor
Aalto University
Barbara Hammer


Barbara Hammer

Professor
Bielefeld University
Moncef Gabbouj

Moncef Gabbouj

Professor
Tampere University of Technology
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