Research — Silo.AI

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

– Peter sarlin, CEO

WHAT DOES
RESEARCH
MEAN AT
SILO.AI?

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 70 AI experts out of which more than 40 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.

SILO.AI RESEARCH COMMUNITY

Join our 500+ strong community of machine learning researchers around the world.

MEMBERS INCLUDE:

risto_miikkulainen

PROFESSOR

Risto Miikkulainen

UNIV OF TEXAS AT AUSTIN

PROFESSOR

Tomasto Aste

UNIV COLLEGE LONDON

PROFESSOR

Jukka Corander

UNIVERSITY OF OSLO

PROFESSOR

Moncef Gabbouj

TAMPERE UNIV OF TECH

Guang_Bin_Huang

PROFESSOR

Guang Bin Huang

NANYANG TECH UNIV

PROFESSOR

Samuel Kaski

AALTO UNIVERSITY

MattiPietikäinen

PROFESSOR

Matti Pietikäinen

UNIVERSITY OF OULU

PROFESSOR

Scott Sanner

UNIVERSITY OF TORONTO

barbarahammer_bw

PROFESSOR

Barbara Hammer

BIELEFELD UNIVERSITY

SIGN UP FOR OUR AI SLACK GROUP:

Meet our
researchers

AI Scientist

Sami Remes

PhD in Machine Learning

Sami Remes is a senior machine learning expert with 8+ years of experience in researching and applying machine learning. Sami began his academic career at the Aalto University in Helsinki and quickly joined Professor Kaski’s Probabilistic Machine Learning Group. During his Master’s degree Sami worked on the Group Factor Analysis model, publishing his findings at 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 publications at both ACML and NIPS in 2017.

In 2019, Sami defended his dissertation entitled “Modelling non-stationary functions with Gaussian processes” at Aalto University.

At Silo.AI, Sami works as AI Scientist and has been part of various machine learning and deep learning projects, including modeling expert knowledge for a financial sector client in order to speed up, and in the future, to automate the invoicing process.

Emil Eirola is one of the first expert hires that joined Silo.AI. Emil is a senior machine learning expert with 10+ years of experience from applied machine learning projects. Emil holds a PhD in machine learning from Aalto University (2014), and has worked as a postdoc at Aalto University and Arcada UAS. Emil is author of 30+ academic papers with 400+ citations.

With a Master’s thesis (2009) on feature selection and PhD thesis (2014) on Machine Learning for data with missing values, Emil’s 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 in fields such as finance, security, and healthcare.

AI Scientist

Emil Eirola

PhD in Machine Learning

HEAD OF RESEARCH

Kaj-Mikael Björk

PhD in Optimisation and
Information Systems

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.

Kaj-Mikael has held 15 different courses in the fields of Machine Learning, Logistics, Management Science and Engineering. At Silo.AI, Kaj-Mikael hosts weekly Research Club where both internal and external speakers share their research topic or paper to the experts.

Luiza is a senior machine learning expert with 7+ years of machine learning experience and a deep understanding of Natural Language Processing for English and Finnish language. During her academic career Luiza has first-authored eight papers and has been active in organizing machine learning study groups since 2016.

Luiza just published her dissertation “Machine Learning Methods for Classification of Unstructured Data” at the Department of Computer Science at the School of Science at Aalto University. Her research focuses on analyzing unstructured data, in other words, free-form text. With its two applications, Android malware classification and soft skill mining, Luiza uses NLP to analyze job candidate resumes and Android application files.

At Silo.AI, Luiza works as AI Scientist with a focus on NLP projects ranging from low resource text classification, information extraction and summarization to speech-to-text applications.

AI Scientist

Luiza Sayfullina

PhD in Machine Learning for
Natural Language Processing

Publication highlights

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.

LEARNING STRUCTURES OF BAYESIAN NETWORKS FOR VARIABLE GROUPS

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

NON-STATIONARY SPECTRAL KERNELS

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

TOWARD 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.

Research resources

TECHNICAL VIDEOS

Research related blog posts

NLP & textual data: How text clustering provides business insights

Pauliina Alanen 2019-09-11

Automated machine learning through evolutionary computation – interview with Professor Risto Miikkulainen at SRA 2019

Pauliina Alanen 2019-06-25

Silo.AI and ÅA organised a hackathon on Edge computing and machine learning

Pauliina Alanen 2019-04-12

Taking a look at BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop

Sami Remes 2019-02-07

The future of work: learning as part of the value base

Tuuli Suominen 2019-01-04

Cases for Applying Multi-Agent Reinforcement Learning

Zhen Li 2018-12-07

Silo.AI Academy Webinar: AI model interpretability

Pauliina Alanen 2018-11-11

Improving cancer diagnostics with AI

Dmitrii Bychkov 2018-10-25

Automated machine learning through evolutionary computation – interview with Professor Risto Miikkulainen at SRA 2019

Automated machine learning through evolutionary computation – interview with Professor Risto Miikkulainen at SRA 2019

Posted by Pauliina Alanen2019-06-25

Silo.AI and ÅA organised a hackathon on Edge computing and machine learning

Silo.AI and ÅA organised a hackathon on Edge computing and machine learning

Posted by Pauliina Alanen2019-04-12

Taking a look at BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop

Taking a look at BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop

Posted by Sami Remes2019-02-07

The future of work: learning as part of the value base

The future of work: learning as part of the value base

Posted by Tuuli Suominen2019-01-04

Cases for Applying Multi-Agent Reinforcement Learning

Cases for Applying Multi-Agent Reinforcement Learning

Posted by Zhen Li2018-12-07

Silo.AI Academy Webinar: AI model interpretability

Silo.AI Academy Webinar: AI model interpretability

Posted by Pauliina Alanen2018-11-11

Improving cancer diagnostics with AI

Improving cancer diagnostics with AI

Posted by Dmitrii Bychkov2018-10-25

Artificial Intelligence and recruiting: Why soft skills matter for an AI-recruiter

Artificial Intelligence and recruiting: Why soft skills matter for an AI-recruiter

Posted by Luiza Sayfullina, PhD2018-09-19

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