GroupDecisions, Networks and Analytics lab (DNA)

Decisions, Networks and Analytics lab (DNA)

The Decisions, Networks and Analytics (DNA) Laboratory is focused on developing and applying algorithmic methods from a wide selection of fields in computer science on real world, large scale applications. Our areas of expertise include networking and network management, data analysis and data mining, Big Data analytics, machine learning, planning and scheduling, resource allocation, process modeling and monitoring, fault diagnosis and decision support. We have long-term experience from both research and real world industrial projects in collaboration with among others EricssonABB, Green Cargo, Trafikverket, SaabTech, Vinnova and SJ.



The DNA Lab is headed by Dr. Daniel Gillblad (lab manager).


Henrik Abrahamsson
PhD, Senior Researcher
+46 70 774 15 95
henrik.abrahamsson [at]

Bengt Ahlgren
PhD, Senior Researcher
+46 70 314 15 62
bengt.ahlgren [at]

Anneli Avatare Nöu
Ph Lic, Researcher
+46 73 347 09 62
anneli.nou [at]

Fehmi Ben Abdesslem
PhD, Senior Researcher
+46 70 547 06 42
fehmi.ben.abdesslem [at]

Björn Bjurling
PhD, Senior Researcher
+46 70 775 15 89
bjorn.bjurling [at]

Magnus Boman
+46 72 720 35 88
magnus.boman [at]

Amaru Cuba Gyllensten

+46 70 794 11 89
amaru.cuba.gyllensten [at]

Per Danielsson
+46 70 385 02 58
per.danielsson [at]

Ariel Ekgren
+46 737 419 704
ariel.ekgren [at]

Jan Ekman
PhD, Senior Researcher
+46 70 225 65 01
jan.ekman [at]

Björn Gambäck
+46 70 568 15 35
bjorn.gamback [at]

Ather Gattami
PhD, Senior researcher
+46 704 225 330
ather.gattami [at]

Daniel Gillblad
PhD; Director, Decisions, Networks and Analytics Laboratory
+46 70 620 17 98
daniel.gillblad [at]

Olof Görnerup
PhD, Senior Researcher
+46 70 252 10 62
olof.gornerup [at]

Anders Holst
PhD, Adjunct Professor
+46 70 233 65 39
anders.holst [at]

Björn Hovstadius
Business Development
+46 70 838 39 47
bjorn.hovstadius [at]

Abubakrelsedik Karali

+46 76 087 28 80
abubakrelsedik.karali [at]

Georgios P. Katsikas
Industrial Ph.D.
+46 72 503 73 45
georgios.katsikas [at]

Dejan Kostic
+46 73 765 20 43
dejan.kostic [at]

Per Kreuger
PhD, Senior Researcher
+46 70 566 37 15
per.kreuger [at]

Anders Lindgren
PhD, Senior Researcher
+46 70 717 72 69
anders.lindgren [at]

Shaoteng Liu
PhD, Senior Researcher
+46 72 293 53 20
shaoteng.liu [at]

Ian Marsh
PhD, Senior Researcher
+46 70 772 15 36
ian.marsh [at]

Akhila Rao
+46 72 278 91 92
akhila.rao [at]

Åsa Rudström
PhD, Senior Researcher
+46 70 774 88 32
asa.rudstrom [at]

Magnus Sahlgren
PhD, Senior Researcher

magnus.sahlgren [at]

Marie Sjölinder
PhD, Senior Researcher
+46 70 776 15 96
marie.sjolinder [at]

Rolf Stadler
+46 73 322 38 48
rolf.stadler [at]

Rebecca Steinert
Group leader, Senior Researcher, PhD
+46 70 773 15 71
rebecca.steinert [at]

Theodore Vasiloudis
+46 70 047 74 23
theodore.vasiloudis [at]


Completed projects



In media


Master thesis projects (Exjobb)

We regularly offer thesis projects for Master students in the DNA lab. We are primarily interested in students studying computer science, computer science and engineering, and information and communication technology at universities in Sweden.

If you are interested in the research areas we work on in DNA, you are welcome to send us an application. Below we often have a list of proposed thesis projects with contact information. If there are none, you are welcome to contact Daniel Gillblad, Bengt Ahlgren, Anders Holst, Martin Aronsson or Rebecca Steinert.

An application should contain the following information. (1) Your CV; (2) record of your completed courses and grades; (3) a report or similar text in English as an example of your writing skills.

Available projects

Old Projects (examples)

Job openings

We are currently not hiring.

We regularly hire post-docs through the ERCIM Fellowship Programme which has two application rounds per year.

DNA Software

Software developed by members of the DNA lab.


A tool for monitoring the performance of a KVM virtual machine for the purpose of testing infrastructure compliance for running various VNFs. The tool can monitor both hardware events as well as software events of a specific KVM-based virtual machine.

License: Apache 2.0


Responsible: Rebecca Steinert


A distributed probabilistic rate monitoring function for node-local traffic rate modelling and congestion detection.

License: Apache 2.0


Responsible: Rebecca Steinert


Technology agnostic link quality metrics for radio access networks.

License: Apache 2.0


Responsible: Rebecca Steinert


A tool for predicting and producing a deployment plan for distributed controller planes. The tool accounts for required operational reliability and required control plane traffic

License: Apache 2.0


Responsible: Rebecca Steinert


pyISC is the Python API to the ISC anomaly detection and classification framework. The framework implements Bayesian statistical methods for anomaly detection and classification. Currently supported statistical models are: Poisson, Gamma and multivariate Gaussian distributions.

License: LGPL


Responsible: Thomas Olsson


The CheesePi project aims to objectively characterise the service users’ obtain from their home Internet connection. The goal is to establish an open-access measurement infrastructure in Sweden.  A small simple easy to use monitoring device is deployed in homes to estimate the quality of the Internet connection over several weeks and months. We use the Raspberry Pi as a quiet, simple device to do this. The code is written in Python is available as is the data we have gathered.

License: Apache 2.0


Responsible: Ian Marsh




Kista Office

Decisions, Networks and Analytics lab
Box 1263
SE-164 29 Kista

Västerås Office

RISE SICS  Västerås
Box 411
SE-721 08 Västerås



How to get to SICS

About DNA

The Decisions, Networks and Analytics (DNA) Laboratory deploy, analyse, develop, and implement algorithmic methods from a wide selection of fields in computer science. The qualifying characteristic of methods chosen is that they should be suited for real world, large scale industrial applications.

Currently this includes methods for planning and scheduling, resource allocation and flow optimisation, data analysis, process modelling and monitoring, fault diagnosis and decision support. This research direction is motivated by the conviction that the science of computation must include the study of practical application of its results. We aim to collect, study, apply, classify, improve and when necessary invent new algorithmic techniques and methods motivated by individual applications. We also aim to widen the field of applications in industry by applying algorithmic methods to a new problem domains within industry.

Research goals

Many of the algorithmic methods studied in individual areas of computer science have been applied only to idealisations of real life industrial problems. To solve or support the solution of real practical industrial problems it is generally necessary to analyse the full problem very carefully and try and understand it in terms of well understood subproblems.

This activity leads to two types of results:

  • Better understanding of typical models for a selection of important real-life industrial problems
  • Better understanding of the properties of a selection of practically useful algorithmic methods

This said, the choice of actual industrial applications at each point in time reflects the backgrounds and experiences of the scientists employed by the laboratory. The current state of the art in the laboratory can thus be characterised in two dimensions:

  • The types of problems of which we have working knowledge
  • The types of algorithmic methods we use and investigate

Problems currently investigated

  • Capacity and network analysis and flow optimisation in e.g. transportation, logistics and telecom
  • Classification and diagnosis in in e.g. bioinformatics
  • Decision support in chemical analysis and synthesis with applications in e.g. drug desigN
  • Infrastructure design support with applications in telecom and transportation
  • Fault detection and analysis with applications in e.g. process and industry
  • Matching and structure detection in bio-informatics
  • Analysis and usage optimisation with application in e.g. telecom and transportation
  • Process planning and monitoring for process and manufacturing industry with applications in e.g. steel, paper and petrochemical industries * Resource allocation in transportation and telecom applications
  • Statical and dynamic scheduling for personnel, vehicles and or other production resources in e.g transportation
  • Traffic analysis and optimisation applications in e.g. transportation, telecom and energy distribution
  • Scheduling and flow optimisation for e.g energy production and distribution

Algorithmic methods currently used

  • Bayesian statistics
  • Combinatory reasoning
  • Constraint programing
  • Discrete event systems
  • Information theory
  • Learning systems
  • Local search methods
  • Mathematical logic and algebra
  • Matching theory
  • Operations research
  • Scheduling methods
  • Statistical modelling methods

Typical results

Depending on the type of application and the type of results expected working methods

  • Models of practical industrial processes
  • Functional requirements and prototypes of support systems
  • Solution methods for diagnosis, planning, resource allocation, scheduling and structure analysis
  • Methodology for application of algorithmic methods
  • Prototype implementations of novel solver algorithms and search heuristics.


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