Department of Machine Intelligence
Machine learning has gained enormous international momentum, and it has been defined as a separate research topic since 2018 at Simula. In particular, as a part of the new Simula Metropolitan Center for Digital Engineering, a new Machine Intelligence Department (MIND) was established in 2018.
MIND aims at advancing frontiers of machine learning and data mining by developing novel methodologies and algorithmic solutions for the analysis of complex systems and high-dimensional data in science and industry. Specifically, the research activities represented within MIND span three general areas: statistical learning and regularization theory; data mining with focus on matrix and tensor factorization; and deep learning applications. In addition, MIND contributes to the areas of system research related to machine learning, like security, performance, model compression.
People at Department of Machine Intelligence
Who we are?
Simula Metropolitan employees are researchers, postdoctoral fellows, PhD students, engineers and administrative people. We are from all over the world, ranging from newly educated to experienced researchers, all working on making research in digital engineering at the highest international level possible.
Projects at Department of Machine Intelligence
DeCipher
Cancer is a significant cause of morbidity and mortality worldwide. In Norway alone, there are more than 33,000 new cancer patients each year, and 11,000 cancer-associated deaths in 2017. A large proportion of these incidents are preventable. For example, a mass-screening program against cervical cancer established in the Nordic countries has demonstrated a reduction in morbidity and mortality almost by 80 %. Despite this success, it remains a significant challenge to improve the screening program, such as minimize over screening and undertreatment and hence reduce expenditure in a broad public health perspective.
Current knowledge about the disease, together with a wealth of available data and modern technologies, can offer far better-personalized prevention, by deriving an individual-based time till the next screening. Existing automatic decision support systems for cervical cancer prevention are, however, extremely conservative as they are mostly limited to identifying patients who are overdue for their next routine screening, without providing any personalized recommendations for follow-ups.
By intelligent use of existing registries and health data, DeCipher aims to develop a data-driven framework to provide a personalized time-varying risk assessment for cancer initiation and identify subgroups of individuals and factors leading to similar disease progression. By unveiling structure hidden in the data, we will develop novel theoretically grounded machine learning methods for the analysis of large-scale registry and health data.
DeCipher consists of an excellent multidisciplinary research team from diverse fields such as machine learning, data mining, screening programs, and epidemiology. Available to screening programs, clinicians, and individuals in the population, the DeCipher results will allow for an improvement of an individual’s preventive cancer healthcare while reducing the cost of screening programs.
SimulaMet’s Role
SimulaMet will play a central role in the development of machine learning algorithms for longitudinal screening data analysis. Moreover, as the coordinator, SimulaMet is responsible for overall project management and dissemination activities.
Funding source
Research Council of Norway, IKTPLUSS
All partners
Cancer Registry Norway
Karolinska University Hospital, Sweden
Lawrence Livermore National Lab, USA
Coordinator
SimulaMet
UPSKILL
An incomplete mapping of the skills of a given individual, combined with insufficient insight into a company's actual need for competence, give rise to quite a few challenges. For instance, it may lead to hiring the wrong candidates, lack of insight into the best path for personal development and challenges when deciding relevant content for courses, learning material and for continued education.
UPSKILL will introduce a global platform for professional networking. The platform will connect individuals, companies and learning providers, and offer automatic methods for identification, mapping, and development of skills and abilities.
The project will result in new methods for representing the skills of an individual, mapping a company's need for competence, as well as new methods for matching available skills and abilities with the actual need for
competence. The methods will be self-learning, applicable for commercial use and independent of industry.
As a result, the UPSKILL platform will lead to simplified and less expensive hiring and restructuring processes, reduced risk of hiring wrong candidates, free competence guidance for individuals, and content recommendation for learning providers. The platform will be launched in Europe and Southeast Asia after project completion.
Simula’s Role
Simula plays a central role in designing and developing data-driven algorithms for the automatic and unbiased hiring process, identification of individual’s competence profile from available data sources, and development of matching algorithms for potential employees and employers. The developed methods will form a solid foundation for the UPSKILL platform.
Funding source
Research Council of Norway, BIA
All partners
Simula Metropolitan Center for Digital Engineering
Oslo Metropolitan University
University of South-Eastern Norway
Coordinator
Conexus AS
Publications at Department of Machine Intelligence
Multiway Reliability Analysis of Mobile Broadband Networks
In the Internet Measurement ConferenceProceedings of the Internet Measurement Conference on - IMC '19. Amsterdam, NetherlandsNew York, New York, USA: ACM Press, 2019.Status: Published
Multiway Reliability Analysis of Mobile Broadband Networks
| Afilliation | Communication Systems, Machine Learning |
| Project(s) | The Center for Resilient Networks and Applications, Department of Machine Intelligence |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2019 |
| Conference Name | the Internet Measurement ConferenceProceedings of the Internet Measurement Conference on - IMC '19 |
| Pagination | 358-364 |
| Publisher | ACM Press |
| Place Published | Amsterdam, NetherlandsNew York, New York, USA |
| ISBN Number | 9781450369480 |
| URL | http://dl.acm.org/citation.cfm?doid=3355369http://dl.acm.org/citation.cf... |
| DOI | 10.1145/335536910.1145/3355369.3355591 |
Unraveling Interpretable Patterns through Data Fusion based on Coupled Matrix and Tensor Factorizations
In AI and Tensor Factorizations for Physical, Chemical, and Biological Systems, 2019.Status: Published
Unraveling Interpretable Patterns through Data Fusion based on Coupled Matrix and Tensor Factorizations
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Talks, invited |
| Year of Publication | 2019 |
| Location of Talk | AI and Tensor Factorizations for Physical, Chemical, and Biological Systems |
| Type of Talk | Invited |
Unraveling Interpretable Patterns through Data Fusion based on Coupled Matrix and Tensor Factorizations
In KDD Workshop on Tensor Methods for Emerging Data Science Challenges, Anchorage, Alaska , 2019.Status: Published
Unraveling Interpretable Patterns through Data Fusion based on Coupled Matrix and Tensor Factorizations
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Talks, invited |
| Year of Publication | 2019 |
| Location of Talk | KDD Workshop on Tensor Methods for Emerging Data Science Challenges, Anchorage, Alaska |
| Type of Talk | Keynote |
Biomarker Discovery through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data
In IEEE EMBC (Engineering in Medicine and Biology Conference), Berlin, Germany, 2019.Status: Accepted
Biomarker Discovery through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Talks, invited |
| Year of Publication | 2019 |
| Location of Talk | IEEE EMBC (Engineering in Medicine and Biology Conference), Berlin, Germany |
| Type of Talk | Invited Session Paper |
Unraveling Biomarkers through Multi-Modal Data Fusion
In IEEE ISMICT (International Symposium on Medical Information and Communication Technology), Oslo, Norway, 2019.Status: Published
Unraveling Biomarkers through Multi-Modal Data Fusion
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Talks, invited |
| Year of Publication | 2019 |
| Location of Talk | IEEE ISMICT (International Symposium on Medical Information and Communication Technology), Oslo, Norway |
| Type of Talk | Invited |
Unraveling Diagnostic Biomarkers of Schizophrenia Through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data
Frontiers in Neuroscience (2019).Status: Published
Unraveling Diagnostic Biomarkers of Schizophrenia Through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | Frontiers in Neuroscience |
| Date Published | Mar-05-2019 |
| Publisher | Frontiers |
| DOI | 10.3389/fnins.2019.00416 |
Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes in Schizophrenia
In ISCAS 2017: Proceedings of IEEE International Symposium on Circuits and Systems. IEEE, 2017.Status: Published
Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes in Schizophrenia
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2017 |
| Conference Name | ISCAS 2017: Proceedings of IEEE International Symposium on Circuits and Systems |
| Pagination | 314-317 |
| Publisher | IEEE |
| DOI | 10.1109/ISCAS.2017.8050303 |
Common and distinct components in data fusion
Journal of Chemometrics 31, no. 7 (2017): e2900.Status: Published
Common and distinct components in data fusion
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Journal Article |
| Year of Publication | 2017 |
| Journal | Journal of Chemometrics |
| Volume | 31 |
| Issue | 7 |
| Pagination | e2900 |
| Date Published | Jan-07-2017 |
| Publisher | Wiley |
| URL | http://doi.wiley.com/10.1002/cem.v31.7http://doi.wiley.com/10.1002/cem.2... |
| DOI | 10.1002/cem.v31.710.1002/cem.2900 |
Forecasting Chronic Diseases Using Data Fusion
Journal of Proteome Research 16, no. 7 (2017): 2435-2444.Status: Published
Forecasting Chronic Diseases Using Data Fusion
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Journal Article |
| Year of Publication | 2017 |
| Journal | Journal of Proteome Research |
| Volume | 16 |
| Issue | 7 |
| Pagination | 2435 - 2444 |
| Date Published | Jul-07-2017 |
| Publisher | ACS |
| ISSN | 1535-3893 |
| URL | http://pubs.acs.org/doi/10.1021/acs.jproteome.7b00039http://pubs.acs.org... |
| DOI | 10.1021/acs.jproteome.7b00039 |
ACMTF for Fusion of Multi-Modal Neuroimaging Data and Identification of Biomarkers
In EUSIPCO 2017: Proceedings of the 25th European Signal Processing Conference. IEEE, 2017.Status: Published
ACMTF for Fusion of Multi-Modal Neuroimaging Data and Identification of Biomarkers
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2017 |
| Conference Name | EUSIPCO 2017: Proceedings of the 25th European Signal Processing Conference |
| Publisher | IEEE |
| DOI | 10.23919/EUSIPCO.2017.8081286 |