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
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
Book Chapter
Multilinear Models, Iterative Methods☆
In Reference Module in Chemistry, Molecular Sciences and Chemical Engineering. Elsevier, 2020.Status: Published
Multilinear Models, Iterative Methods☆
In this section, multilinear models for multi-way arrays requiring iterative fitting algorithms are outlined. Among them: the PARAFAC (PARAllel FACtor analysis) model and one of its variants (the PARAFAC2 model); Tucker models in which one or more modes are reduced (viz., the N-way Tucker-N and Tucker-m models); hybrid models having intermediate properties between PARAFAC and Tucker ones; and coupled matrix and tensor decompositions (CMTF) which simultaneously decomposes multiple tensors. Five examples are included as to illustrate some practical aspects concerning the use of these models on analytical data.
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Book Chapter |
| Year of Publication | 2020 |
| Book Title | Reference Module in Chemistry, Molecular Sciences and Chemical Engineering |
| Publisher | Elsevier |
| ISBN Number | 978-0-12-409547-2 |
| Keywords | -way Tucker models, CANDECOMP, Curve resolution, Exploratory analysis, Least squares, Linked mode PARAFAC, Multi-way analysis, Multi-way array, Multilinear model, PARAFAC, PARAFAC2, PARALIND, Restricted Tucker models, Tensor decomposition, Tensor-matrix factorization |
| URL | http://www.sciencedirect.com/science/article/pii/B9780124095472146098 |
| DOI | 10.1016/B978-0-12-409547-2.14609-8 |
Proceedings, refereed
Tracing Network Evolution Using The Parafac2 Model
In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona, Spain: IEEE, 2020.Status: Published
Tracing Network Evolution Using The Parafac2 Model
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Proceedings, refereed |
| Year of Publication | 2020 |
| Conference Name | ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Publisher | IEEE |
| Place Published | Barcelona, Spain |
| URL | https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9040208http... |
| DOI | 10.1109/ICASSP40776.202010.1109/ICASSP40776.2020.9053902 |
Journal Article
Unraveling Diagnostic Biomarkers of Schizophrenia Through Structure-Revealing Fusion of Multi-Modal Neuroimaging Data
Frontiers in Neuroscience 13 (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 |
| Volume | 13 |
| Date Published | Mar-05-2019 |
| Publisher | Frontiers |
| DOI | 10.3389/fnins.2019.00416 |
Biomarkers of individual foods, and separation of diets using untargeted LC-MS based plasma metabolomics in a randomized controlled trial
Molecular Nutrition & Food Research 63 (2019): 1-10.Status: Published
Biomarkers of individual foods, and separation of diets using untargeted LC-MS based plasma metabolomics in a randomized controlled trial
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Journal Article |
| Year of Publication | 2019 |
| Journal | Molecular Nutrition & Food Research |
| Volume | 63 |
| Number | 1800215 |
| Pagination | 1-10 |
| Publisher | Wiley |
| DOI | 10.1002/mnfr.201800215 |
Proceedings, refereed
Multiway Reliability Analysis of Mobile Broadband Networks
In the Internet Measurement ConferenceProceedings of the Internet Measurement Conference on - IMC '19. New York, NY, 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 | New York, NY, 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 |
Talks, invited
Unraveling Interpretable Patterns through Data Fusion based on Coupled Matrix and Tensor Factorizations
In AI and Tensor Factorizations for Physical, Chemical, and Biological Systems, Santa Fe, NM, USA, 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, Santa Fe, NM, USA |
| 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, USA, 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, USA |
| 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: Published
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 |
Journal Article
The molecular fingerprint of fluorescent natural organic matter offers insight into biogeochemical sources and diagenetic state
Analytical Chemistry 90, no. 24 (2018): 14188-14197.Status: Published
The molecular fingerprint of fluorescent natural organic matter offers insight into biogeochemical sources and diagenetic state
| Afilliation | Machine Learning |
| Project(s) | Department of Machine Intelligence |
| Publication Type | Journal Article |
| Year of Publication | 2018 |
| Journal | Analytical Chemistry |
| Volume | 90 |
| Issue | 24 |
| Pagination | 14188–14197 |
| Publisher | ACS |
| ISSN | 0003-2700 |
| URL | http://pubs.acs.org/doi/10.1021/acs.analchem.8b02863http://pubs.acs.org/... |
| DOI | 10.1021/acs.analchem.8b02863 |