SimulaMets research projects are both internally and externally financed. We collaborate with universities both in Norway and in other countries, as well as with industry.
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
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