The international community is facing numerous global issues, as highlighted by the UN, including challenges related to sustainable development, health, and climate change. Finding solutions to these global issues requires extensive research and collaboration across various disciplines. The use of data and technology is integral to driving innovation towards resolving these issues. However, effective solutions require state-of-the-art data science and information systems knowledge. Data science and the associated knowledge of modeling, algorithms, and systems help facilitate the transformation of structured and unstructured data into actionable information that can guide interventions aimed at solving these issues. Knowledge of information systems is also essential to support these interventions in varied organizational and institutional contexts through the use of digital technology.
Climate and health are interconnected, and climate changes pose significant threats to both the social and environmental determinants of health. It is in fact described as "the biggest health threat facing humanity". The fusion of climate and health studies is known as "climate health". We are a group of data science and IS researchers associated with the bioinformatics and IS research groups at UiO, who are eager to use machine learning to tackle the climate health challenges that lie ahead.
If you share our interest in contributing towards alleviating the global climate crisis, we invite you to join us in one of our several climate health projects that are being funded by international donors. Our projects are anchored in the HISP center (https://www.mn.uio.no/hisp/english) and managed by:
- Professor Geir Kjetil Ferkingstad Sandve
- Associate professor Silvia Masiero
- Associate professor Sune Dueholm Müller
Master thesis project ideas:
How to use machine learning to predict and mitigate...
- outbreaks of climate and waterborne diarrhea and Dengue fever
- extreme weather events (e.g., drought and flooding) and associated problems with nutrition
- sudden changes in Malaria cases due to changing weather patterns
... and make the resulting information accessible and actionable through digital technology and the people working with it.
One reason that climate change is considered among the biggest health threats is that it has so many different and complex influences on human health. This means that a very large number of studies will need to be initiated internationally to predict and adapt to the myriad different consequences of climate change on health. To help make such studies more efficient, we are also interested in developing core computational resources that can streamline the initiation, development and quality control of specific analysis in the climate health domain. Such generic computational resources include:
- Flexible simulation frameworks for generating climate health data that can guide and stress test current and future machine learning methodologies
- Software libraries for handling common tasks in climate health (machine learning) analysis
- Software platforms for integrating climate health machine learning solutions in a way that improves accessibility, interoperability, and reproducibility of the integrated solutions
- Automated benchmarks for machine learning methods in the climate health field, with appropriate selection of data sets and evaluation metrics for benchmarking
We welcome both non-technical and technical IS students who are interested in the research described.