The Predictive Society and Data Analytics Lab pursues innovative research in data science. That means we are creatively developing and applying high-dimensional network-based methods in machine learning, statistics and artificial intelligence that can be used for knowledge extraction from data.
Examples for data we are analyzing are high-throughput data from genomic experiments. For instance, the data can come from gene expression, methylation or RNA-seq experiments, e.g., generated from next-generation sequencing (NGS) technologies. In this context, we are mainly interested in biomedical and health related questions of complex disorders and their relation to basic biological mechanisms. We have experience working with data from many cancer types, including, breast cancer, colon cancer, bladder cancer, prostate cancer, kidney cancer and lymphoma but we are also involved in studies of cystic fibrosis, asthma and diabetes. In addition, we are working on text mining problems, e.g., from electronic health records (EHR) of patients, for automizing clinical prediction tasks. All of these studies are conducted in close collaboration with biologists and clinicians.
Other types of data we are analyzing come from the stock market, social media, social sciences, marketing, online gaming or business and industrial processes. We are particularly interested in studying ways to forecast or predict the system behavior, like gene functioning, consumer behavior or human personality, and utilize for this network-based approaches in combination with a statistical framework. Examples for indistrial partners are Cargotec (Finland) and Hughes insurance (UK).
We are conducting data-driven research for making impactful predictions on the societal level.
Our general research interest is in data science within the following fields:
• Machine Learning/Artificial Intelligence
• Computational Biology
• Network Science
• Computational Social Science
Our publication statistics (see also google scholar):
• Journal Articles: 181
• Conference Papers: 29
• Book Chapters: 14
• Books: 16
Our team consists of:
• PhD students: 2
• Research associates: 2