Dr Thomas Heinis works in the area of management of big data, particularly on the development of software and hardware to improve the diagnosis of brain disease. These technological solutions seek to mimic the connections in the brain, making use of unsupervised learning to create a disease signature. This involves using different sets of patient data as input into the learning algorithm, to identify clusters of patients that have similar disease and understand how they relate to each other.
Thomas’ research focuses on the management of big data. Thomas develops software and hardware for problems that cannot be understood due to their complexity and hence can not be analysed in supercomputers. To understand these issues, Thomas is developing hardware that mimics the connections in the brain (“neuromorphic hardware”). Since the brain is still a much more sophisticated machine than the computer and there is a lot of scope for big data analytics to learn from the human machinery. Thomas also works on software development that is aligned with the neuromorphic hardware and, in doing this, he also incorporates machine learning. Currently Thomas is working on the Human Brain Project, where he analysis big health data, to find out how clusters of patients with similar brain disease relate to each other; however, the applications of this research go far beyond health care.
The biggest potential is the similarities between patients and treatments and the inclusion of this approach in processes like clinical trials or pharmacovigilance.
To help understand risk and help in the underwriting and pricing process.
- Market Research
To better understand market trends and customer behaviour.
To improve image recognition, same as the brain would do it.