Katharina Reusch


Katharina Reusch


Researcher & Data Scientist


IBM Research Europe - United Kingdom Daresbury, England


Katharina has a background in Neuroscience and did her PhD in Electrical and Electronic Engineering at the University of Nottingham (UK) as part of a highly interdisciplinary team of researchers, building semi-artificial brains. After she graduated, she joined IBM as a Technical Consultant in the IBM Big Data Analytics division, where she worked as a data scientist on a variety of projects and industries, such as retail, transport, internet of things and geology, all with a strong focus on large scale data analytics in the Hadoop and Spark ecosystem.

In May 2016, Katharina joined the the IBM Research UK team where she contunied to focus on large-scale analytics projects. She quickly excelled in the position and is now in the exiting position to share both managerial and technical responsibilities at IBM Research. In her technical role she continues to work as a research data scientist from agriculture to transport, healthcare to retail. Her focus is on data science and machine learning methods, analysing large, complex data sets at scale with the help of High Performance Computers where necessary. By gaining deep expertise in installing, configuring and extensively using Big Data technologies such as the Hadoop Open Stack environment (HBase, Hive etc) with Spark, Python and machine learning libraries such as SciKit learn and Deep Learning tools such as Tensorflow, she likes to go to the edge of what is currently possible and discover the new and unknown, to not only use Big Data tools but bring them to the next level. 

For her managerial role, she manages a team of scientists, software engineers and system administrators to guide them through a successful career in IBM and shape their future. She is a passionate and highly motivated leader and manager, hoping for my team to find the best versions of themselves, achieve and exceed their goals and be proud of their achievements every step along the way in their career.




Auto-omics for healthcare and drug discovery

An automated explainable bioinformatics and AI workflow for multi-omic, clinical and experimental data, applied to healthcare and drug discovery problems.

Auto-omics for climate and sustainability

An automated explainable bioinformatics and AI workflow for multi-omic, climate and environmental data, applied to sustainability problems e.g., nature-based carbon capture.

Top collaborators

Blair Edwards

Blair Edwards

STSM - Climate & Sustainability - Geospatial Data and Modelling
Paolo Fraccaro

Paolo Fraccaro

Staff Research Scientist in Data Science and Artificial Intelligence