Tilman is an automation manager, currently taking care of the reporting sub-stream in the Gemstone Ruby project.

What ever you do, do it with passion or et least with style! We in Medical Devices and eHealth stand for life changing combinations of medical devices together with our drugs. I´m proudly presenting the eHealth & Medical Software Solution team. Our joint vision is that every patient should get access to break trough eHealth solutions. Come and visit us - go/MDeH - or connect with me via Linkedin. Always keen to chat about "AI applied in med tech", "data mech in clinical practice", "if compliance and agility can coexist" & "how to get form a research hypothesis or concept to a data product"

Work in CTTS (Clinical Trial Technology Strategy). Was part of Clinical Operations where I worked in Data Driven Feasibility and recently co-led the initiatives for DCT. Work with Academic Institutions and external innovative companies from start up, large blue chip companies to top 5 tech companies.

Data Acquisition Technology Expert (Associate Director) at Bayer Healthcare Pharmaceuticals

Given the considerable amount of data that is collected throughout clinical trials, it can be asked if this data can be used to improve our understanding of the trial, regardless of outcome. The radiomics pilot seeks to provide a toolkit that is capable of analyzing the different modalities of information found in clinical trials (genome, gene expression, biomarker, clinical information, and imaging information) to derive insights into why a patient may be responding, or why a clinical trial was unsuccessful. The current efforts have focused on clinical trials in oncology, while collaborating with the RED-ONC function. Overall, the project is in its pilot state, and demonstrating a proof of concept, while documenting many challenges and learnings with the development of such a toolkit. Potential directions that could expand this toolbox include deriving the factors that best predict why a patient would stay in a clinical trial, and providing assistance with patient selection of clinical trials. Other potential directions involve suggesting why a particular trial may have been unsuccessful and providing that information back to the drug development team.

Our vision at Data Science Services & Solutions, DS3, is to Enable, Scale, and Operationalize R&D Data Science & AI capabilities to achieve Clinical Research and Development Speed, Efficiency, and Optimization. Learn more about our areas of responsibility, capabilities, strategic priorities, and collaborations. For more information, contact DS3 Head: Abi Velurethu. Artwork created using Stable Diffusion.

The Applied Machine Learning Group (AML) strives for the application of a data-driven approach to drug research and development by developing and utilizing best-in-class machine learning (ML) models to optimize R&D processes. The implementation of ML algorithms accelerates drug discovery and a platform to monitor and self-train models enables portfolio view of our capabilities, allowing us to become more focused. We envision to work in a fast-paced setting, we expect to fail quickly and adapt swiftly, all while expanding your ML capabilities. We also believe in making the right choices not only for our immediate team but decisions to build solutions catered to broader R&D. The success at AML will be measured by bucketing the benefit of the developed capability into one or more of these three buckets, (1) Savings in time (2) Savings in cost (3) Generation of new insights

Head of Product Owner Hub - Portfolio & Operations