Richard Polzin — AI Researcher & Computational Biomedicine

About me

Hey there! I'm a medical AI researcher based in Aachen, Germany, working with Prof. Schuppert's team at the Joint Research Center of Computational Biomedicine.

My work sits at the intersection of healthcare and technology — I build predictive models using AI and Machine Learning to help improve patient outcomes, particularly with time-series data. I'm also passionate about making High Performance Computing more accessible to researchers at German University Hospitals through the NHR4CES Simulation and Data Lab.

When I'm not diving into data, you'll probably find me hunting for the perfect cup of coffee or getting creative with photography, video, or music.

What i'm doing

Project Conceptualization

Bridging the gap between disciplines — because the best ideas come from collaboration.
Open Source

Championing reproducible research through good software practices and shared tools.
Modern Presentation

Making science accessible and engaging — no more death by PowerPoint!
Media

Capturing moments and telling stories through photo, video, and creative projects.

Resume

Education

Postdoc at JRC COMBINE
2024 — Present
Fresh off my PhD, I'm continuing to push the boundaries of computational biomedicine with Prof. Schuppert's team — now with more autonomy and exciting new challenges.
Ph.D. at JRC COMBINE
2018 — 2024
Six years of deep diving into ARDS prediction for ICU patients. Built predictive models, learned a ton about clinical data, and discovered that healthcare AI is exactly where I want to be.
M.Sc. at Maastricht University
2016 — 2018
Fell in love with NLP and machine learning here. Spent my days exploring sentiment analysis, topic modeling, and getting my hands dirty with deep learning.
B.Sc. in Aachen, Germany
2013 — 2016
Where it all started — computer science and math fundamentals. Also landed my first real programming gig at a CNC simulation company, which taught me that coding is way more fun when it solves real problems.

Projects

EDITH CSA
2023 — Present
Working on the future of digital twins in healthcare — helping chart the roadmap from single-organ models to fully integrated whole-body simulations. It's ambitious, and that's what makes it exciting.
SDL Digital Patient
2023 — Present
Making supercomputers accessible to healthcare researchers who have great data but maybe not the HPC expertise. We're bridging that gap so clinicians can run serious simulations without needing a CS degree.
CCLS
2023 — Present
Part of RWTH's push to bring computational methods to life sciences. I focus on getting the word out, running training programs, and helping researchers see what's possible when biology meets computing.
SMITH
2018 — 2023
My PhD home base. Built an ARDS prediction system for ICU patients as part of the ASIC use case — turning messy clinical data into actionable early warnings.