Vollmer Research Group is a constellation of researchers who—while geographically and institutionally spread—are united under the supervision of Professor Sebastian Vollmer, with a shared goal of advancing data science methods and tools, and using them across industrial and socially-important applications.
Our main homes are the German Research Center for Artificial Intelligence (DFKI) , where we form the Data Science and its Applications research department, and TU Kaiserslautern, where we form the Applied Machine Learning group.
Vollmer Research Group currently focuses on four major streams:
- Bayesian methodology and Monte Carlo Methods
- the development of novel methodologies and the extension of established theoretical works pertaining to machine learning.
- Machine Learning in Julia
- Development of the software package Machine Learning in Julia (MLJ), a modelling toolbox providing a common interface and meta-algorithms for selecting, tuning, evaluating and building composite models.
- Applications of machine learning with a focus on health care
- The research group has applied these methodologies to develop tools to generate actionable insights to inform the British government’s response to Covid-19, perform treatment selection for diabetes and predict emergency admissions in Scotland.
- Data Science for Social Good
- We use our knowledge in these areas to champion new ways of delivering impact, through short term collaborations with industrial, nonprofit and academic partners to address real-world challenges in programs that combine training and delivery.
Latest News
On March 18, 2022, German Chancellor Olaf Scholz visited the DFKI site in Kaiserslautern together with the Minister President of Rhineland-Palatinate, Malu Dreyer.
On Wednesday the 19th of January, 2022, Prof Sebastian Vollmer was acting as a witness in an evidence session of the UK House of Commons Science and Technology Committee on reproducibility and research integrity
I have received many questions about survival neural networks (‘survival networks’) in R, ranging from “is this even possible?” to “how do I install Python in R?” and “how can I tune these models?”. If you are an R user with an interest in survival networks then this is the article for you!
Short account of Harry’s internship
MLJ (Machine Learning in Julia) is a toolbox written in Julia providing a common interface and meta-algorithms for selecting, tuning, evaluating, composing and comparing machine learning models written in Julia and other languages.
