About me
My Story
I am Instructor in Investigation at Massachusetts General Hospital, Harvard Medical School. Here, I apply my theoretical background combined with my interest in neuroimage analysis across the life-span to further research to improve our understanding of diseases, such as stroke, and the associated outcome for patients. In my current work, I promote the use of clinical magnetic resonance images in large scale analyses. My goal is the facilitation of translational research to the point where theoretical neuroimage analysis can be used to understand individual differences in patients. This will help support medical decision making and personalize treatment options for patients in order to improve their long-term outcome.
In 2005 I studied physics at the RWTH Aachen University and received my diploma in 2011. In the last year at the RWTH I focused on theoretical physics, in particular cosmology, and wrote my thesis on the halo mass function. During my studies I spent a year at Osaka University, working on the Terahertz Color Scanner at the Araki laboratory under supervision of Prof. Yasui and Prof. Aaraki.
From 2012-2015 I did my PhD at King’s College London, as part of the Division of Imaging Sciences and Biomedical Engineering and the Centre for the Developing Brain. I investigated the development of structural connectivity, in particular in premature babies. In order to do so I utilized network theory, as well as machine learning techniques to allow for group comparisons. The change from theoretical physics to biomedical engineering and research based on early brain development came from the desire to impact and improve people’s well-being. It allowed me to apply my computational and mathematical background, as well as my skills for problem solving to the growing field of connectomics in the developing brain.
In 2016 I started as a research fellow as part of the J. Philip Kistler Stroke Research Centre at the Massachusetts General Hospital and Harvard Medical School in Boston. My current work focuses on stroke patients, where I investigate the concept of reserve that helps a patient to a better outcome after stroke.
In 2017 I was honored to receive the Marie-Curie Global-Fellowship by the European Union, which supports me in my endeavors to bring methodological advances directly to the clinic, as part of the German Center for Neurodegenerative Disease Bonn, Germany, Harvard Medical School, and Massachusetts General Hospital. My research project ARTEMIS helped me translate many methodological advances into the low quality imaging setting of acute stroke populations, where data is acquired in the emergency room.
In 2020, I became head of the Computational Neuroradiology research group at the University Hospital Bonn. Incorporated into the Clinic for Neuroradiology, we studied clinical data sets and utilize our theoretical understanding of modern methodologies to bridge the gap between bench and bedside.
As of 2022, I am an instructor in investigation at Massachusetts General Hospital. The goal of my research is to further advance image analysis methodologies into the realm of real-world applications in stroke populations, with a specific focus on the DISCOVERY landmark study.
My Vision Of Teaching. Teaching, just as learning, is an exciting and integral part of an academic journey. It is my belief that there are three essential aspects that will help students find the same passion and dedication I see in my collaborators and myself in academia.
The first necessary element to achieve this goal is simply to have fun at what you do. Knowledge will stick with a student easier, if it is not forced onto them in a tedious way, which can be achieved by a mixture of lecture, variation in presentation style, as well as open exchange of experiences of success and failure related to the topics.
The second essential part of learning, which will further enhance the first, is by considering that most of what we learn has been proven useful in some area of life and across disciplines. There are applications to real world challenges in most cases which should be highlighted in any classroom. While it is essential to build a good theoretical foundation, many students are interested in how their new gained knowledge can be applied.
The third aspect combines and reinforces the first two. Instead of ‘dry’ education, the application of the learned elements to controlled environments through ‘experimentation’ not only reinforces the material, but also allows the learners to dive into issues that will inevitably occur while creating their own projects. Practical and ‘real world’ advice and interpretations build a cornerstone of what I consider key elements of academic guidance.
Looking back over my teaching experience, I found that these three elements are highly encouraging and enhance the learners curiosity for the subject matter. I have used these principles in all of my teaching settings. This included teaching theoretical physics, informatics, statistics, and machine learning to undergraduate and graduate students throughout the years. Teaching and learning is a journey, which I am excited to take together with my students every year.