Lukas graduated with a Bachelor’s degree and subsequently with a Master’s degree in Medical Computer Science at the University of Siegen.
During his time as a student he worked with different chairs at the University of Siegen to research and publish a variety of problems and solutions in the domains of Machine Learning and Deep Learning (Anomaly Detection, Human Activity Recognition).
Accompanying the progression of his Master’s degree he also gathered practical experience in leading and executing a large-scale software project (a novel invoice management system) through an industrial job.
For his Ph.D. he will focus on Recommender Systems. He is currently working on related research in the field in which he studies the impact of data features in state-of-the-art algorithms.
In the future he will identify new or persisting issues in Recommender Systems and aim to find novel solutions for them, e.g. cold start, popularity bias, data shortage, scalability, sparsity, and so on…
Recommender systems are a subset of machine learning that are designed to predict the likelihood that a user will interact with an item. These systems are used in a variety of applications, including e-commerce, social media, and content platforms. They are commonly used to make personalized recommendations to users based on their past interactions, such as which products they have purchased or which videos they have watched.
Research in the field of recommender systems is important because it has the potential to improve the user experience and drive business growth. For example, in e-commerce, personalized recommendations can increase sales and customer engagement. Additionally, research in this field can also lead to advances in other areas of machine learning, such as natural language processing and computer vision.
There are many reasons why it is a good idea to do a PhD in recommender systems after completing a Masters. For one, a PhD allows for a deeper understanding of the field and the ability to make original contributions to the research. Additionally, a PhD provides opportunities to work with leading researchers and access to cutting-edge technologies. Additionally, the field of recommender systems is rapidly evolving and there is a high demand for experts in this area, making it a viable career path.
In summary, Recommender systems play an important role in personalizing the online experience for users, and research in the field has the potential to drive business growth and advances in other areas of machine learning. Pursuing a PhD in recommender systems after completing a Masters can lead to a deeper understanding of the field and opportunities for original research and professional advancement.