We welcome Lennart Purucker as a new full-time Ph.D. student at the Intelligent Systems Group at the University of Siegen.

Lennart Purucker, Ph.D. Student at the University of Siegen, conducting research on Automated Machine Learning (AutoML)

Before joining our group, Lennart finished his Bachelor’s and Master’s degrees. He completed his bachelor’s degree in a dual study program at the Baden-Wuerttemberg Cooperative State University (DHBW) Stuttgart. Lennart did multiple internships at different companies and departments as part of this dual study program. Afterward, he completed his master’s degree at the RWTH Aachen University.

Lennart is interested in Automated Machine Learning (AutoML) and consequently, his Ph.D. will focus on enhancing AutoML. That is, improving the state-of-the-art, creating novel techniques, and extending AutoML concepts to new fields. The overall goal is to increase the effectiveness of AutoML and in this way help automate the tedious work of everyday data science.

Specifically, his research areas are embeddings of algorithms (like Algorithm-Performance Personas) and how such embeddings can be used for Meta-Learning or other fields of AutoML; exploring new techniques to enable Federated Meta-Learning, transferring applying AutoML techniques to Recommendation Systems (like AutoSuprise).

Automated Machine Learning (AutoML) is a subfield of machine learning that aims to automate the process of building and tuning machine learning models. This includes tasks such as selecting appropriate algorithms, optimizing hyperparameters, and preprocessing data. AutoML has gained significant attention in recent years as it has the potential to make machine learning more accessible to a wider range of users and organizations, without the need for specialized expertise.

Recent developments in AutoML have led to significant improvements in the accuracy and efficiency of machine learning models. This includes advances in neural architecture search, which automates the process of designing deep learning models, and Bayesian optimization, which is used to optimize the hyperparameters of machine learning models. Additionally, there has been a growing interest in AutoML in the industry, with many companies developing and using AutoML systems to improve their products and services.

A PhD in Automated Machine Learning is a good career choice for several reasons. Firstly, AutoML is a rapidly growing field and there is a high demand for experts in this area. Additionally, a PhD in AutoML provides the opportunity to work with leading researchers and access to cutting-edge technologies. Furthermore, a PhD in AutoML can lead to a career in academia, industry, or government research. And, as the field is advancing rapidly, the research skills and experience gained during a PhD will be valuable in the future job market.

Joeran Beel

Please visit https://isg.beel.org/people/joeran-beel/ for more details about me.


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