You can't ML your way out of bad data, no matter how many parameters you add.
Dr. Marc-André Schulz
With a background in physics, Marc transitioned to machine learning and deep learning, specializing in the development and critical evaluation of machine learning methodologies for personalized psychiatry. Marc's research focuses on characterizing the constraints and limitations of these methods to assess if and under which conditions they offer potential for clinical translation.
You can't ML your way out of bad data, no matter how many parameters you add.
Present Positions And Title
PostDoc
Research Groups
Career
| Period | Institution | Role |
|---|---|---|
| Since 2024 | University of Tübingen | PostDoc |
| 2020 - 2024 | Charité - Universitätsmedizin Berlin | PostDoc |
| 2019 - 2020 | QuantumBlack / McKinsey | Academic Fellow - Data Science |
| 2017 - 2020 | RWTH Aachen University, Germany | PhD student |
| 2018 - 2019 | dida GmbH, Germany | Machine Learning Scientist |
Academic Education
| Year | Degree | Institution | Field of Study |
|---|---|---|---|
| 2024 | Dr. rer. nat. | Technische Universität Berlin, Germany | Machine learning |
| 2017 | B.Sc. | RWTH Aachen University, Germany | Physics |