Let’s try to make sense of data by mapping it into two dimensions.
Jan Niklas Böhm
Nik is a PhD student in the Department of Data Science at the Hertie Institute for AI in Brain Health at the University of Tübingen and the IMPRS-IS graduate school. He is interested in dimensionality reduction techniques for high-dimensional data. Learning good and compact representations in an unsupervised setting is a key part of that, some examples include contrastive learning in the form of t-SimCNE and t-SNE.
Let’s try to make sense of data by mapping it into two dimensions.
Present Positions And Title
PhD Student
Research Group
Email Address
Phone
Career
| Period | Institution | Role |
|---|---|---|
| Since 2021 | University of Tübingen | PhD student |
| 2019 | CERN | Summer student |
| 2018 | Bosch | Student employee |
| 2018 | University of Tübingen | Teaching assistant |
| 2017 | German Research Center for Artificial Intelligence, Kaiserslautern | Research assistant |
| 2015 - 2017 | RheinMain University of Applied Sciences | Teaching assistant |
Academic Education
| Year | Degree | Institution | Field of Study |
|---|---|---|---|
| 2017 - 2020 | M.Sc. | University of Tübingen | Computer Science |
| 2019 | M.Sc. | University of Amsterdam | Exchange Semester |
| 2014 - 2017 | B.Sc. | RheinMain University of Applied Sciences | Applied Computer Science |