Group members
Titel | Name | Position/Tätigkeiten | Kontakt |
|---|---|---|---|
| Elke Schubert | Office Assistant | elke.schubert(at)dzne.de | |
| Dr. | Marie Oestreich | Postdoc | marie.oestreich(at)dzne.de |
| Shubhi Ambast | PhD Student | shubhi.ambast(at)dzne.de | |
| Charles Mwangi Kaumbutha | PhD Student | charlesmwangi.kaumbutha(at)dzne.de | |
| Karola Mai | PhD Student | karola.mai(at)dzne.de | |
| Dayoung Lee | Master Student | dayoung.lee(at)dzne.de | |
| Philip Horvat | Labrotation Student | philiplucien.horvat(at)dzne.de |
Shubhi Ambast
I am interested in applying deep learning methods, particularly graph neural networks, to unravel complex biological insights from datasets such as those generated by single-cell transcriptomics. I am also intrigued by the use of explainable AI to interpret and validate my research, providing a deeper understanding of the underlying biological mechanisms in healthcare research.
Karola Mai
My research focuses on the PriSyn project, where I evaluate the biological plausibility of generated synthetic scRNAseq data and work on integrating scRNAseq data with genotype information. Additionally, leveraging my hands-on experience in flow cytometry, I now use machine learning methods to enhance the standardisation and scalability of flow cytometry data analysis.
Charles Mwangi Kaumbutha
My research interest is in Meta-Data Analysis particularly addressing missing data. Currently, my primary focus is examining evaluation metrics for imputation methods, to ensure quality downstream machine learning tasks such as classification.
Marie Oestreich
My research focuses on chemoinformatics, specifically on developing deep generative models that propose novel small molecules to speed up the drug development process. In this context, I further investigate chemically-informed embedding strategies for molecules to maximise chemical information content during training of downstream deep learning models.