About

I started as a PhD student at the Statistical Learning & Data Science working group in May 2022. I obtained a Bachelor's degree (B.A.) in Statistics and a consecutive Master's degree (M.Sc.) in Biostatistics from LMU Munich. My main research focus is on deep representation learning, specifically semi-supervised learning for genome sequence classification and analysis, and I am involved in the GenomeNet project, in collaboration with the Computational Biology of Infection Research team in the Helmholtz Centre for Infection Research.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

x.to [at] stat.uni-muenchen.de

Research Interests

My main research interest include:

• Natural Language Processing
• Self-supervised Learning
• Semi-supervised Learning
• Deep Learning in general

You Can Find me on

• Google Scholar
• GitHub
• LinkedIn

References

1. Gündüz HA, Binder M, To X-Y, Mreches R, Bischl B, McHardy AC, Münch PC, Rezaei M (2023) A self-supervised deep learning method for data-efficient training in genomics. Communications Biology 6, 928.
   link|pdf.
2. Münch P, Mreches R, To X-Y, Gündüz HA, Moosbauer J, Klawitter S, Deng Z-L, Robertson G, Rezaei M, Asgari E, Franzosa E, Huttenhower C, Bischl B, McHardy A, Binder M (2023) A platform for deep learning on (meta)genomic sequences (preprint).
   link | pdf.
3. Hurmer N, To X-Y, Binder M, Gündüz HA, Münch PC, Mreches R, McHardy AC, Bischl B, Rezaei M (2022) Transformer Model for Genome Sequence Analysis LMRL Workshop - NeurIPS 2022,
   link | pdf .
4. Gündüz HA, Binder M, To X-Y, Mreches R, Münch PC, McHardy AC, Bischl B, Rezaei M (2021) Self-GenomeNet: Self-supervised Learning with Reverse-Complement Context Prediction for Nucleotide-level Genomics Data.
   link | pdf .