About

I am a Ph.D. student at the Chair of Statistical Learning and Data Science at the LMU Munich since June 2020.
I received my BSc degrees in both Electrical Engineering and Physics from Bogazici University, Istanbul.
I completed an MSc degree in Communications Engineering at TU Munich.
My doctoral research is on the GenomeNet project, in collaboration with the Computational Biology of Infection Research team in the Helmholtz Centre for Infection Research. The project aims to develop suitable deep learning models for sequential genomics data. So far, I have worked on self-supervised learning, uncertainty quantification and model design using model-based optimization. Self-supervised learning techniques utilize big unlabeled datasets to boost the performance of machine learning models, especially when number of labeled data samples is limited ^[3]. Uncertainty quantification in machine learning targets evaluation and reduction of uncertainties in decisions of machine learning models ^[1].
I am part of the Methods Beyond Supervised Learning and Probabilistic Machine and Deep Learning research groups.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33, D-80539 München

anil.guenduez [at] stat.uni-muenchen.de

Research Interests

• Deep Learning for Genomics Data
• Self-supervised Learning
• Uncertainty Quantification
• Model-based Optimization
• Time Series Problems

You Can Find me on

• Google Scholar
• GitHub
• LinkedIn

References

1. Turkoglu, M. O., Becker, A., Gündüz, H. A., Rezaei, M., Bischl, B., Daudt, R. C., D’Aronco, S., Wegner, J. D., & Schindler, K. (2022). FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation. Advances in Neural Information Processing Systems (NeurIPS 2022).
   link | pdf
2. Hurmer, N., To, X.-Y., Binder, M., Gündüz, H. A., Münch, P. C., Mreches, R., McHardy, A. C., Bischl, B., & Rezaei, M. (2022). Transformer Model for Genome Sequence Analysis. LMRL Workshop - NeurIPS 2022.
   link | pdf
3. Gündüz, H. A., Binder, M., To, X.-Y., Mreches, R., Münch, P. C., McHardy, A. C., Bischl, B., & Rezaei, M. (2021). Self-GenomeNet: Self-supervised Learning with Reverse-Complement Context Prediction for Nucleotide-level Genomics Data.
   link | pdf
4. Mreches, R., To, X.-Y., Gündüz, H. A., Moosbauer, J., Klawitter, S., Deng, Z.-L., Robertson, G., Rezaei, M., Asgari, E., Franzosa, E. A., Huttenhower, C., Bischl, B., McHardy A. C., Binder, M., Münch, P. C. A platform for deep learning on (meta)genomic sequences
   pdf
5. Vahidi, A., Wimmer, L., Gündüz, H. A., Bischl, B., Rezaei, M. Uncertainty-Aware Self-Supervised Learning with Independent Sub-networks.
   link | pdf