Theses

Masters and Bachelors theses

If you are interested in a Masters or Bachelor thesis project in our group we are happy to propose a concrete problem related to our current interests. Our group focuses on i) machine learning and optimization, in particular ii) learning from few and or noisy data, iii) deep learning for inverse problems, iv) active learning, and v) DNA data storage and DNA information technologies. To get an idea about our current research projects, please check out our recent papers at google scholar. Projects usually involve a mixture of theory and applied work and require strong interest documented by excellent grades in relevant subjects such as basic math courses (linear algebra, probability, and statistics), machine learning, signal processing, and/or optimization.

If you are interested in a project, please send an email with i) information about which topic you are generally interested in, ii) what the timeline is (rough start and end dates), and iii) your curriculum vitae and transcript of records to: reinhard.heckel@tum.de. Below, we have an incomplete list of open topics, but we typically have several other potential projects in our research areas.

External Masters and Bachelors thesis

We are not interested in supervising Masters and Bachelors theses carried out externally in a company, unless they are very related to one of our current research projects.

Open projects

  • Imaging through atmospheric turbulence: We're looking for a Master's student to work on deep neural network based methods for imaging through atmospheric turbulence. You would implement several method in pytorch and evaluate the methods on data.
  • Estimating the age of a DNA sample from sequencing data: We’re looking for a Master’s student to develop, implement, and test an idea to estimate the age of a given DNA sample from sequencing data alone. First you would perform literature research on existing approaches and then implement and test a few concrete ideas in python. Interest in interdisciplinary work and data analysis is desired.
  • Cardiac magnetic resonance imaging with neural networks: We’re looking for a Master’s student to work on a new deep neural network based method for imaging a non-static object, specifically a beating heart. You would implement an idea for a neural network based method in pytorch, evaluate the method on real data within a collaboration with the University Hospital, and there is also room for developing your own ideas.

Current and past theses in the group

Johannes Kunz ``Dynamic MRI reconstruction’’, Project/Forschungspraxis, 2022

Weixing Wang, ``Graph neural networks for clustering and aligning DNA sequences for DNA storage'', Project/Forschungspraxis, 2022

Yundi Zhang, ``Coordinate-based image priors'', Master's thesis, 2022.

Samuel Eadie, ``Rate-Distortion Stochastic Autoencoding for Robust Representation Learning and Out-of-Distribution Detection'' (carried out at Bosch Research), Master's thesis, 2022

Kang Lin, ``Transformers for image recovery'', Master's thesis, 2021.

Frederik Fraaz, ``Image recovery with invertible neural networks'', Master's thesis, 2021.

Youssef Mansour, ``CT imaging with deep learning'', Master's thesis, 2021.

Benedikt Böck, ``Multiplicative filter networks for image processing applications'', Project/Forschungspraxis, 2021

Mohamed Ketata,  ``Data standardisation, multi-domain learning, and artifact robustness for improved MRI'', Bachelor's thesis, 2021.

Deniz Uysal, ``A simple encoder and decoder for DNA data storage with Polar codes'', Project/Forschungspraxis, 2021.

Yundi Zhang, ``Deep matrix decoder for collaborative filtering'', Project/Forschungspraxis, 2021.

Youssef Mansour, ``Ensembles of image reconstruction method for MRI'', Project/Forschungspraxis, 2021.

Jacob Geussen, ``Diffusion MRI denoising with neural networks'', Bachelor's thesis, 2020.

Lena Heidemann, ``FastMRI with untrained neural networks'', Master's thesis, 2020.

Tobit Klug, ``Image separation with untrained neural networks'', Master's thesis, 2020.

Oleksii Khakhlyuk, ``Convolutional neural networks with fixed kernels'', Bachelor's thesis, 2019. 

Zi Yang, ``Probabilistic matching networks for few-shot learning'', Master's thesis, 2019.