Theses
Masters and Bachelors theses and research internships
If you are interested in a Masters or Bachelor thesis project or a research internship (Forschungspraxis) in our group we are happy to propose a concrete problem related to our current interests. Our group focuses on machine learning and optimization, deep learning for inverse problems, and DNA data storage and DNA information technologies. To get an idea about our current research, 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 linear algebra, probability and statistics, machine learning, signal processing, optimization, or related courses.
Below is a list of open topics. If you are interested in one of the projects, please send an email directly to the superviser and cc reinhard.heckel(at)tum.de. Include your transcript of records from TUM and your curriculum vitae and the planned start and end dates.
The list of topics is sometimes incomplete, and we are also happy to propose other topics if there is a good fit. If you are interested in a topic related to our current interests that is not listed below, please reach out to reinhard.heckel(at)tum.de and again include your transcript of records and CV.
External projects: If you are planning to carry our our project externally, for example at a company or another university, and you want us to supervise the project, please send an email to reinhard.heckel(at)tum.de including the name and contact of the exernal supervisor, your transcipt of records and CV, and a project description and an explanation how the project is related to our expertise. We can only supervise external projects if they are related to our expertise and current research interests.
Thesis and project administration and logistics: Here are some guidelines on the thesis and research project administration and on the grading.
Open projects
Deep learning based fetal magnetocardiography system
We are looking for a Master’s student to work on deep learning based reconstruction for a fetal magnetocardiography system, in a interdisciplinary collaboration with Prof. Fierlinger and Prof. Wacker-Gussmann. The goal of the thesis is to develop deep learning based signal reconstruction techniques for this new medical imaging and sensing technology.
Supervisor: Reinhard Heckel (reinhard.heckel(at)tum.de)
Prerequisites: Strong coding skills, specially in pytorch. A good understanding of deep learning for inverse problems.
Type of project: Masters thesis
3D PET Self-Supervised Image Reconstruction
Self-supervised reconstruction is beneficial in PET imaging, where ground truth images are hard to obtain. We have developed a 2D self-supervised reconstruction method that has been thoroughly tested on simulated data. We are looking for a Master’s student to extend this method to 3D and evaluate it on real data.
Supervisors: Youssef Mansour (y.mansour(at)tum.de), Reinhard Heckel (reinhard.heckel(at)tum.de), Georg Schramm from KU Leuven
Prerequisites: Strong coding skills, especially in PyTorch. A solid understanding of deep learning frameworks and image reconstruction.
Type of project: Master’s thesis
A new loss function for Spatio-Temporal Denoising based on analyzing noise characteristics
ARRI is looking for a student to optimize a new loss function for deep learning-based video denoising. The aim is to achieve high-quality, natural-looking results, with a focus on temporal consistency, essential for high-end film production. The project involves optimizing the loss function and fine-tuning the model architecture to align with real-world noise characteristics and visual expectations.
Supervisor: This project is supervised by Dr. Seybold at ARRI and carried out at ARRI, the supervision from the TUM side is by a PhD Student in the group.
Prerequisites: Strong PyTorch skills, experience in deep learning for image/video processing.
Type of project: Bachelor’s/Master’s thesis or Research Internship.
Studying the Torn Paper Channel in the Presence of Edit Errors as Inspired by the DNA Decay Process in DNA-Based Storage Systems
We are looking for a Master’s student to work on theoretical problems arising from the DNA decay process observed in DNA-based storage systems. The goal of the thesis is to investigate certain questions related to the noisy torn paper channel in which a sequence is first corrupted by edit errors (such as insertions, deletions, and substitutions) and is then broken to shorter fragments. At the end, we need to reconstruct the original sequence from the set of the obtained noisy fragments. Many theoretical questions arise under this paradigm and we will investigate some of them.
Supervisor: Maria Abu-Sini (maria.abu-sini(at)tum.de)
Prerequisites: Good knowledge in the fields of coding theory and information theory. The courses Channel Coding and Information Theory given at the Institute of Communication Engineering are highly recommended.
Type of project: Masters thesis
Current and past theses in the group
Jakub Dvorak, ``Diffusion Prior for Reconstruction in Cryo-ET’’, Master’s thesis, ongoing
Diyor Khayrutdinov, ``Denoising at Test Time for Better Membrane Segmentation of Cryo-Electron Tomograms’’, Master’s thesis, ongoing
Xiufeng Yang, ``Retrieval and generation based test-time-training for imaging’’, Master’s thesis, ongoing
Kai Eberl, ``Foundation Models prior for Image Restoration’’, Master’s thesis, ongoing
Jonas Emrich, ``Cardiography’’, Master’s thesis, ongoing
Diyor Khayrutdinov, ``Denoising at Test Time for Better Membrane Segmentation of Cryo-Electron Tomograms’‘, Project/Forschungspraxis, 2025
Julian Streit, ``Addressing Synchronization Uncertainty in Foundational Error Correction Models’‘, Master’s thesis, 2025
Zeineb Ben Chaben, ``Self-Supervised PET Image Reconstruction from Subsampled Data with Variational Networks’‘, Master’s thesis, 2025
Moritz Bauman, ``Multiple Sequence Base Calling for DNA data storage’’, Project/Forschungspraxis, 2025
Oliver Kovacs, ``Adapting SAM for Data-Efficient Particle Picking in Cryo-ET’‘, Master’s thesis, 2024
Oliver Kovacs, ``Developing a VarNet for DeepDeWedge’’, Project/Forschungspraxis, 2024
Jakub Dvorak, ``Simultaneous Self Supervised Image Denoising and Deconvolution’’, Project/Forschungspraxis, 2024
Isabel Schorr, ``DPO’’, Master's thesis, 2024
Andreas Faika, ``Optimizing data mixtures ’’, Master's thesis, 2024
Tim Lindenau, ``Optimization of mixing proportions with zero-shot optimization’’, Project/Forschungspraxis, 2024
Serden Sait Eranil, ``Signal processing for fetal magnetocardiography’’, Project/Forschungspraxis, 2024
Kun Wang, ``Motion reconstrution for MRI’’, Master's thesis, 2024
Claudio Kaserer, ``Improving Mathematical Reasoning of Language Models Using Supervision Data’’, Master’s thesis, 2024
Serdar Caglar, ``Test time training for denoising distribution shifts’’, Project/Forschungspraxis, 2024
Andreas Faika, ``Comparison of Tokenizers for LLMs’’, Project/Forschungspraxis, 2024
Raimundo Parra, ``Unintentional Bilingualism in Large Language Models’’, Project/Forschungspraxis, 2024
Mamdouh Aljoud, ``Filtering techniques in next generation multimodal datasets’’, Master's thesis, 2023
Cheng Yan, ``Meta-Learning for mulit-task MRI reconstruction’’, Master's thesis, 2023
Francesco Bollero, ``Data pruning for image reconstruction’’, Master's thesis, 2023
Faidra Patsatzi, ``Randomized smoothing for inverse problems’’, Bachelor's thesis, 2023
Xiaodong Lei, ``Adversarial robustness of deblurring methods’’, Project/Forschungspraxis, 2023
Dogukan Atik, ``Scaling laws for self-supervised image denoising’’, Master's thesis, 2023
Mamdouh Aljoud, ``Deep networks for nanopore basecalling’’, Project/Forschungspraxis, 2023
Rafael Vorländer, ``Reimplementing CryoGAN’’, Project/Forschungspraxis, 2023
Juan Cao, ``Uncertainty quantification methods for compressed sensing’’, Project/Forschungspraxis, 2023
Litao Li, ``Datawork for accelerated MRI’’, Master's thesis, 2023
Guang Chai, ``Evaluating deep-learning based imaging systems’’, Master's thesis, 2023
Johannes Kunz, ``Generative models for Cardiac Magnetic Resonance Imaging’’, Master's thesis, 2023
Ali Can, ``Understanding the Contribution of Training Samples on a Prediction of a Single Test Image in a Denoising Task Using Attention Mechanism’’, Project/Forschungspraxis, 2022
Deniz Uysal, ``Spectral Computed Tomography Image Reconstruction’’, Master's thesis, 2022
Xuyang Zhong, ``Self-Supervised Learning for Image Denoising’’, Master's thesis, 2022
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, ``Neural network architectures for image recovery and denoising'', 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.