Thesis Topics
This list includes topics for potential bachelor or master theses, guided research, projects, seminars, and other activities. Search with Ctrl+F for desired keywords, e.g. ‘machine learning’ or others.
PLEASE NOTE: If you are interested in any of these topics, click the respective supervisor link to send a message with a simple CV, grade sheet, and topic ideas (if any). We will answer shortly.
Of course, your own ideas are always welcome!
Segmentation of Shoe Trace Images
Type of Work:
- Master
Keywords:
- benchmarking
- deep learning
- image segmentation
- keypoint extraction
- self-attention
Description:
Help fight crime with AI! The DFKI and the Artificial Intelligence Transferlab of the State Criminal Police Office (Landskriminalamt) are searching for master candidates eager to apply their knowledge in AI to support crime scene analysis. The student will have the opportunity to visit the Transferlab in Mainz for an in-depth introduction to the topic and full access to DFKI’s computing cluster infrastructure.
General goal: improve identification of specific markers normally present in shoe trace images acquired in crime scenes.
Specific goals:
- [benchmarking] evaluate existing image segmentation models in the context of shoe trace analysis;
- [research] propose a segmentation model combining semantics and keypoint information tailored to specific markers present in crime scene photographs;
- [research] assess model performance on labeled data.
- [research] definition of limits and requirements for the existing training- and test-data.
Retrieval of Shoe Sole Images
Type of Work:
- Master
Keywords:
- benchmarking
- deep learning
- graph neural networks
- image retrieval
- keypoint extraction
Description:
Help fight crime with AI! The DFKI and the Artificial Intelligence Transferlab of the State Criminal Police Office (Landskriminalamt) are searching for master candidates eager to apply their knowledge in AI to support crime scene analysis. The student will have the opportunity to visit the Transferlab in Mainz for an in-depth introduction to the topic and full access to DFKI’s computing cluster infrastructure.
General goal: improve retrieval of shoe sole images acquired in laboratory, i.e. under controlled conditions and used as reference by forensics specialists.
Specific goals:
- [benchmarking] evaluate existing image retrieval approaches in the context of shoe trace recognition;
- [research] propose a graph network architecture based on keypoint information extracted from the images.
- [research] evaluate performance of proposed model against existing methods.
Sherlock Holmes goes AI - Generative comics art of detective scenes and identikits
Type of Work:
- Master
Keywords:
- Bias in image generation models
- Deep Learning Frameworks
- Frontend visualization
- Speech-To-Text, Text-to-Image Models
- Transformers, Diffusion Models, Hugging Face
Description:
Sherlock Holmes is taking the statement of the witness. The witness is describing the appearance of the perpetrator and the forensic setting they still remember. Your task as the AI investigator will be to generate a comic sketch of the scene and phantom images of the accused person based on the spoken statement of the witness. For this you will use state-of-the-art transformers and visualize the output in an application. As AI investigator you will detect, qualify and quantify bias in the images which are produced by different generation models you have chosen.
Note:
This work is embedded in the DFKI KI4Pol lab together with the law enforcement agencies. The stories are fictional you will not work on true crime.
Requirements:
- German level B1/2 or equivalent
- Outstanding academic achievements
- Motivational cover letter
Generative Adversarial Networks for Agricultural Yield Prediction
Type of Work:
- Guided Research
- Master
Keywords:
- Deep Learning
- Generative Adversarial Networks
- Yield Prediction
Description:
Agricultural yield prediction has been an essential research area for many
years, as it helps farmers and policymakers to make informed decisions about
crop management, resource allocation, and food security. Computer vision and
machine learning techniques have shown promising results in predicting crop
yield, but there is still room for improvement in the accuracy and precision
of these predictions. Generative Adversarial Networks (GANs) are a type of
neural network that has shown success in generating realistic images, which
can be leveraged for the prediction of agricultural yields.
References:
- ‘Goodfellow, Ian, et al. “Generative adversarial networks.” Communications of the ACM 63.11 (2020)': 139-144.
- ‘Z. Xu, J. Du, J. Wang, C. Jiang and Y. Ren, “Satellite Image Prediction Relying on GAN and LSTM Neural Networks,” ICC 2019 - 2019 IEEE International Conference on Communications (ICC), Shanghai, China, 2019, pp. 1-6, doi’: 10.1109/ICC.2019.8761462.
- ‘Drees, Lukas, et al. “Temporal prediction and evaluation of brassica growth in the field using conditional generative adversarial networks.” Computers and Electronics in Agriculture 190 (2021)': 106415
Efficient Sampling of Training Patches for Image Super-Resolution
Type of Work:
- Guided Research
- Master
Keywords:
- benchmark
- deep learning
- proxy datasets
- single image super-resolution
Description:
Typically, the Single Image Super-Resolution (SISR) training pipeline decomposes train images into sub-images. It follows the deep learning paradigm of “more data is better.” Be that as it may, it is not necessarily true [1, 2]. This work aims to rethink the image decomposition for the SISR training pipeline and test various sampling methods. The results should be evaluated using state-of-the-art deep learning models on standard datasets.
- [1] Not All Samples Are Created Equal: Deep Learning with Importance Sampling
- [2] Less is More: Proxy Datasets in NAS approaches
Knowledge Graphs für das Immobilienmanagement
Type of Work:
- Bachelor
- Master
Keywords:
- corporate memory
- knowledge graph
- ontologie
Description:
Das Management von Immobilien ist komplex und umfasst verschiedenste Informationsquellen und -objekte zur Durchführung der Prozesse. Ein Corporate Memory kann hier unterstützen in der Analyse und Abbildung des Informationsraums um Wissensdienste zu ermöglichen. Aufgabe ist es, eine Ontologie für das Immobilienmanagement zu entwerfen und beispielhaft ein Szenario zu entwickeln. Für die Materialien und Anwendungspartner sind gute Deutschkenntnisse erforderlich.
Graph Structure and Node Label Prediction on Circuit Diagrams
Type of Work:
- Master
Keywords:
- Autoencoder
- GAN
- Graph Neural Network
- LSTM
Description:
The objective of this master’s thesis is to examine the performance of deep (graph) neural networks for node labeling and structure prediction on graph structures representing electrical circuit diagrams. In the course of the thesis, the following model types should be evaluated: Classification Models should be used to aid the human user to get insights in the circuit’s functional principle. For example, all nodes of a circuit should be classified whether or not they contribute to the power supply of the described device. This part of the thesis is intended to compete (partly) against the existing rule-based approach. Transformative Models should be trained in a natural-language translator-like fashion to map one circuit to another, in which the entire graph structure is mapped to a coding space by an encode an subsequently unwrapped by a decoder model. The output graph should be an alternate version of the input graph which desirable properties. For example, the output graph should represent an energy- optimized version of the input device. Optionally, the process of turning the content of a coding space into a graph structure should be examined here. Biologically inspired approaches should be considered in which a single node divides, and differentiates under the exchange of signals like cells in embryonic tissue (mitosis, apoptosis, release and reception of cytokines). Generative Models (trained e.g. in GAN structures) should allow for the high-dimension interpolation between graphs. This should facilitate the generation of new devices like “a mixture of an AM and an FM radio”, “a slightly more toaster-ish backing oven.”
Fault and Efficiency Prediction in High Performance Computing
Type of Work:
- Master Thesis
Keywords:
- deep learning
- event data modelling
- survival modelling
- time series
Description:
High use of resources are thought to be an indirect cause of failures in large cluster systems, but little work has systematically investigated the role of high resource usage on system failures, largely due to the lack of a comprehensive resource monitoring tool which resolves resource use by job and node. This project studies log data of the DFKI Kaiserslautern high performance cluster to consider the predictability of adverse events (node failure, GPU freeze), energy usage and identify the most relevant data within. The second supervisor for this work is Joachim Folz.
Data is available via Prometheus-compatible system:
Reference:
Feel free to reach out if the topic sounds interesting or if you have ideas related to this work. We can then brainstorm a specific research question together. Link to my personal website.
Construction & Application of Enterprise Knowledge Graphs in the E-Invoicing Domain
Type of Work:
- Bachelor
- Guided Research Project
- Master
Keywords:
- knowledge graphs
- knowledge services
- linked data
- semantic web
Description:
In recent years knowledge graphs received a lot of attention as well in industry as in science. Knowledge graphs consist of entities and relationships between them and allow integrating new knowledge arbitrarily. Famous instances in industry are knowledge graphs by Microsoft, Google, Facebook or IBM. But beyond these ones, knowledge graphs are also adopted in more domain specific scenarios such as in e-Procurement, e-Invoicing and purchase-to-pay processes. The objective in theses and projects is to explore particular aspects of constructing and/or applying knowledge graphs in the domain of purchase-to-pay processes and e-Invoicing.
Graph Extraction/Generation from Diagrams
Type of Work:
- Bachelor
- Master
- Project
- Seminar
Keywords:
- cnn
- cv
- dynamic system simulation
Description:
I am currently supervising students in multiple topics related to the extraction and simulation of Graph-Based Engineering Drawings from optical sources: Graph Extraction from Printed/Handdrawn Circuit Diagrams/Piping&Instumentation Diagrams, Generation of Hand-Drawn Diagrams, Interactive Circuit Detection and Simulation
Learning Analytics in Education
Type of Work:
- Master
- Project
Keywords:
- affective state
- cognitive state
- deep learning
- machine learning
Description:
None
Anomaly detection in time-series
Type of Work:
- Master
- Project
Keywords:
- cnn
- explainability
Description:
Working on deep neural networks for making the time-series anomaly detection process more robust. An important aspect of this process is explainability of the decision taken by a network.
Time Series Forecasting Using transformer Networks
Type of Work:
- Guided Research
- Project
Keywords:
- time series forecasting
- transformer networks
Description:
Transformer networks have emerged as competent architecture for modeling sequences. This research will primarily focus on using transformer networks for forecasting time series (multivariate/ univariate) and may also involve fusing knowledge into the machine learning architecture.