Open topics for theses and practical courses
Unless otherwise specified, all topics are available as practical course (P1/P2), Data Science projects, bachelor or master thesis.
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Type:
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Automatic tracking of individual cancer organoid in 3D from optical coherence tomography images [Master Thesis]
Semantic Segmentation of cancer organoids for chemotherapy treatment efficacy prediction [Master Thesis]
Text Clustering of Audit Reports from Erste Group [Practical Course or Master Thesis]
Digital Humanities - Computer Vision and Machine Learning in Archaeology [Practical Course or Bachelor Thesis]
Domain Knowledge in Performative Prediction
Developing anonymized datasets for Graph Neural Networks [Bachelor Thesis]
Capabilities of LLMs for causal reasoning [Practical Course or Master Thesis]
Investigation into the Assumptions of Causal Learning Methods [Practical Course or Master Thesis]
Clustering of time-series of different scalings [Practical Course or Master Thesis]
Find non-redundant pattern within time-series [Practical Course or Master Thesis]
Deep representation learning and clustering for time-series data [Practical Course or Master Thesis]
Comparing clustering algorithms for time series data [Practical Course]
Exploratory data analysis of time series data [Practical Course or Bachelor Thesis]
Efficient Knowledge Distillation from Graph Neural Networks for Scalable e-Commerce Recommendation Systems
Knowledge Discovery From Deep Learning Models
Inverse Reinforcement Learning Under Embodiment Mismatch
Causal Abstractions in Reinforcement Learning
Understanding AI Systems Supporting Sequential Decision-Making
Learning with Imbalanced Molecular Data
3d-Shape Descriptors and Molecular Descriptors for Clustering [Practical Course or Master Thesis]
Graph Neural Networks and Molecular Fingerprints
Building self-explanatory transparent models
Attentive pixel prediction (Reinforcement Learning)
The Complexity of Computing the Graph Edit Distance
Reinforcement Learning for improving mental health treatments
Efficient algorithms for uncertain graphs [Master Thesis]
Common Subgraph Problems in Tree-Like Graphs
Dynamic Orbit Partition [Practical Course or Master Thesis]
Interpretable and Explainable Deep Learning
Dynamic Information Acquisition in Questionnaires
Deep Probabilistic Clustering for Heterogeneous and Incomplete Data
Multi-agent Teaching Primitives
Abstraction in Reinforcement Learning
Bivariate causal measures: Experiments with existing causal methods in python on synthetic and real data [Practical Course]
Oracle analysis of distant supervision errors
Weakly supervised discourse relation prediction
Incomplete Schema Relation Clustering
Weakly supervised learning with latent class predictions
Gradient matching for semi-supervised learning
Threshold-finding for knowledge-base completion using Gaussian processes
Path-based knowledge-base completion
Better sentence representations based on BERT
Explainable Policies for Game Play [Master Thesis]
The Cost of Feedback
Reinforcement Learning from Implicit and Explicit Feedback
Machine Learning for Personalized Education [Practical Course or Bachelor Thesis]
Reward Inference for Sequential Decision Making from Diverse and Implicit Feedback [Master Thesis]
Imitation Learning Under Domain Mismatch
Posterior Consistency in Partial Variational Autoencoders
Causal inference among climatological time series with extreme events [Master thesis]
Completed
- Melanija Kraljevska (Data Science), Master Thesis: "Classification of treatment response in depression patients using motif discovery" (supervised by Claudia Plant and co-supervised by Katerina Schindlerova), winter term 2023/2024
- Luis Caumel Morales (Data Science), Master Thesis: "Clustering of Wind Related Time Series in a Wind Turbine Farm" (supervised by Claudia Plant and co-supervised by Katerina Schindlerova), winter term 2023/2024
- Rainer Wöss, Bachelor Thesis: "Visualization of spatio-temporal influences of wind related meteorogical variables in a wind turbine farm in Andau", (supervised by Katerina Schindlerova), summer term 2023
- Alexander Pintsuk, Bachelor Thesis: "Visualization of causal inference for wind turbine extreme events", (supervised by Katerina Schindlerova), winter term 2022/2023
- Christina Pacher (Scientific Computing), Master Thesis: "Analysis of an EEG Database of Depression Patients by means of Graphical Granger Causality" (supervised by Claudia Plant and co-supervised by Katerina Schindlerova), winter term 2022/2023
- Mykola Lazarenko (Business Analytics), Master Thesis: "Clustering brain regions by similar interaction patterns based on multivariate neural signals for identifying the response to antidepressants" (supervised by Claudia Plant and co-supervised by Katerina Schindlerova), winter term 2022/2023
- Wei Chen, Bachelor Thesis: "Mining Brain Networks", winter term 2022/2023
- Kejsi Hoxhallari, Bachelor Thesis: "Statistical validation and visualization of causal inference with extremes in wind-turbine data set", winter term 2022/2023
- Daan Scheepens, Master Thesis: "A deep convolutional RNN model for spatio-temporal prediction of wind speed extremes in the short-to-medium range for wind energy applications", winter term 2021/2022
- Yigit Berkay Bozkurt, Bachelor Thesis: "Anomaly Detection by Heterogenous Graphical Granger Causality and its Application to Climate Data", 2019
- Christina Pacher, Bachelor Thesis: "Clustering Weather Stations: A Clustering Application for Meteorological Data", summer term 2019
- Thomas Spendlhofer, Bachelor Thesis: "Evaluating the usage of Tensor Processing Units (TPUs) for unsupervised learning on the example of the k-means algorithm", summer term 2019
- Ernst Naschenweng, Bachelor Thesis: "A cache optimized implementation of the Floyd-Warshall Algorithm", summer term 2018
- Hermann Hinterhauser, Bachelor Thesis: "ITGC: Information-theoretic grid-based clustering", summer term 2018, accepted paper in EDBT 2019 (download available here)
- Mahmoud A. Ibrahim, Bachelor Thesis: "Parameter Free Mixed-Type Density-Based Clustering", winter term 2017/2018, accepted paper in DEXA 2018 (download available here)
- Markus Tschlatscher: "Space-Filling Curves for Cache Efficient LU Decomposition", winter term 2017/2018
- Theresa Fruhwuerth, Master Thesis: "Uncovering High Resolution Mass Spectrometry Patterns through Audio Fingerprinting and Periodicity Mining Algorithms: An Exploratory Analysis", summer term 2017
- Robert Fritze, PR1 "Combining spatial information and optimization for locating emergency medical service stations: A case study for Lower Austria", summer term 2017
- Alexander Pfundner, PR2 "Integration of Density-based and Partitioning-based Clustering Methods", summer term 2017
- Anton Kovác, Katerina Hlavácková-Schindler, Erasmus project, "Graphical Granger Causality for Detection Temporal Anomalies in EEG Data", winter term 2016/2017 (download available here)