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.
Work group:

Type:

Predicting treatment outcome of antidepressant therapy from EEG recordings [Bachelor or Master Thesis]
Predicting solar thermal heat production [Master Thesis]
Fault Detection for solar thermal plants [Master Thesis]
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]
The source of errors in causal discovery [Master Thesis]
Investigation into the Assumptions of Causal Learning Methods [Practical Course or Master Thesis]
Clustering of timeseries of different scalings [Practical Course or Master Thesis]
Find nonredundant pattern within timeseries [Practical Course or Master Thesis]
Deep representation learning and clustering for timeseries 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 eCommerce Recommendation Systems
Knowledge Discovery From Deep Learning Models
Inverse Reinforcement Learning Under Embodiment Mismatch
Causal Abstractions in Reinforcement Learning
Understanding AI Systems Supporting Sequential DecisionMaking
Learning with Imbalanced Molecular Data
3dShape Descriptors and Molecular Descriptors for Clustering [Practical Course or Master Thesis]
Graph Neural Networks and Molecular Fingerprints
Building selfexplanatory 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 TreeLike 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
Multiagent 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 semisupervised learning
Thresholdfinding for knowledgebase completion using Gaussian processes
Pathbased knowledgebase 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 cosupervised 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 cosupervised by Katerina Schindlerova), winter term 2023/2024
 Rainer Wöss, Bachelor Thesis: "Visualization of spatiotemporal 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 cosupervised 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 cosupervised 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 windturbine data set", winter term 2022/2023
 Daan Scheepens, Master Thesis: "A deep convolutional RNN model for spatiotemporal prediction of wind speed extremes in the shorttomedium 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 kmeans algorithm", summer term 2019
 Ernst Naschenweng, Bachelor Thesis: "A cache optimized implementation of the FloydWarshall Algorithm", summer term 2018
 Hermann Hinterhauser, Bachelor Thesis: "ITGC: Informationtheoretic gridbased clustering", summer term 2018, accepted paper in EDBT 2019 (download available here)
 Mahmoud A. Ibrahim, Bachelor Thesis: "Parameter Free MixedType DensityBased Clustering", winter term 2017/2018, accepted paper in DEXA 2018 (download available here)
 Markus Tschlatscher: "SpaceFilling 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 Densitybased and Partitioningbased Clustering Methods", summer term 2017
 Anton Kovác, Katerina HlaváckováSchindler, Erasmus project, "Graphical Granger Causality for Detection Temporal Anomalies in EEG Data", winter term 2016/2017 (download available here)