Open topics for theses and practical courses

Unless otherwise specified, all topics are available as practical course (P1/P2), bachelor or master thesis.

 

Work group:
Type:

 

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


Causal Structure Learning for Questionnaires


Attentive pixel prediction (Reinforcement Learning)


Machine learning based climate model weighting


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 Reinforcement Learning under Mismatch


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]


Learning Composable Policies [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


Selecting Sequences of Items for Non-monotone functions [Bachelor or Master Thesis]


Posterior Consistency in Partial Variational Autoencoders


Causal inference among climatological time series with extreme events [Master thesis]


Completed

  • 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)