Theses

The chair typically offers various thesis topics each semester in the areas computational statistics, machine learning, data mining, optimization and statistical software. You are welcome to suggest your own topic as well.

Before you apply for a thesis topic make sure that you fit the following profile:

Before you start writing your thesis you must look for a supervisor within the group.

Send an email to the contact person listed in the potential theses topics files with the following information:

Your application will only be processed if it contains all required information.


Potential Thesis Topics

[Potential Thesis Topics] [Student Research Projects] [Current Theses] [Completed Theses]

Below is a list of potential thesis topics. Before you start writing your thesis you must look for a supervisor within the group.

Available thesis topics

Title Type Supervisor
Open topic in interpretable machine learning MA Casalicchio
(Training) Data Initialization for Deep Active Learning with pre-trained language models MA Aßenmacher
Predicting User Smartphone Behavior using Hawkes Processes MA Rügamer
Deep Efficient Transformers for Learning Representation of Genomic Sequences MA Rezaei
Self-supervised Learning Method for Imbalanced Positive Unlabeled Data MA Rezaei
Deep Self-Supervised Divergence Learning for Multi-Modal Representation Learning MA Rezaei
Uncertainty-Aware Self-Supervised Transformer Model for Medical Image Analysis MA Rezaei
Nutrition and time-to-event outcomes in critically ill patients: Multi-state modeling with cumulative effects MA Bender
Laplace Approximation for Uncertainty Quantification in Positive Unlabeled Learning MA Rezaei
Knowledge distillation for proteasomal cleavage prediction in a vaccine design framework MA Rügamer
Learning Set and Irregular Data using Deep Meta-learning MA Rezaei
Deep Reinforcement Learning MA Rezaei
Analyzing the Permutation Feature Importance MA Casalicchio
Wildlife Image Classification - Detection of Outliers MA Bothmann
Uncertainty Estimation in Deep Learning-based Hybrid Localization MA Ott

Disputation

Procedure

The disputation of a thesis lasts about 60-90 minutes and consists of two parts. Only the first part is relevant for the grade and takes 30 minutes (bachelor thesis) and 40 minutes (master thesis). Here, the student is expected to summarize his/her main results of the thesis in a presentation. The supervisor(s) will ask questions regarding the content of the thesis in between. In the second part (after the presentation), the supervisors will give detailed feedback and discuss the thesis with the student. This will take about 30 minutes.

FAQ

You have to prepare a presentation and if there is a bigger time gap between handing in your thesis and the disputation you might want to reread your thesis.

That’s up to you, but you have to respect the time limit. Prepariong more than 20 slides for a Bachelor’s presentation and more than 30 slides for a Master’s is VERY likely a very bad idea.

Bernd’s office, in front of the big TV. At least one PhD will be present, maybe more. If you want to present in front of a larger audience in the seminar room or the old library, please book the room yourself and inform us.

We do not care, you can choose.

A document (Prüfungsprotokoll) which you get from “Prüfungsamt” (Frau Maxa or Frau Höfner) for the disputation.Your laptop or a USB stick with the presentation. You can also email Bernd a PDF.

The student will be graded regarding the quality of the thesis, the presentation and the oral discussion of the work. The grade is mainly determined by the written thesis itself, but the grade can improve or drop depending on the presentation and your answers to defense questions.

The presentation should cover your thesis, including motivation, introduction, description of new methods and results of your research. Please do NOT explain already existing methods in detail here, put more focus on novel work and the results.

The questions will be directly connected to your thesis and related theory.


Student Research Projects

[Potential Thesis Topics] [Student Research Projects] [Current Theses] [Completed Theses]

We are always interested in mentoring interesting student research projects. Please contact us directly with an interesting resarch idea. In the future you will also be able to find research project topics below.

Available projects

Currently we are not offering any student research projects.

For more information please visit the official web page Studentische Forschungsprojekte (Lehre@LMU)


Current Theses (With Working Titles)

[Potential Thesis Topics] [Student Research Projects] [Current Theses] [Completed Theses]

Title Type
Empirical Evaluation of Methods for Discrete Time-to-event Analysis BA
Enhancing stance prediction by utilizing party manifestos MA
Examining and Mitigating Gender Bias in German Word Embeddings BA
Exploring the Effects of Domain Shift on Inferred Topics in Neural and Non-Neural Topic Models BA
Transformer Uncertainty Estimation with Stochastic Attention MA
Transfer Learning of Simulation to Hardware Direction Finding for Indoor Position MA
Reliable Self-supervised Learning for Medical Image Analysis MA
Quantification of Uncertainties via Deep Learning for Medical Image Segmentation MA
Deep Efficient Transformers for Learning Representation of Genomic Sequences MA
Self-Supervised Multimodal Metric Learning MA
Diverse Sentence Embedding for Legal Multi-Label Document Classification MA
Unsupervised Domain Adaptive Object Detection MA
Uncertainty-Aware Self-Supervised Learning MA
Data-driven Lag-lead Selection for Exposure-Lag-Response Associations BA
Probabilistic Deep Learning of Liver Failure in Therapeutical Cancer Treatment MA
Model agnostic Feature Importance by Loss Measures MA
Model-agnostic interpretable machine learning methods for multivariate MA
time series forecasting MA
Normalizing Flows for Interpretablity Measures MA
Representation Learning for Semi-Supervised Genome Sequence Classification MA
Neural Architecture Search for Genomic Sequence Data MA
Comparison of Machine Learning Models For Competing Risks Survival Analysis MA
Multi-accuracy calibration for survival models MA
Interpretation of black box models using tree-based surrogate models MA

Completed Theses

[Potential Thesis Topics] [Student Research Projects] [Current Theses] [Completed Theses]

Completed Theses (LMU Munich)

Title Type Completed
Domain transfer across country, time and modality in multiclass-classification BA 2022
Predicted Sentiments of Customer Texts as Covariates for Time Series Forecasting MA 2022
Gaussian Process Regression and Bayesian Deep Learning for Insurance Tariff Migration MA 2022
Transformer Model for Genome Sequence Analysis BA 2022
Self-supervised Representation Learning for Genome Sequence Data MA 2022
Self-supervised Learning Framework for Imbalanced Positive-Unlabeled Data MA 2022
Deep Self-Supervised Divergence Learning MA 2021
Neural Architecture Search for Genomic Sequence Data MA 2021
Multi-state modeling in the context of predictive maintenance MA 2021
A comparative Evaluation of the Utility of linguistic Features for Part-of-Speech-Tagging BA 2022
Evaluating pre-trained language models on partially unlabeled multilingual economic corpora MA 2022
How Different is Stereotypical Bias in Different Languages? Analysis Multilingual Language Models MA 2022
Leveraging pairwise constraints for topic discovery in weakly annotated text data MA 2022
Word Embedding Evaluation with Intrinsic Evaluators BA 2022
Application of neural topic models to twitter data from German politicians BA 2022
Multi-state modeling in the context of predictive maintenance MA 2021
Model Based Quality Diversity Optimization MA 2021
mlr3automl - Automated Machine Learning in R MA 2021
Knowledge destillation - Compressing arbitrary learners into a neural net MA 2020
Personality Prediction Based on Mobile Gaze and Touch Data MA 2020
Identifying Subgroups induced by Interaction Effects MA 2020
Benchmarking: Tests and Vizualisations MA 2019
Counterfactual Explanations MA 2019
Methodik, Anwendungen und Interpretation moderner Benchmark-Studien am Beispiel der MA 2019
Risikomodellierung bei akuter Cholangitis    
Machine Learning pipeline search with Bayesian Optimization and Reinforcement Learning MA 2019
Visualization and Efficient Replay Memory for Reinforcement Learning BA 2019
Neural Network Embeddings for Categorical Data BA 2019
Localizing phosphorylation sites by deep learning-based fragment ion intensity MA 2019
Average Marginal Effects in Machine Learning MA 2019
Wearable-based Severity Detection in the Context of Parkinson’s Disease Using MA 2018
Deep Learning Techniques    
Bayesian Optimization under Noise for Model Selection in Machine Learning MA 2018
Interpretable Machine Learning - An Application Study using the Munich Rent Index MA 2018
Automatic Gradient Boosting MA 2018
Efficient and Distributed Model-Based Boosting for Large Datasets MA 2018
Linear individual model-agnostic explanations - discussion and empirical analysis of modifications MA 2018
Extending Hyperband with Model-Based Sampling Strategies MA 2018
Reinforcement learning in R MA 2018
Anomaly Detection using Machine Learning Methods MA 2018
RNN Bandmatrix MA 2018
Configuration of deep neural networks using model-based optimization MA 2017
Kernelized anomaly detection MA 2017
Automatic model selection amd hyperparameter optimization MA 2017
mlrMBO / RF distance based infill criteria MA 2017
Kostensensitive Entscheidungsbäume für beobachtungsabhängige Kosten BA 2016
Implementation of 3D Model Visualization for Machine Learning BA 2016
Eine Simulationsstudie zum Sampled Boosting BA 2016
Implementation and Comparison of Stacking Methods for Machine Learning MA 2016
Runtime estimation of ML models BA 2016
Process Mining: Checking Methods for Process Conformance MA 2016
Implementation of Multilabel Algorithms and their Application on Driving Data MA 2016
Stability Selection for Component-Wise Gradient Boosting in Multiple Dimensions MA 2016
Detecting Future Equipment Failures: Predictive Maintenance in Chemical Industrial Plants MA 2016
Fault Detection for Fire Alarm Systems based on Sensor Data MA 2016
Laufzeitanalyse von Klassifikationsverfahren in R BA 2015
Benchmark Analysis for Machine Learning in R BA 2015
Implementierung und Evaluation ergänzender Korrekturmethoden für statistische Lernverfahren BA 2014
bei unbalancierten Klassifikationsproblemen    

Completed Theses (Supervised by Bernd Bischl at TU Dortmund)

Title Type Completed
Anwendung von Multilabel-Klassifikationsverfahren auf Medizingerätestatusreporte zur Generierung von Reparaturvorschlägen MA 2015
Erweiterung der Plattform OpenML um Ereigniszeitanalysen MA 2015
Modellgestützte Algorithmenkonfiguration bei Feature-basierten Instanzen: Ein Ansatz über das Profile-Expected-Improvement Dipl. 2015
Modellbasierte Hyperparameteroptimierung für maschinelle Lernverfahren auf großen Daten MA 2015
Implementierung einer Testsuite für mehrkriterielle Optimierungsprobleme BA 2014
R-Pakete für Datenmanagement und -manipulation großer Datensätze BA 2014
Lokale Kriging-Verfahren zur Modellierung und Optimierung gemischter Parameterräume mit Abhängigkeitsstrukturen BA 2014
Kostensensitive Algorithmenselektion für stetige Black-Box-Optimierungsprobleme basierend auf explorativer Landschaftsanalyse MA 2013
Exploratory Landscape Analysis für mehrkriterielle Optimierungsprobleme MA 2013
Feature-based Algorithm Selection for the Traveling-Salesman-Problem BA 2013
Implementierung und Untersuchung einer parallelen Support Vector Machine in R Dipl. 2013
Sequential Model-Based Optimization by Ensembles: A Reinforcement Learning Based Approach Dipl. 2012
Vorhersage der Verkehrsdichte in Warschau basierend auf dem Traffic Simulation Framework BA 2011
Klassifikation von Blutgefäßen und Neuronen des menschlichen Gehirns anhand von ultramikroskopierten 3D-Bilddaten BA 2011
Uncertainty Sampling zur Auswahl optimaler Sampler aus der trunkierten Normalverteilung BA 2011
Over-/Undersampling für unbalancierte Klassifikationsprobleme im Zwei-Klassen-Fall BA 2010