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• Data Analytics and Machine Learning Group
• TUM School of Computation, Information and Technology
• Technical University of Munich

Open Topics

We offer multiple Bachelor/Master theses, Guided Research projects and IDPs in the area of data mining/machine learning. A  non-exhaustive list of open topics is listed below.

If you are interested in an internal thesis or a guided research project, please send your CV and transcript of records to Prof. Stephan Günnemann via email ([email protected]) or to the project's reference PhD student. 

If you are interested in an  external thesis with us, please write directly to  Prof. Stephan Günnemann via email ([email protected]).

Generative Models for Drug Discovery

Type:  Mater Thesis / Guided Research

Prerequisites:

• Strong machine learning knowledge
• Proficiency with Python and deep learning frameworks (PyTorch or TensorFlow)
• Knowledge of graph neural networks (e.g. GCN, MPNN)
• No formal education in chemistry, physics or biology needed!

Description:

Effectively designing molecular geometries is essential to advancing pharmaceutical innovations, a domain which has experienced great attention through the success of generative models. These models promise a more efficient exploration of the vast chemical space and generation of novel compounds with specific properties by leveraging their learned representations, potentially leading to the discovery of molecules with unique properties that would otherwise go undiscovered. Our topics lie at the intersection of generative models like diffusion/flow matching models and graph representation learning, e.g., graph neural networks. The focus of our projects can be model development with an emphasis on downstream tasks ( e.g., diffusion guidance at inference time ) and a better understanding of the limitations of existing models.

Contact :  Leon Hetzel

References:

Equivariant Diffusion for Molecule Generation in 3D

Equivariant Flow Matching with Hybrid Probability Transport for 3D Molecule Generation

Structure-based Drug Design with Equivariant Diffusion Models

Efficient Machine Learning: Pruning, Quantization, Distillation, and More

Type: Master's Thesis / Guided Research / Hiwi

• Strong knowledge in machine learning
• Proficiency with Python and deep learning frameworks (TensorFlow or PyTorch)

The efficiency of machine learning algorithms is commonly evaluated by looking at target performance, speed and memory footprint metrics. Reduce the costs associated to these metrics is of primary importance for real-world applications with limited ressources (e.g. embedded systems, real-time predictions). In this project, you will investigate solutions to improve the efficiency of machine leanring models by looking at multiple techniques like pruning, quantization, distillation, and more.

Contact: Bertrand Charpentier

• The Efficiency Misnomer
• A Gradient Flow Framework for Analyzing Network Pruning
• Distilling the Knowledge in a Neural Network
• A Survey of Quantization Methods for Efficient Neural Network Inference

Deep Generative Models

Type:  Master Thesis / Guided Research

• Strong machine learning and probability theory knowledge
• Knowledge of generative models and their basics (e.g., Normalizing Flows, Diffusion Models, VAE)
• Optional: Neural ODEs/SDEs, Optimal Transport, Measure Theory

With recent advances, such as Diffusion Models, Transformers, Normalizing Flows, Flow Matching, etc., the field of generative models has gained significant attention in the machine learning and artificial intelligence research community. However, many problems and questions remain open, and the application to complex data domains such as graphs, time series, point processes, and sets is often non-trivial. We are interested in supervising motivated students to explore and extend the capabilities of state-of-the-art generative models for various data domains.

Contact : Marcel Kollovieh , David Lüdke

• Flow Matching for Generative Modeling
• Auto-Encoding Variational Bayes
• Denoising Diffusion Probabilistic Models 
• Structured Denoising Diffusion Models in Discrete State-Spaces

Active Learning for Multi Agent 3D Object Detection 

Type: Master's Thesis  Industrial partner: BMW 

Prerequisites: 

• Strong knowledge in machine learning 
• Knowledge in Object Detection 
• Excellent programming skills 
• Proficiency with Python and deep learning frameworks (TensorFlow or PyTorch) 

Description: 

In autonomous driving, state-of-the-art deep neural networks are used for perception tasks like for example 3D object detection. To provide promising results, these networks often require a lot of complex annotation data for training. These annotations are often costly and redundant. Active learning is used to select the most informative samples for annotation and cover a dataset with as less annotated data as possible.   

The objective is to explore active learning approaches for 3D object detection using combined uncertainty and diversity based methods.  

Contact: Sebastian Schmidt

References: 

• Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous Driving   
• Efficient Uncertainty Estimation for Semantic Segmentation in Videos   
• KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
• Towards Open World Active Learning for 3D Object Detection   

Graph Neural Networks

Type:  Master's thesis / Bachelor's thesis / guided research

• Knowledge of graph/network theory

Graph neural networks (GNNs) have recently achieved great successes in a wide variety of applications, such as chemistry, reinforcement learning, knowledge graphs, traffic networks, or computer vision. These models leverage graph data by updating node representations based on messages passed between nodes connected by edges, or by transforming node representation using spectral graph properties. These approaches are very effective, but many theoretical aspects of these models remain unclear and there are many possible extensions to improve GNNs and go beyond the nodes' direct neighbors and simple message aggregation.

Contact: Simon Geisler

• Semi-supervised classification with graph convolutional networks
• Relational inductive biases, deep learning, and graph networks
• Diffusion Improves Graph Learning
• Weisfeiler and leman go neural: Higher-order graph neural networks
• Reliable Graph Neural Networks via Robust Aggregation

Physics-aware Graph Neural Networks

Type:  Master's thesis / guided research

• Proficiency with Python and deep learning frameworks (JAX or PyTorch)
• Knowledge of graph neural networks (e.g. GCN, MPNN, SchNet)
• Optional: Knowledge of machine learning on molecules and quantum chemistry

Deep learning models, especially graph neural networks (GNNs), have recently achieved great successes in predicting quantum mechanical properties of molecules. There is a vast amount of applications for these models, such as finding the best method of chemical synthesis or selecting candidates for drugs, construction materials, batteries, or solar cells. However, GNNs have only been proposed in recent years and there remain many open questions about how to best represent and leverage quantum mechanical properties and methods.

Contact: Nicholas Gao

• Directional Message Passing for Molecular Graphs
• Neural message passing for quantum chemistry
• Learning to Simulate Complex Physics with Graph Network
• Ab initio solution of the many-electron Schrödinger equation with deep neural networks
• Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
• Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

Robustness Verification for Deep Classifiers

Type: Master's thesis / Guided research

• Strong machine learning knowledge (at least equivalent to IN2064 plus an advanced course on deep learning)
• Strong background in mathematical optimization (preferably combined with Machine Learning setting)
• Proficiency with python and deep learning frameworks (Pytorch or Tensorflow)
• (Preferred) Knowledge of training techniques to obtain classifiers that are robust against small perturbations in data

Description : Recent work shows that deep classifiers suffer under presence of adversarial examples: misclassified points that are very close to the training samples or even visually indistinguishable from them. This undesired behaviour constraints possibilities of deployment in safety critical scenarios for promising classification methods based on neural nets. Therefore, new training methods should be proposed that promote (or preferably ensure) robust behaviour of the classifier around training samples.

Contact: Aleksei Kuvshinov

References (Background):

• Intriguing properties of neural networks
• Explaining and harnessing adversarial examples
• SoK: Certified Robustness for Deep Neural Networks
• Certified Adversarial Robustness via Randomized Smoothing
• Formal guarantees on the robustness of a classifier against adversarial manipulation
• Towards deep learning models resistant to adversarial attacks
• Provable defenses against adversarial examples via the convex outer adversarial polytope
• Certified defenses against adversarial examples
• Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks

Uncertainty Estimation in Deep Learning

Type: Master's Thesis / Guided Research

• Strong knowledge in probability theory

Safe prediction is a key feature in many intelligent systems. Classically, Machine Learning models compute output predictions regardless of the underlying uncertainty of the encountered situations. In contrast, aleatoric and epistemic uncertainty bring knowledge about undecidable and uncommon situations. The uncertainty view can be a substantial help to detect and explain unsafe predictions, and therefore make ML systems more robust. The goal of this project is to improve the uncertainty estimation in ML models in various types of task.

Contact: Tom Wollschläger ,   Dominik Fuchsgruber ,   Bertrand Charpentier

• Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
• Predictive Uncertainty Estimation via Prior Networks
• Posterior Network: Uncertainty Estimation without OOD samples via Density-based Pseudo-Counts
• Evidential Deep Learning to Quantify Classification Uncertainty
• Weight Uncertainty in Neural Networks

Hierarchies in Deep Learning

Type:  Master's Thesis / Guided Research

Multi-scale structures are ubiquitous in real life datasets. As an example, phylogenetic nomenclature naturally reveals a hierarchical classification of species based on their historical evolutions. Learning multi-scale structures can help to exhibit natural and meaningful organizations in the data and also to obtain compact data representation. The goal of this project is to leverage multi-scale structures to improve speed, performances and understanding of Deep Learning models.

Contact: Marcel Kollovieh , Bertrand Charpentier

• Tree Sampling Divergence: An Information-Theoretic Metricfor Hierarchical Graph Clustering
• Hierarchical Graph Representation Learning with Differentiable Pooling
• Gradient-based Hierarchical Clustering
• Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space

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Student theses

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3d face reconstruction using deep learning.

Student thesis : Master

3D fingerprint detection in ancient museum sculptures from CT data

Achieving long term fairness through curiosity driven reinforcement learning: how intrinsic motivation influences fairness in algorithmic decision making, a coherent temporal visualization of algorithm dynamics over large graphs.

Student thesis : Bachelor

A comparative study for process mining approaches in a real-life environment

A comparative study on unsupervised deep learning methods for x-ray image denoising with multi-image self2self and single frequency denoising, a comparison of quantitative evaluation and human perception of quality of generated images of faces, a computational biology framework: a data analysis tool to support biomedical engineers in their research, active learning for text classification, active learning in vae latent space, activity recognition using deep learning in videos under clinical setting, a dashboard for emulating lstm-based predictive process monitoring and its qualitative evaluation, a data cleaning assistant, a data cleaning assistant for machine learning, adding formal specifications to a legacy code generator, a deep learning approach for clustering a multi-class dataset, a detailed understanding of actor involvement in business processes, adopting the factorized model of execution in a graph database engine, advances in understanding and initializing einsum networks, adversarial attacks on deep dreams, adversarial datasets through sentence length and conjunctions, adversarial nlp benchmarks: data characteristics complicating automated generation of adversarial examples, adversarial noise benchmarking on image caption, aerial imagery pixel-level segmentation, aethra db: optimising analytical processing through query-tailored code generation, a feasibility study on automated database exercise generation with large language models, a forecasting framework for recirculation in baggage handling systems, a framework for understanding business process remaining time predictions, age(ing) in software development, aggregated information visualization for process alignments, a heuristic approach for the vrptw using dual information of its lp formulation, a hybrid model for pedestrian motion prediction, algorithms for center-based trajectory clustering, aligning incompatible state (and action) representations through disentanglement for multi-task transfer in offline rl, allocation decision-making in service supply chain with deep reinforcement learning, a method for identifying undesired medical treatment variants using process and data mining techniques, a method to determine actual time worked from event logs, an adaptive and scrutable math tutoring system, an adversarial analysis of inference capabilities acquired by state-of-the-art nlp models from the rte dataset, analysis and improvement of process models with respect to key performance indicators: a debt collection case study, analysis of the influence of routines on task execution performance, analyzing application usage logs to understand the users, analyzing causes of outlier cascade behavior in baggage handling systems, analyzing collaborations and routines in event graphs using statistics and pattern mining, analyzing complexity progression and complexity correlation of sql questions on stack overflow, analyzing customer journey with process mining: from discovery to recommendations, analyzing data of operating rooms in hospitals to reduce rework, analyzing policy gradient approaches towards rapid policy transfer, analyzing routines and habits in event graphs using statistics and pattern mining.

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:

• Knowledge in machine learning.
• Good R or python skills.

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:

• Planned starting date of your thesis.
• Thesis topic (of the list of thesis topics or your own suggestion).
• Previously attended classes on machine learning and programming with R.

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

Disputation

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.

• How do I prepare for the disputation?

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.

• How many slides should I prepare?

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.

• Where do I present?

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.

• English or German?

We do not care, you can choose.

• What do I have to bring with me?

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.

• How does the grading work?

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.

• What should the presentation cover?

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.

• What kind of questions will be asked after the presentation?

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

Student Research Projects

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)

Completed Theses

Completed theses (lmu munich).

Completed Theses (Supervised by Bernd Bischl at TU Dortmund)

Bachelor and Master Theses

BSc (100), MSc (76), MSc SciComp (15), Diploma (5)

• Alexandra Kowalewski:  Querying Web Tables with Language Models , Bachelor Thesis, July 2024.
• Nils Krehl:  Real-world Clinical Knowledge Graph Construction and Exploration , Master Thesis, July 2024. 
• Christopher Lindenberg:  Exploring the Behavior of Function Calling Systems Using Small LLMs ,  Bachelor Thesis, July 2024.
• Björn Bulkens:  Towards Automatic Generation of Knowledge Graphs using LLMs , Master Thesis, May 2024. 
• Abdulghani Almasri:  A Framework to Measure Coherence in Query Sessions , Bachelor Thesis, May 2024.
• Stefan Lenert: Building Conversational Question Answering Systems , Master Thesis, March 2024. 
• Alexander Kosnac:  Quantity-centric Summarization Techniques for Documents , Bachelor Thesis, March 2024.
• Nicolas Hellthaler: Footnote-Augmented Documents for Passage Retrieval , Bachelor Thesis, February 2024.
• Simon Gimmini: Exploring Temporal Patterns in Art Through Diffusion Models , Master Thesis, February 2024
• Xingqi Cheng:  A Rule-based Post-processor for Temporal Knowledge Graph Extrapolation , Master Thesis, January 2024.
• Raphael Ebner: Leveraging Large Language Models for Information Extraction and Knowledge Representation , Bachelor Thesis, January 2024.
• Angelina Basova: Table Extraction from PDF Documents , Master Thesis, December 2023
• Milena Bruseva:  Benchmarking Vector Databases: A Framework for Evaluating Embedding Based Retrieval , Master Thesis, December 2023.
• Luis Wettach:  Medical Electronic Data Capture at Home – A Privacy Compliant Framework , Master Thesis, December 2023.
• Jayson Pyanowski:  Semantic Search with Contextualized Query Generation , Master Thesis, December 2023.
• Philipp Göldner: Information Retrieval using Sparse Embeddings , Master Thesis, December 2023.
• Vivian Kazakova:  A Topic Modeling Framework for Biomedical Text Analysis , Bachelor Thesis, October 2023
• Dennis Geiselmann: Context-Aware Dense Retrieval , Master Thesis, October 2023.
• Konrad Goldenbaum: Semantic Search and Topic Exploration of Scientific Paper Corpora , Bachelor Thesis, October 2023
• Yingying Cao:  Keyword-based Summarization of (Legal) Documents , Master Thesis Scientific Computing, August, 2023.
• Julian Freyberg: Structural and Logical Document Layout Analysis u sing Graph Neural Networks , Master Thesis, August 2023.
• Marina Walther:  A Universal Online Social Network Conversation Model , Master Thesis, August 2023.
• David Pohl:  Zero-Shot Word Sense Disambiguation using Word Embeddings , Bachelor Thesis, August 2023
• Klemens Gerber:  Automatic Enrichment of Company Information in Knowledge Graphs , Master Thesis, August 2023.
• Bastian Müller:  An Adaptable Question Answering Framework with Source-Citations , Bachelor Thesis, August 2023
• Jiahui Li:  Styled Text Summarization via Domain-specific Paraphrasing ,  Master Thesis Scientific Computing, July 2023.
• Sophia Matthis: Multi-Aspect Exploration of Plenary Protocols , Master Thesis, June 2023.
• Till Rostalski:  A Generic Patient Similarity Framework for Clinical Data Analysis , Bachelor Thesis, June 2023
• David Jackson:  Automated Extraction of Drug Analysis and Discovery Networks , Master Thesis Scientific Computing, May 2023.
• Christopher Brückner:  Multi-Feature Clustering of Search Results , Master Thesis, April 2023.
• Paul Dietze:  Formula Classification and Mathematical Token Embeddings , Bachelor Thesis, April 2023.
• Sophia Hammes:  A Neural-Based Approach for Link Discovery in the Process Management Domain , Master Thesis, March 2023.
• Fabian Kneissl:  Time-Dependent Graph Modeling of Twitter Conversations , Master Thesis, March 2023.
• Lucienne-Sophie Marmé:   A Bootstrap Approach for Classifying Political Tweets into Policy Fields , Bachelor Thesis, March 2023.
• Jing Fan: Assessing Factual Accuracy of Generated Text Using Semantic Role Labeling , Bachelor Thesis, March 2023.
• Fabio Gebhard:  A Rule-based Approach for Numerical Question Answering , Master Thesis, December 2022.
• Severin Laicher:  Learning and Exploring Similarity of Sales Items and its Dependency on Sales Data , Master Thesis, September 2022.
• Raeesa Yousaf: Explainability of Graph Roles Extracted from Networks , Bachelor Thesis, September 2022.
• Julian Seibel: Towards GAN-based Open-World Knowledge Graph Completion , Master Thesis, June 2022.
• Claire Zhao Sun: Extracting and Exploring Causal Factors from Financial Documents , Master Thesis Scientific Computing, May 2022.
• Ziqiu Zhou:  Semantic Extensions of OSM Data Through Mining Tweets in the Domain of Disaster Management , Master Thesis, May 2022.
• Lukas Ballweg:  Analysis of Lobby Networks and their Extraction from Semi-Structured Data ,  Bachelor Thesis, April 2022.
• Benjamin Wagner:  Benchmarking Graph Databases for Knowledge Graph Handling , Bachelor Thesis, March 2022.
• Cedric Bender:  Exploration and Analysis of Methods for German Tweet Stream Summarization , Bachelor Thesis, March 2022. 
• Johannes Klüh:  Polyphonic Music Generation for Multiple Instruments using Music Transformer , Bachelor Thesis, March 2022.
• Nicolas Reuter: Automatic Annotation of Song Lyrics Using Wikipedia Resources , Master Thesis, December 2021.
• Mateusz Chrzastek: Extraktive Keyphrases form Noun Chunk Similarity , Bachelor Thesis, October 2021. 
• Fabrizio Primerano: Document Information Extraction from Visually-rich Documents with Unbalanced Class Structure , Master Thesis, October 2021.
• Sarah Marie Bopp: Gender-centric Analysis of Tweets from German Politicians , Bachelor Thesis, September 2021.
• Philipp Göldner: A Framework for Numerical Information Extraction , Bachelor Thesis, July 2021.
• Robin Khanna: Adaptive Topic Modelling for Twitter Data , Bachelor Thesis, July 2021.
• Thomas Rekers: Correlating Postings from Different Social Media Platforms , Master Thesis, July 2021.
• Duc Anh Phi: Background Linking of News Articles , Master Thesis, May 2021.
• Eike Harms: Linking Table and Text Quantities in Documents , Master Thesis, April 2021.
• Raphael Arndt: Regelbasierte Binärklassifizierung von Webseiten , Bachelor Thesis, April 2021.
• Jonas Gann: Integrating Identity Management Providers based on Online Access Law , Bachelor Thesis, March 2021.
• Björn Ternes: Kontextbasierte Informationsextraktion aus Datenschutzerklärungen , Bachelor Thesis, March 2021.
• Fabio Becker: A Generative Model for Dynamic Networks with Community Structures , Master Thesis, December 2020.
• Jan-Gabriel Mylius: Visual Analysis of Paragraph Similarity , Bachelor Thesis, December 2020
• Alexander Hebel: Information Retrieval mit PostgreSQL , Master Thesis, November 2020.
• Jonas Albrecht: Lexikon-basierte Sentimentanalyse von Tweets , Bachelor Thesis, November 2020.
• Marina Walther: A Network-based Approach to Investigate Medical Time Series Data , Bachelor Thesis, September 2020.
• Stefan Hickl: Automatisierte Generierung von Inhaltsverzeichnissen aus PDF-Dokumenten , Bachelor Thesis, September 2020.
• Christopher Brückner: Structure-centric Near-Duplicate Detection , Bachelor Thesis, August 2020.
• David Jackson: Extracting Knowledge Graphs from Biomedical Literature , Bachelor Thesis, August 2020.
• David Richter: Single-Pass Training von Klassifikatoren basierend auf einem großem Web-Korpus , Master Thesis, August 2020.
• Julian Freyberg: Time-sensitive Multi-label Classification of News Articles , Bachelor Thesis, July 2020.
• John Ziegler: Modelling and Exploration of Property Graphs for Open Source Intelligence , Master Thesis, August, 2020.
• Johannes Keller: A Network-based Approach for Modeling Twitter Topics , Master Thesis, June 2020.
• Erik Koynov : Three Stage Statute Retrieval Algorithm with BERT and Hierachical Pretraining" , Bachelor Thesis, Mai 2020.
• Fabian Kaiser: Cross-Reference Resolution in German and European Law , Master Thesis, April 2020.
• Hasan Malik: Open Numerical Information Extraction , Master Thesis, Scientific Computing, March 2020.
• Matthias Rein: Exploration of User Networks and Content Analysis of the German Political Twittersphere , Master Thesis, March 2020.
• Philip Hausner: Time-centric Content Exploration in Large Document Collections , Master Thesis, March 2020.
• Mohammad Dawas: On the Analysis of Networks Extracted from Relational Databases , Master Thesis, Scientific Computing, February 2020.
• Lea Zimmermann: Mapping Machine Learning Frameworks to End2End Infrastructures , Bachelor Thesis, February 2020
• Bente Nittka: Modelling Verdict Documents for Automated Judgment Grounds Prediction , Bachelor Thesis, November 2019
• Michael Pronkin: A Framework for a Person-Centric Gazetteer Service , Bachelor Thesis, November 2019
• Jessica Löhr: Analysis and Exploration of Register Data of Companies , Bachelor Thesis, October 2019
• Seida Basha: Extraction of Comment Threads of Political News Articles , Bachelor Thesis, September 2019
• Lukas Rüttgers: Analyse von YouTube-Kommentaren zur Förderung von Diskussionen , Master Thesis, Scientific Computing, July 2019
• Gloria Feher: Concepts in Context: A Network-based Approach , Master Thesis, July 2019
• Dennis Aumiller: Implementation of a Relational Document Hypergraph for Information Retrieval , Master Thesis, April 2019
• Raheel Ahsan: Efficient Entity Matching , Master Thesis, Scientific Computing, March 2019
• Christian Straßberger: Time-Varying Graphs to Explore Medical Time Series , Master Thesis, Scientific Computing, March 2019
• Frederik Schwabe: Zitationsnetzwerke in Gesetzestexten und juristischen Entscheidungen , Bachelor Thesis, February 2019
• Kilian Claudius Valenti: Extraktion und Exploration von Kookkurenznetzwerken aus Arztbriefen , Bachelor Thesis, February 2019
• Satya Almasian: Learning Joint Vector Representation of Words and Named Entities , Master Thesis, Scientific Computing, October 2018
• Naghmeh Fazeli: Evolutionary Analysis of News Article Networks , Master Thesis, Scientific Computing, October 2018
• Lukas Kades: Development and Evaluation of an Indoor Simulation Model for Visitor Behaviour on a Trade Fair , Master Thesis, October 2018
• David Stronczek: Named Entity Disambiguation using Implicit Networks , Master Thesis, August 2018
• Julius Franz Foitzik: A Social Network Approach towards Location-based Recommendation , Master Thesis, April 2018
• Carine Dengler: Network-based Modeling and Analysis of Political Debates , Master Thesis, May 2018
• Maximilian Langknecht: Exploration-Based Feature Analysis of Time Series Using Minimum Spanning Trees ,  Bachelor Thesis, May 2018
• Jayson Salazar: Extraction and Analysis of Dynamic Co-occurence Networks from Medical Text , Master Thesis, Scientific Computing, April 2018
• Fabio Becker: Toponym Resolution in HeidelPlace , Bachelor Thesis, April 2018
• Felix Stern: Correlating Finance News Articles and Stock Indexes , Master Thesis, March 2018
• Oliver Hommel: Symbolical Inversion of Formulas in an OLAP Context , Master Thesis, Scientific Computing,  March 2018
• Jan Greulich: Reasoning with Imprecise Temporal and Geographical Data , Master Thesis, February 2018
• Johannes Visintini: Modelling and Analyzing Political Activity Networks , Bachelor Thesis, February 2018
• Sebastian Lackner:  Efficient Algorithms for Anti-community Detection , Master Thesis, February 2018
• Leonard Henger: Erstellung eines konzeptionellen Datenmodells für Zeitreihen und Erkennung von Zeitreihenausreißern , Bachelor Thesis, December 2017
• Christian Kromm: Short-term travel time prediction in complex contents , Master Thesis, December 2017
• Christian Schütz: A Generative Model for Correlated Geospatial Property Graphs with Social Network Characteristics , Bachelor Thesis, December 2017
• Sophia Stahl: Association Rule Based Pattern Mining of Cancer Genome Variants , Master Thesis, December 2017
• Patrick Breithaupt: Evolving Topic-centric Information Networks , Master Thesis, October 2017
• Michael Müller: Graph Based Event Summarization , Master Thesis, September 2017
• Slavin Donchev: Statement Extraction from German Newspaper Articles , Bachelor Thesis, August 2017
• Dennis Aumiller: Mining Relationship Networks from University Websites , Bachelor Thesis, August 2017
• Katja Hauser: Latent Information Networks from German Newspaper Articles , Bachelor Thesis, April 2017
• Xiaoyu Ye: Extraction and Analysis of Organization and Person Networks , Master Thesis, April 2017
• Martin Enderlein: Modeling and Exploring Company Networks , Bachelor Thesis, January 2017
• Ludwig Richter: A Generic Gazetter Data Model and an Extensible Framework for Geoparsing , Master Thesis, October 2016
• Benjamin Keller: Matching Unlabeled Instances against a Known Data Schema Using Active Learning , Bachelor Thesis, August 2016
• Julien Stern: Generation and Analysis of Event Networks from GDELT Data , Bachelor Thesis, July 2016
• Hüseyin Dagaydin: Personalized Filtering of SAP Internal Search Results based on Search Behavior , Master Thesis, March 2016
• Zaher Aldefai: Improvement of SAP Search HANA results through Text Analysis , Master Thesis, April 2016
• Jens Cram: Adapting In-Memory Representations of Property Graphs to Mixed Workloads , Bachelor Thesis, April 2016
• Antonio Jiménez Fernández: Collection and Analysis of User Generated Comments on News Articles , Bachelor Thesis, April 2016
• Nils Weiher: Temporal Affiliation Network Extraction from Wikidata , Bachelor Thesis, March 2016
• Claudia Dünkel: Erweiterung des Wu-Holme Modells für Zitationsnetzwerke , Bachelor Thesis, January 2016
• Muhammad El-Hindi: VisIndex: A Multi-dimensional Tree Index for Histogram Queries , Master Thesis, December 2015
• Annika Boldt: Rahmenwerk für kontextsensitive Hilfe von webbasierten Anwendungen , Master Thesis, December 2015
• Carine Dengler: Das INDY-Bildanalyseframework für die Geschichtswissenschaften , Bachelor Thesis, October 2015
• Leif-Nissen Lundbaek: Conceptional analysis of cryptocurrencies towards smart financial networks , Master Thesis, Scientific Computing, October 2015
• Viktor Bersch: Effiziente Identifikation von Ereignissen zur Auswertung komplexer Angriffsmuster auf IT Infrastrukturen , Master Thesis, September 2015
• Ranjani Dilip Banhatti: Graph Regularization Parameter for Non-Negative Matrix Factorization , Master Thesis, Scientific Computing, September 2015
• Konrad Kühne: Temporal-Topological Analysis of Evolutionary Message Networks , Bachelor Thesis, July 2015
• Stefanie Bachmann: The K-Function and its use for Bandwidth Parameter Estimation , Bachelor Thesis, July 2015
• Philipp Daniel Freiberger: Temporal Evolution of Communities in Affiliation Networks , Bachelor Thesis, June 2015
• Johannes Auer: Bewertung von GitHub Projekten anhand von Eventdaten , Bachelor Thesis, March 2015
• Christian Kromm: Erkennung und Analyse von Regionalen Hashtag Communities in Twitter , Bachelor Thesis, March 2015
• Matthias Brandt: Evolution of Correlation of Hashtags in Twitter, Master Thesis, February 2015
• Jonas Scholten: Effizientes Indexing von Twitter-Daten für temporale und räumliche TopK-Suche unter Verwendung von Mongo DB , Bachelor Thesis, February 2015
• Patrick Breithaupt: Experimentelle Analyse des Exponetial Random Graph Modells , Bachelor Thesis, February 2015
• Timm Schäuble: Classification of Temporal Relations between Events , Bachelor Thesis, January 2015
• Andreas Spitz: Analysis and Exploration of Centrality and Referencing Patterns in Networks of News Articles, Master Thesis , November 2014
• Tobias Zatti: Simulation und Erweiterung von sozialen Netzwerken durch Random Graphs am Beispiel von Twitter , Bachelor Thesis, November 2014
• Ludwig Richter: Automated Field-Boundary Detection by Trajectory Analysis of Agricultural Machinery , Bachelor Thesis, August 2014
• Thomas Metzger: Mining Sequential Patterns from Song Lists , Bachelor Thesis, July 2014
• Arthur Arlt: Determining Rates of False Positives and Negatives in Fast Flux Botnet Detection , Master Thesis, July 2014.
• Hanna Lange: Stream-based Event and Place Detection from Social Media , June 2014
• Christian Karr: Effektive Indexierung von räumlichen und zeitlichen Daten , Bachelor Thesis, May 2014
• Haikuhi Jaghinyan: Evaluation of the HANA Graph Engine, Bachelor Thesis, March 2014
• Sebastian Rode: Speeding Up Graph Traversals in the SAP HANA Database , Diploma Thesis, Mathematics/Computer Science, March 2014
• Isil Özge Pekel: Performing Cluster Analysis on Column Store Databases , Master Thesis, March 2014
• Andreas Runk: Integrating Information about Persons from Linked Open Data , Master Thesis, February 2014
• Tobias Limpert: Verbesserung der spatio-temporal Event Extraktion und ihrer Kontextinformation durch Relationsextraktionsmethoden , Bachelor Thesis, December 2013
• Christian Seyda: Comparison of graph-based and vector-space geographical topic detection , Master Thesis, December 2013
• Bartosz Bogasz: Generation of Place Summaries from Wikipedia , Master Thesis, December 2013
• David Richter: Segmentierung geographischer Regionen aus Social Media mittels Superpixelverfahren , Bachelor Thesis, Oktober 2013
• Marek Walkowiak: Gazetteer-gestützte Erkennung und Disambiguierung von Toponymen in Text , Bachelor Thesis, Oktober 2013
• Mirko Kiefer: Histo: A Protocol for Peer-to-Peer Data Synchronization in Mobile Apps , Bachelor Thesis, September 2013
• Daniel Egenolf: Extraktion und Normalisierung von Personeninformation für die Kombination mit Spatio-temporal Events , Bachelor Thesis, September 2013
• Lisa Tuschner: Tag-Recommendation auf Basis von Flickr Daten , Bachelor Thesis, September 2013
• Edward-Robert Tyercha: An Efficient Access Structure to 3D Mesh Data with Column Store Databases , Master Thesis, September 2013
• Matthias Iacsa: Study of NetPLSA with respect to regularization in multidimensional spaces , Bachelor Thesis, Juli 2013
• Timo Haas: Analyse und Exploration von temporalen Aspekten in OSM-Daten , Bachelor Thesis, June 2013
• Julian Wintermayr: Evaluation of Semantic Web storage solutions focusing on Spatial and Temporal Queries , Bachelor Thesis, June 2013
• Bertil Nestorius Baron: Aggregate Center Queries in Dynamic Road Networks , Diploma Thesis, Mathematics/Computer Science, Mai 2013
• Viktor Bersch: Methoden zur temporalen Analyse und Exploration von Reviews , Bachelor Thesis, Mai 2013
• Cornelius Ratsch: Adaptive String Dictionary Compression in In-Memory Column-Store Database Systems , Master Thesis, April 2013
• Andreas Zerkowitz: Aufbau und Analyse eines Event-Repository aus Wikipedia , Bachelor Thesis, April 2013
• Erik von der Osten: Influential Graph Properties of Collaborative-Filtering based Recommender Systems , Diploma Thesis, Mathematics/Computer Science, March 2013
• Philipp Harth: Local Similarity in Geometric Graphs via Spectral Correspondence , Master Thesis, February 2013
• Benjamin Kirchholtes: A General Solution for the Point Cloud Docking Problem , Master Thesis, February 2013
• Manuel Kaufmann: Modellierung und Analyse heuristischer und linguistischer Methoden zur Eventextraktion , Bachelor Thesis, November 2012
• Dennis Runz: Socio-Spatial Event Detection in Dynamic Interaction Graphs , Master Thesis, November 2012
• Andreas Schuster: Compressed Data Structures for Tuple Identifiers in Column-Oriented Databases , Master Thesis, October 2012
• Christian Kapp: Person Comparison based on Name Normalization and Spatio-temporal Events , Master Thesis, September 2012
• Jörg Hauser: Algorithms for Model Assignment in Multi-Gene Phylogenetics , Master Thesis, August 2012
• Andreas Klein: The CSGridFile for Managing and Querying Point Data in Column Stores , Master Thesis, August 2012
• Andreas Runk: Dynamisches Rerouting in Strassennetzwerken , Bachelor Thesis, August 2012
• Markus Neusinger: Erkennung von Sternströmen mit Hilfe moderner Clusteringverfahren , Diploma Thesis Physics/Computer Science, August 2012
• Clemens Maier: Visualisierung und Modellierung des auf BRF+ aufgebauten Workflows , Bachelor Thesis, August 2012
• Daniel Kruck: Investigation of Exact Graph and Tree Isomorphism Problems , Bachelor Thesis, July 2012
• Andreas Fay: Correlation and Exploration of Events , Master Thesis, February 2012
• Cornelius Ratsch: Extending Context-Aware Query Autocompletion , Bachelor Thesis, February 2012
• Alexander Wilhelm: Spezifikation und Suche komplexer Routen in Strassennetzwerken , Diploma Thesis, Mathematics/Computer Science, February 2012
• Britta Keller: Ein Event-basiertes Ähnlichkeitsmodell für biomedizinische Dokumente , Bachelor Thesis, February 2012
• Simon Jarke: Effiziente Suche von Substrukturen in grossen geometrischen Graphen , Master Thesis, November 2011
• Markus Kurz: Visualizing and Exploring Nonparametric Density Estimations of Context-aware Itemsets , Bachelor Thesis, October 2011
• Frank Tobian: Modelle und Rankingverfahren zur Kombination von textueller und geographischer Suche , Bachelor Thesis, September 2011
• Alexander Hochmuth: Efficient Computation of Hot Spots in Road Networks , Bachelor Thesis, June 2011
• Selina Raschack: Spezifikation von Mustern auf räumlichen Daten und Suche von zugehörigen Musterinstanzen , Bachelor Thesis, Mai 2011
• Bechir Ben Slama: Dynamische Erkennung von Ausreißern in Straßennetzwerken , Master Thesis, March 2011
• Marcus Schaber: Scalable Routing using Spatial Database Systems , Bachelor Thesis, March 2011
• Edward-Robert Tyercha: Co-Location Pattern Mining mit MapReduce , Bachelor Thesis, March 2011
• Benjamin Hiller: Analyse und Verarbeitung von OpenStreetMap-Daten mit MapReduce , Bachelor Thesis, March 2011
• Serge Thiery Akoa Owona: Apache Cassandra as Database System for the Activiti BPM Engine , Bachelor Thesis, February 2011
• Maik Häsner: Bestimmung und Überwachung von Hot Spots in Strassennetzwerken , Master Thesis, October 2010.
• Philipp Harth: Scale-Dependent Pattern Mining on Volunteered Geographic Information , Bachelor Thesis, August 2010.
• Peter Artmann: Design and Implementation of a Rule-based Warning and Messaging System , Bachelor Thesis, June 2010.
• Christopher Röcker: Analyse und Rekonstruktion unvollständiger Sensordaten , Bachelor Thesis, March 2010.
• Andreas Klein: Eine Indexstruktur zur Verwaltung und Anfrage an Moving Regions auf Grundlage des TPR∗-Baumes , Bachelor Thesis, February 2010.
• Benjamin Kirchholtes: Object Recognition and Extraction in Satellites Images using the Insight Segmentation and Registration Toolkit (ITK) , Bachelor Thesis, February 2010.
• Fabian Rühle: Performance Analysis of Column-based Main Memory Databases , Bachelor Thesis, December 2009.
• Pavel Popov: GeoDok: Extraktion und Visualisierung von Ortsinformationen in Dokumenten , Bachelor Thesis, Dezember 2009.

• Navigationsstruktur

Prof. Bouros has a long experience in advising and (co-)supervising undergraduate and graduate student projects.

The Data Management group offers interesting topics for theses (bachelor or master) on query processing, database systems and information systems. Some suggested topics can be found in the following link (last update on June 5, 2024). Interested students can also suggest their own topics of interest.

For more information contact Prof. Bouros via email ( bouros@uni-mainz.de ).

Ongoing (first by type, then in alphabetical order)

• Christian Rauch, Doctorate thesis Temporal Information Retrieval (working title)
• Achilleas Michalopoulos, Doctorate thesis Partitioning and Indexing Techniques for Scalable Spatial Query Evaluation University of Ioannina Primary supervisor: Prof. Nikos Mamoulis
• Dimitrios Tsitsigkos, Doctorate thesis Join Operators for Complex Data University of Ioannina Primary supervisor: Prof. Nikos Mamoulis
• Samia Mubarika Goraya, Master thesis Comparing State-of-the-Art NLP Techniques for Information Extraction from Systems Neuroscience Publications
• Sebastian Hemberger, Bachelor thesis Indexing composite interval data (working title)
• Jakob Heeß, Bachelor thesis Managing and visualizing systems neuroscience data (working title)
• Joshua Kempter, Bachelor thesis A user interface for displaying, exploring and searching through systems neuroscience data
• Dennis Scheck, Bachelor thesis Generating interval data (working title)

Completed (by completion year)

• 2024 - Tobias Laures, Master thesis Academic Social Networks: Evolution of Scientific Communities during the COVID-19 Pandemic
• 2024 - Giorgos Kotsinas, Master thesis Ranking Queries over Range Data University of Ioannina Primary supervisor: Prof. Nikos Mamoulis
• 2024 - Jan Raider, Bachelor thesis A Graphical User Interface for Managing Interval Data
• 2024 - Patrick Götz, Bachelor thesis A graphical user interface for systems neuroscience data
• 2024 - Julius Hoffmann, Bachelor thesis A Graphical Interface for Managing Geosocial networks
• 2024 - Timo Suk, Master thesis Route Inference as a Tool for Generating Training Data for ML-based tasks University of Kostanz Primary supervisor: Dr. Theodoros Chondrogiannis
• 2023 - Yazhou Pan, Bachelor thesis Processing spatial keyword search queries
• 2023 - Lina Khidair, Bachelor thesis An interactive graphical user interface for systems neuroscience graph data
• 2023 - Huu Duy Nguyen, Bachelor thesis Path Diversification for Evacuation Planning
• 2023 - Nina Röckelein, Bachelor thesis Measuring the Attractiveness of Transportation Modes using Relative Reachability and Mobility Patterns University of Kostanz Primary supervisor: Dr. Theodoros Chondrogiannis
• 2023 - Abed Al Rahman Sansour, Bachelor thesis A Graphical User Interface for generating Geosocial networks
• 2023 - George Christodoulou, PhD thesis Interval Data Management in Main Memory Department of Computer Science and Engineering, University of Ioannina, Greece Primary supervisor: Prof. Nikos Mamoulis
• 2023 - Arjanit Arifi, Bachelor thesis Training ETA models with recovered routes
• 2023 - Maximilian Detlef Zerbe, Bachelor thesis Spatio-textual outlier detection
• 2023 - Adrian Kissinger, Bachelor thesis Selecting geospatial data on maps for routing applications
• 2023 - David Betz, Bachelor thesis Extending the EURASIM Interface for Evacuation Planning in Urban Areas
• 2022 - Judith Kunz, Master thesis Finding Similarity Through Dissimilarity: Utilizing path dissimilarity for role extraction in networks University of Kostanz Primary supervisor: Dr. Theodoros Chondrogiannis (for Prof. Dr. Michael Grossniklaus)
• 2022 - Samia Mubarika Goraya, Bachelor thesis Generating complex and composite data
• 2022 - Claudia Berenice Perez Martinez, Bachelor thesis An interactive interface for generating road networks
• 2022 - Lisa-Patricia Barth, Master thesis App-Based Specification and Visualization of User Preferences in Routing Primary supervisor: Prof. Dr. Stefan Kramer
• 2021 - Yannic Marcel Moog, Bachelor thesis Extending k -Shortest Paths with Limited Overlap
• 2021 - Marc Araujo Conde, Bachelor thesis A Database with Amnesia Primary supervisor: Prof. Dr. Felix Schuhknecht
• 2021 - Daniel-Valentin Kowalski, Bachelor thesis Evaluating GeoSocial Reachability Queries
• 2021 - Christian Häcker, Bachelor thesis k -Most Diverse Near-Shortest Paths
• 2020 - Artur Titkov, Bachelor thesis Spatially Combined Text Searches
• 2019 - Mohamed Masarwa, Bachelor thesis Monitoring Geo-Social Influence in Location-Aware Social Networks
• 2019 - Alina Gerhards, Master thesis Equi-joins on GPU

Primary supervisor: Prof. Bertil Schmidt

• 2019 - Matthias Sawatzky, Bachelor thesis A Web Application for Analysis and Comparison of k-Shortest Paths with Limited Overlap Algorithms on Road Networks
• 2017 - Theodoros Chondrogiannis, PhD thesis Efficient Algorithms for Route Planning Problems on Road Networks Faculty of Computer Science, Free University of Bozen-Bolzano, Italy Primary supervisor: Prof. Johann Gamper
• 2016 - Shuyao Qi, PhD thesis Advanced Ranking Queries on Composite Data Department of Computer Science, University of Hong Kong, China PR Primary supervisor: Prof. Nikos Mamoulis
• 2016 - Jens C. B. Madsen and Benjamin Petersen, Graduate student project Continuous Monitoring of Geo-Socially Influential Users

Department of Computer Science, Aarhus University, Denmark

• 2016 - Tobias Sommer, Graduate student project Outlier detection on spatio-temporal data
• 2015 - Florian Hönicke, Diploma thesis Optimizing Set Similarity Joins on MapReduce Department of Computer Science, Humboldt-Universität zu Berlin, Germany Primary supervisor: Prof. Johann-Christoph Freytag
• 2015 - John Liagouris, Doctorate thesis Web Data Management with Applications in Privacy (in greek) School of Electrical and Computer Engineering, National Technical University of Athens, Greece
• 2014 - Anja Kunkel, Undergraduate student project (Studienarbeit) Set Containment Joins using Two Prefix Trees Department of Computer Science, Humboldt-Universität zu Berlin, Germany
• 2012 - Shen Ge, PhD thesis Advanced analysis and join queries in multidimensional spaces Department of Computer Science, University of Hong Kong, China PR Primary supervisor: Prof. Nikos Mamoulis
• 2009 - Dora Kontogianni, Diploma thesis GRAPHIT-DB: Graph Data Management System (II) (in greek) School of Electrical and Computer Engineering, National Technical University of Athens, Greece
• 2009 - Vassilis Giannopoulos, Diploma thesis P-Miner+: Portal Catalogs Administration Supporting Usage Data Mining Processes (in greek) School of Electrical and Computer Engineering, National Technical University of Athens, Greece
• 2008 - John Liagouris and Trifon Farmakakis, Diploma thesis DataBase supported Reasoning System (DBRS) (in greek) School of Electrical and Computer Engineering, National Technical University of Athens, Greece
• 2007 - Lemonia Boula, Diploma thesis GRAPHIT-DB: Graph Data Management System (I) (in greek) School of Electrical and Computer Engineering, National Technical University of Athens, Greece
• 2006 - Theodore Galanis, Diploma thesis P-Miner: Portal Catalogs Administration Supporting Usage Data Mining Processes (in greek) School of Electrical and Computer Engineering, National Technical University of Athens, Greece

File(s) under embargo

until file(s) become available

Multi-Agent-Based Collaborative Machine Learning in Distributed Resource Environments

This dissertation presents decentralized and agent-based solutions for organizing machine learning resources, such as datasets and learning models. It aims to democratize the analysis of these resources through a simple yet flexible query structure, automate common ML tasks such as training, testing, model selection, and hyperparameter tuning, and enable privacy-centric building of ML models over distributed datasets. Based on networked multi-agent systems, the proposed approach represents ML resources as autonomous and self-reliant entities. This representation makes the resources easily movable, scalable, and independent of geographical locations, alleviating the need for centralized control and management units. Additionally, as all machine learning and data mining tasks are conducted near their resources, providers can apply customized rules independently of other parts of the system.

Degree Type

• Doctor of Philosophy
• Computer and Information Technology

Campus location

• West Lafayette

Advisor/Supervisor/Committee Chair

Additional committee member 2, additional committee member 3, additional committee member 4, usage metrics.

• Artificial life and complex adaptive systems
• Autonomous agents and multiagent systems
• Machine learning not elsewhere classified
• Distributed systems and algorithms

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Bachelor's thesis

SirongHuang/Twitter-data-mining

Folders and files.

Repository files navigation

Twitter data mining - social media as a rising customer service channel.

Presentation slides ---- Thesis paper

Collect data from Twitter API using Python scripts that records the customer service interactions on major consumer electronic brands such as Apple and Samsung

Parse Json data objects in Python and organize tweets into customer service sessions with unique customers with Pandas

Use NLTK(Natural Language Processing toolkit) to conduct Text Mining and Sentiment Analysis

Use Naive Bayes classifier and bag of words method to predict sentiment of a customer towards the customer service received

Unfortunately my codes are forever gone with damaged computer, and I was too inexperienced to back it up on github.

UKnowledge > College of Engineering > Mining Engineering > Theses & Dissertations

Theses and Dissertations--Mining Engineering

Theses/dissertations from 2024 2024.

THE METHODOLOGY FOR INTEGRATING ROBOTIC SYSTEMS IN UNDEGROUND MINING MACHINES , Peter Kolapo

DISCRETE ELEMENT MODELING TO PREDICT MUCKPILE PROFILES FROM CAST BLASTING , Russell Lamont

AUTONOMOUS SHUTTLE CAR DOCKING TO A CONTINUOUS MINER USING RGB-DEPTH IMAGERY , Sky Rose

Theses/Dissertations from 2023 2023

ASSESSMENT OF AIR OVERPRESSURE FROM BLASTING USING COMPUTATIONAL FLUID DYNAMICS , Cecilia Estefania Aramayo

RECOVERY OF VALUABLE METALS FROM ELECTRONIC WASTE USING A NOVEL AMMONIA-BASED HYDROMETALLURGICAL PROCESS , Peijia Lin

AN ACID BAKING APPROACH TO ENHANCE RARE EARTH ELEMENT RECOVERY FROM BITUMINOUS COAL SOURCES , Ahmad Nawab

PREDICTION OF DYNAMIC SUBSIDENCE IN THE PROXIMITY OF LONGWALL PANEL BOUNDARIES , JESUS DAVID ROMERO BENITEZ

Prediction of Blast-Induced Ground Vibrations: A Comparison Between Empirical and Artificial-Neural-Network Approaches , Luis F. Velasquez

A LABORATORY AND NUMERICAL INVESTIGATION OF THE STRENGTH OF IRREGULARLY SHAPED PILLARS , Zachary Wedding

Theses/Dissertations from 2022 2022

DEVELOPMENT OF UNIVARIATE AND MULTIVARIATE FORECASTING MODELS FOR METHANE GAS EMISSIONS IN UNDERGROUND COAL MINES , Juan Diaz

PARAMETRIC NUMERICAL ANALYSIS OF INCLINED COAL PILLARS , Robin Flattery

Strain Energy Analysis Related To Strata Failure During Caving Operations , Caroline Gerwig

LAPTOP RECYCLING CASE STUDY: ESTIMATING THE CONTAINED VALUE AND VALUE RECOVERY PROCESS FEASIBILITY OF END-OF-LIFE CONSUMER ELECTRONICS , Zebulon Hart

INVESTIGATION INTO, & ANALYSIS OF TEMPERATURE & STRAIN DATA FOR COAL MINE SEAL MATERIAL DURING CURING , Stephanus Jaco van den Berg

Theses/Dissertations from 2021 2021

DEVELOPMENT OF AN AUTONOMOUS NAVIGATION SYSTEM FOR THE SHUTTLE CAR IN UNDERGROUND ROOM & PILLAR COAL MINES , Vasileios Androulakis

Investigation of Coal Burst Potential Using Numerical Modeling and Rock Burst Indices , Cristian David Cardenas Triana

Capture of Respirable Dust using Maintenance Free Impingement Screen , Neeraj Kumar Gupta

OXIDATION PRETREATMENT FOR ENHANCED LEACHABILITY OF RARE EARTH ELEMENTS FROM BITUMINOUS COAL SOURCES , Tushar Gupta

AN APPROACH FOR PREDICTING FLOW CHARACTERISTICS AT THE CONTINUOUS MINER FACE , Kayla Henderson

CONCEPTS FOR DEVELOPMENT OF SHUTTLE CAR AUTONOMOUS DOCKING WITH CONTINUOUS MINER USING 3-D DEPTH CAMERA , Sibley Miller

MODELING OF RARE EARTH SOLVENT EXTRACTION PROCESS FOR FLOWSHEET DESIGN AND OPTIMIZATION , Vaibhav Kumar Srivastava

Application of a Novel Ventilation Simplification Algorithm , Caitlin V. Strong

A METHODOLOGY FOR AUTONOMOUS ROOF BOLT INSTALLATION USING INDUSTRIAL ROBOTICS , Anastasia Xenaki

Theses/Dissertations from 2020 2020

NUMERICAL APPROXIMATION OF THE GROUND REACTION AND SUPPORT REACTION CURVES FOR UNDERGROUND LIMESTONE MINES , Jesus Castillo Gomez

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Design Thinking in Practice - The case of WienerLinien

The E&I institute is proud to share another submission of a Bachelor thesis in the newly introduced video thesis format.

Our E&I student Emma Litschka has successfully completed her Bachelor thesis on “Design Thinking in Practice: The Case of WienerLinien” under the supervision of our E&I colleague Shtefi Mladenovska.

Emma created an educational video that exemplifies how a group of students in the E&I Project Course “InnoLab” employed Design Thinking to help WienerLinien tackle the challenge of elderly people not using public transport.

The students interviewed 65 elderly people in Vienna and observed their behaviour in public transport to identify their key frustrations. They employed five ideation methods to generate +80 ideas for solving these frustrations, and after ranking them based on technical feasibility and customer value, one of the solutions they chose to work on was a navigation screen for elderly people.

To turn this idea into a reality, the students developed a paper prototype for the navigation screen by simply sketching the layout of a user interface, which they then transformed into a digital mock-up that simulates user interface using the web application Figma.

Finally, to ensure that this solution actually satisfied user needs, they went to gather feedback from the users. Ten seniors tried out the prototype, providing feedback on features they liked and features they deem needed some improvement. The feedback was used to adjust the prototype accordingly and test it again. 

We thank our contact person at WienerLinien, Ms. Lilian Izsak, for providing the students with the opportunity to work on this exciting project.

The student team that worked on this project in the InnoLab project course includes Katharina Anic, Aaron Farrokhnejad Afshar, Richard Wagentristl, and Marina Yazykova. They delivered this project under the supervision of our colleagues Erik Kommol and Carola Wandres. We thank them for the excellent work and for making their detailed documentation available for this thesis.

Do you also want to make an impact with your Bachelor Thesis?

The newly introduced Video Thesis format might be an opportunity!

Develop an educational video for an element (theory, method, tool, framework, etc.) of Core Lecture 1 - and thereby help future E&I students when learning and gain you public visibility as an E&I expert!

Follow this link to learn more about this format.

Is there a CL 1 element that you think deserves such a video?

Contact the CL-1 module experts and apply for supervision.

Follow this link to learn more about the application process and module expert contact details.

Design Thinking in Practice: The…

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CENG Connection

Today’s news, tomorrow’s impact.

• Digital Democracy: Transforming Legislative Transparency and Civic Engagement in California 

• July 30, 2024
• CalMatters , Collaboration , Computer Science , Digital Democracy

A decadelong passion for local journalism and a push for government transparency in California have culminated in the latest version of Digital Democracy, a groundbreaking project led by a dedicated professor.  

Digital Democracy offers journalists and citizens unparalleled access to a comprehensive, searchable database of state-level legislative information, fostering greater civic engagement and healthier public interest decisions in California – the world’s fifth-largest economy with an annual budget of about $300 billion.  

“We are processing 200 hours of hearings a week, with up to 10 happening simultaneously during the Legislature’s busy periods,” said Foaad Khosmood, computer science professor and research director at the Institute for Advanced Technology & Public Policy (IATPP). “It would take an army of 200 reporters to cover that many hearings.”  

Since his days as a technology manager for the Mustang Daily (now Mustang News) at Cal Poly, Khosmood has been a staunch supporter of local journalism. Disturbed by the ongoing cuts to reporters and resources in newsrooms, he has witnessed the rise of “news deserts” – communities with limited access to credible and comprehensive information – across California and the country.  

Digital Democracy has provided Khosmood with an outlet for action and activism. 

“We wanted to empower journalists to write stories that help Californians understand their state government and hold their politicians accountable,” Khosmood said. “With local journalism devastated, Digital Democracy is our way of making a difference.”  

Project Evolution  

Digital Democracy was initially launched in 2015 by former state Sen. Sam Blakeslee and then-Lt. Gov. Gavin Newsom through the IATPP with key contributors including Khosmood and Cal Poly alumna Christine Robertson, the original designer and manager of the platform. 

In March 2024, a revamped Digital Democracy was launched by CalMatters, a nonprofit news organization, broadening its reach and impact.  

The database consolidates hard-to-access public information, capturing every word from public hearings and floor sessions, the full text of bills with amendments, votes, and lists of supporters and opponents. It also includes financial data such as campaign donations, expenditures, gifts and travel, and district data like voter registration, election results and demographics.  

“We use all kinds of AI on this,” said Khosmood, noting that the database and artificial intelligence were designed and built by faculty and students at Cal Poly. 

Additionally, it supports journalism by providing reporters with quick and easy access to extensive legislative information, enabling them to uncover relationships, patterns and anomalies within the Legislature and the policymaking process.  

“We are leveraging the uniqueness of this database, which compiles records that aren’t available in a single location anywhere else,” Khosmood said. “Some of these records were never available before.” 

For example, through Digital Democracy, CalMatters reporters uncovered that legislators often kill bills by simply not voting. This insight was made possible by the database, which provided access to 1 million votes cast by current legislators over the past five years.  

Digital Democracy also assists journalists in identifying stories about the legislative process with a custom AI tool that scans the database and suggests story ideas. These ideas and the resulting stories are then shared with news outlets across the state.  

Innovative Data Solutions  

Cal Poly computer science graduate Thomas Gerrity, who was one of at least 12 graduate students to write their master’s thesis on Digital Democracy, now oversees the technical program for CalMatters.  

“It’s interesting to see legislators take notice, realizing they are being monitored,” he said.  

He highlighted a feature that tracks the number of words each legislator speaks, creating a “Top 10 Most Talkative” list. Legislators have taken note, with some even mentioning their ranking during addresses about bills or issues.  

Gerrity regularly collaborates with Cal Poly students and at least one from outside Cal Poly who is working on the project this summer. Sasha Prostota, a data science student from UCLA studying applied math, is helping tackle one of the major challenges in mining data from legislative hearings: names.  

There is no standardized identification when a person, organization or company testifies in a public hearing. Variations like “ACLU” vs. “American Civil Liberties Union” or subgroups such as “Association for Commuter Transportation” vs. “Association for Commuter Transportation, Southern California Chapter” can complicate data analysis, as these entities are the same.  

“This was a serious problem that came up early, and there was no existing solution,” Khosmood said. “Many organizations have different names, but it’s crucial we accurately characterize these groups to determine their political influence.”  

Working out of IATPP headquarters on campus, Prostota and Gerrity are developing a methodology to identify relationships between groups and their names, with the goal of creating an automated system.  

“Applying what I’ve learned in my classes to something impactful and meaningful is very rewarding,” said Prostota, who advocates for greater transparency between citizens and their government in California.  

For more details and to see Digital Democracy in action, explore the project here .

By Emily Slater

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