Here you can see a list of all currently proposed projects. For a list of all previous proposals, see the proposal archive
This project would investigate the storage access patterns of LLM agents using open-source state-of-the-art inference frameworks such as vLLM and SGLang and workload traces such as the ones released by Mooncake, Alibaba, Swiss AI Initiative, etc. Depending on the size and duration of the project, it can also propose or implement optimizations for the storage access.
This project would be suitable …
Supervisors:
Pınar Tözün, Jens Birk Andersen
Semester: Fall 2026
Tags: SSDs, LLMs, agents, modern storage
This project investigates how running multiple deep learning tasks simultaneously (collocation) affects performance on resource-constrained edge devices, specifically the NVIDIA DGX Spark. Students will deploy and benchmark various model in isolated vs. different collocated scenarios, measure metrics such as GPU utilization, memory usage, training time, and analyze the trade-offs between …
Supervisors:
Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2026
Tags: machine learning systems, GPU utilization, performance characterization, workload collocation
Traditionally solid-state drives (SSDs) does not give the users the ability to control the data placement on the SSD. This often leads to suboptimal performance and lowers SSD lifetime, since SSDs internally don’t allow in-place updates. The updated disk pages are written elsewhere and the old versions have to be garbage collected. This poses problems if data with different lifetimes and …
Supervisors:
Pınar Tözün
Semester: Fall 2026
Tags: SSDs, data management systems, modern storage
This rise of hardware accelerators to meet the demand of AI workloads has also led to a variety of novel methods to leverage GPUs for traditional data analytics workloads. A key concern for any data-intensive system using GPUs is the efficiency of moving the data to the accelerator. In this project, we will investigate the performance of GPU databases and how they are impacted by the different …
Supervisors:
Pınar Tözün
Semester: Fall 2026
Tags: GPU databases, data analytics, data movement
This project would investigate key-value(KV)-cache offloading in large language model (LLM) inference engines, with particular emphasis on data placement and storage efficiency. First, the project would use an emulator such as FEMU to characterize workload access patterns, including hot-key distributions, write intensity, and write amplification factor (WAF). Then, we would evaluate the potential …
Supervisors:
Pınar Tözün, Jens Birk Andersen
Semester: Fall 2026
Tags: SSDs, LLMs, kv-cache, modern storage
This project would investigate the storage access patterns of LLM agents using open-source state-of-the-art inference frameworks such as vLLM and SGLang and workload traces such as the ones released by Mooncake, Alibaba, Swiss AI Initiative, etc. Depending on the size and duration of the project, it can also propose or implement optimizations for the storage access.
This project would be suitable …
Supervisors:
Pınar Tözün, Jens Birk Andersen
Semester: Fall 2026
Tags: SSDs, LLMs, agents, modern storage
This project would investigate key-value(KV)-cache offloading in large language model (LLM) inference engines, with particular emphasis on data placement and storage efficiency. First, the project would use an emulator such as FEMU to characterize workload access patterns, including hot-key distributions, write intensity, and write amplification factor (WAF). Then, we would evaluate the potential …
Supervisors:
Pınar Tözün, Jens Birk Andersen
Semester: Fall 2026
Tags: SSDs, LLMs, kv-cache, modern storage
This project investigates how running multiple deep learning tasks simultaneously (collocation) affects performance on resource-constrained edge devices, specifically the NVIDIA DGX Spark. Students will deploy and benchmark various model in isolated vs. different collocated scenarios, measure metrics such as GPU utilization, memory usage, training time, and analyze the trade-offs between …
Supervisors:
Pınar Tözün, Ehsan Yousefzadeh-Asl-Miandoab
Semester: Fall 2026
Tags: machine learning systems, GPU utilization, performance characterization, workload collocation
BSc / MSc Thesis.
This project focuses on designing and implementing a metadata management store for Apache Wayang. Wayang lets users express a data processing task in a platform-agnostic way and then decides which underlying platform (e.g., Spark, Flink, Postgres, Java) should execute each part of the plan. Today, however, Wayang has no dedicated component for managing metadata: information such …
Supervisors:
Martin Hentschel, Zoi Kaoudi
Semester: Fall 2026
Tags: database, open source, Apache
BSc Thesis / MSc Thesis.
The goal of this project is to use AI coding tools such as ChatGPT Codex and Claude Code together with optimization tools such as OpenEvolve to find innovative solutions for various database-y workloads, including:
SIEM workloads, such as finding IP addresses in large security data sets String search workloads, such as finding substrings or evaluating regular expressions …
Supervisors:
Martin Hentschel
Semester: Fall 2026
Tags: AI, query optimizer, database, HPC
BSc Thesis.
This project builds on a previous master’s thesis, Prompting Luck: Bias and Manipulability in LLM-Generated Randomness, that explored replacing traditional random number generators (RNG) in games with large language models (LLMs). The idea is that an LLM can produce “intelligent” random numbers, for example the outcome of a slot machine spin, that take context into …
Supervisors:
Martin Hentschel
Semester: Fall 2026
Tags: AI, LLMs, games
BSc / MSc Thesis.
This project focuses on designing and implementing a metadata management store for Apache Wayang. Wayang lets users express a data processing task in a platform-agnostic way and then decides which underlying platform (e.g., Spark, Flink, Postgres, Java) should execute each part of the plan. Today, however, Wayang has no dedicated component for managing metadata: information such …
Supervisors:
Martin Hentschel, Zoi Kaoudi
Semester: Fall 2026
Tags: database, open source, Apache
Query optimization lies at the core of database systems and learning-based query optimization attracts more and more attention because AI techniques are expected to bring new opportunities to further improve the task. As is generally believed, data preparation plays an important role in machine learning tasks, and this may also apply to learning-based query optimization. However, it is noted that …
Supervisors:
Xiao Li, Zoi Kaoudi
Semester: Spring 2026
Tags: data preparation, query optimization, machine learning, database
Query optimization is crucial for any data management system to achieve good performance. Recent advancements in AI have led academia and industry to investigate learning-based techniques in query optimization. In particular, many works propose replacing the cost model used during plan enumeration with a machine learning model (typically a regression model) that estimates the runtime of a query …
Supervisors:
Zoi Kaoudi
Semester: Fall 2025
Tags: machine learning, database, query optimization, ranking
Are you interested in working with a big data open source project?
You are welcome to conduct your thesis/project in Apache Wayang. Apache Wayang is the first cross-platform framework that allows users to specify their task/query in a system-agnostic manner and Wayang will determine which is the best system(s) to execute this task with the goal of optimizing performance. For a general overview …
Supervisors:
Zoi Kaoudi
Semester: Fall 2025
Tags: big data, database, cross-platform data processing, open source, Apache
Knowledge graphs (KGs) are extensively used in many application domains, such as search engines, product recommendation, and bioinformatics. Knowledge graph completion (a.k.a.~link prediction), i.e.,~the task of inferring missing information from knowledge graphs, is a widely used task in the above applications. This project will investigate how to loosely-couple the data-driven power of knowledge …
Supervisors:
Zoi Kaoudi
Semester: Fall 2025
Tags: knowledge graph, LLMs, reasoning
Are you interested in working with a big data open source project and AI?
You are welcome to conduct your thesis/project in the context of Apache Wayang. Apache Wayang is the first cross-platform framework that allows users to specify their task/query in a system-agnostic manner and Wayang will determine which is the best system(s) to execute this task with the goal of optimizing performance. For …
Supervisors:
Zoi Kaoudi
Semester: Fall 2025
Tags: big data, AI, LLMs, cross-platform data processing, open source, Apache
Are you interested in working with a big data open source project and help the environment?
You are welcome to conduct your thesis/project in Apache Wayang. Apache Wayang is the first cross-platform framework that allows users to specify their task/query in a system-agnostic manner and Wayang will determine which is the best system(s) to execute this task with the goal of optimizing performance. …
Supervisors:
Zoi Kaoudi
Semester: Fall 2025
Tags: big data, database, cross-platform data processing, open source, Apache
Query optimization is crucial for any data management system to achieve good performance. Recent advancements in AI have led academia and industry to investigate learning-based techniques in query optimization. In particular, many works propose replacing the cost model used during plan enumeration with a machine learning model that estimates the runtime of a plan. However, to build such a model …
Supervisors:
Zoi Kaoudi
Semester: Fall 2025
Tags: machine learning, training data, query optimizer
Constrastive learning models have made it easier to find relevant content from multimedia collections through descriptive text queries, reducing the interactivity needed to solve simple tasks. However, for complex tasks not only pertaining visual elements, or tasks focused on answering questions involving the contents of a singular or multiple media items (videos/images) still requires a fair …
Supervisor: Omar Shahbaz Khan
Semester: Spring 2026
Tags: Multimedia Retrieval, VLM, RAG, Vector Store, Multimedia Indexing
The extended Cluster Pruning (eCP) index, is a hierarchical approximate nearest neighbor index.
eCP-FS is a “white-box” implementation of the index as a file structure using zarr. The index building is done through Python, while the index loading and search have been implemented in both pure Python and a Rust-Python package.
While the index is slower from disk than other disk-based ANN …
Supervisor: Omar Shahbaz Khan
Semester: Fall 2026
Tags: High-dimensional Indexing, Vector Store, Rust, Python, Approximate Nearest Neighbor
Query optimization lies at the core of database systems and learning-based query optimization attracts more and more attention because AI techniques are expected to bring new opportunities to further improve the task. As is generally believed, data preparation plays an important role in machine learning tasks, and this may also apply to learning-based query optimization. However, it is noted that …
Supervisors:
Xiao Li, Zoi Kaoudi
Semester: Spring 2026
Tags: data preparation, query optimization, machine learning, database
Machine learning methods are often evaluated on benchmark datasets, in computer vision, medical imaging, NLP and other fields. In such evaluation, researchers often describe the data as being:
representative, for example based on the distribution of ages of the patients mirroring the world population, similar, for example because both dataset contain pictures of animals diverse, for example …
Supervisors:
Veronika Cheplygina
Semester: Spring 2025
Tags: machine learning, medical imaging, data analysis, meta-research
LoRa is a long range, low bandwith networking protocol widely used in Internet of Things projects, sensor networks, low power, low cost and embedded systems. LoRa’s encoding schema allows for extremely long distance communications with small power usage and small simple antennas. This combination of features has made it attractive to small satellite operators flying cubesats and LoRa is now …
Supervisors:
Julian Priest
Semester: Fall 2025
Tags: satellites, LoRa, cubesat, IoT, embedded, electronics
The Danish Student Cubesat Program is an inter university collaboration that will launch 3 cubesats into Low Earth Orbit over the next 4 years. The satellites will be designed, operated, programmed and built by students and the project offers an opportunity for Master’s students to take part in a live satellite project. ITU is partnering with Aarhus University on DISCOSAT2 which will be an …
Supervisors:
Julian Priest
Semester: Fall 2021
Tags: Satellite, Cubesat, Image processing, Machine Learning, edge, constrained computing
The idea behind “15-minutes cities” is that within a short walk or bike ride people should have access to all necessary facilities that constitute the essence of urban living, such as parks, shops, cafes, schools, hospitals. Initiatives to transform cities according to this paradigm are currently being implemented across the world, in an attempt to make urban spaces more liveable, …
Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: spatial data analysis, visualisation, Python, OSM data
As a response to increased traffic congestion and the need to reduce carbon emissions, cities consider ways to modernise, build and extend transit systems. Transit network design solutions can benefit from analysing the large amount of crowd-sourced location data available, which provides valuable insights into population mobility needs. Designing efficient metro lines, bicycle paths, or bus …
Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: spatial data analysis, network design, Python, OSM data
The idea behind “15-minutes cities” is that within a short walk or bike ride people should have access to all necessary facilities that constitute the essence of urban living, such as parks, shops, cafes, schools, hospitals. Initiatives to transform cities according to this paradigm are currently being implemented across the world, in an attempt to make urban spaces more liveable, …
Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: spatial data analysis, graph summaries, Python, OSM data
Musical genres are inherently ambiguous and difficult to define. Even more so is the task of establishing how genres relate to one another. Yet, genre is perhaps the most common and effective way of describing musical experience. The number of possible genre classifications (e.g. Spotify has over 4000 genre tags, LastFM over 500,000 tags) has made the idea of manually creating music taxonomies …
Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: scalable algorithms, hyperbolic embeddings, Python, Spotify data