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Degree projects – Department of Computing Science

Below you find a list of degree projects in collaborations with our research groups or external partners.

Collaborate with us

Are you searching for students to your research group, project, company or organization? Please contact Jan-Erik Moström, Director of Studies at the Department of Computing Science.

Degree projects – our research groups

Ericsson Internship/Master Thesis: Automatic segmentation for semantic SLAM dataset 

Motivation
The Efficient understanding of both geometric and semantic characteristics of the environment is needed so that robots or XR devices can appropriately perform requested tasks and understand the local physical environment. Simultaneous localization and mapping (SLAM) algorithms allow the XR device to simultaneously geometrically map the environment and localize itself within the environment, while object detection (OD) models can be used to semantically understand what those features in the environment represent or mean.

For a long time, SLAM and OD have been taken as separate processes or tasks and research has been carried out on separate datasets. There are many datasets with object class and location labels and ground truth in the form of bounding boxes or segmentation masks. While there are many other datasets with maps and devices that pose ground truth. But very few datasets, if any include both, and the ones that do are mostly synthetic datasets.

At Ericsson, we are investigating how to perform joint detection and SLAM in a way that such processes benefit from each other in an efficient and integrated way. For that, we need a joint dataset that allows us to evaluate the performance of SLAM and object detection or segmentation algorithms together. However, semantically segmenting SLAM datasets can be very time-consuming and a fast and efficient acceleration via automatic annotation tools is needed.

Internship Assignment
The proposed project is an iterative process that will evolve and take shape during the internship work. It is expected to have many iterations and trial and error tests in the pipeline. The project will be carried out during the period (Sep-Dec) 2022. We are looking for an intern who can help us accelerate the semantic annotation of existing visual-inertial SLAM datasets in an iterative way, reducing manual annotation efforts. The internship can be converted into a master's thesis if necessary.

The scope of the internship includes but is not limited to:

a) Become acquainted to ML and SLAM frameworks
b) Become acquainted to object detection or segmentation frameworks using RGB images.
c) Carry out a literature survey on prior works related to automatic annotation processes
d) Develop python/OpenCV scripts and/or ROS wrapper/libraries for a proof of concept

Qualifications
We are looking for a self-motivated and creative student with the following qualifications:

  • Master student in the area of Engineering Physics, Electrical Engineering, Computer Science, or similar.
  • Fluency in English
  • Excellent analytical skills
  • Hands-on programming experience
  • Some experience with computer vision and/or Robotics and SLAM
  • Ability to work independently
  • Understanding of networking concepts
  • Understanding of User Experience, Quality of Experience, Experiment Design
  • Understanding of XR, especially Augmented Reality.

Supervisor(s):
The internship coordinator and supervisor is Héctor Caltenco, who will be able to help with administrative matters, purchase of material and general internship-related issues, as well as take care of project matters and research-related questions.

Further Information:
If you have any questions about this position, please contact Nithesh Chandher Karthikeyan, Research Engineer at Department of Computing Science, Umeå University.

Thesis work in Software Engineering or Computer Security (15/30 ECTS)

In the Software Engineering and Security group, we aim to improve the quality of software by identifying and removing weaknesses during its whole life cycle – from the design and implementation phase to the deployment phase. We develop tools and approaches to analyze software, test software, understand the software and protect software from bugs and vulnerabilities. Our research has reald-world impact: Covid tests may leak personal data 

Our research focuses on topics in software engineering and computer security such as:

  • Software testing
  • Program analysis
  • Malware analysis
  • Reverse engineering
  • Vulnerability exploitation and mitigation
  • Data protection
  • Privacy


Do you plan to do your bachelor/master thesis in software engineering or computer security?

Please, contact Prof. Alexandre Bartel: alexandre.bartel@cs.umu.se or 072 208 73 56.

Are you a fast learner and like to work with Robotics/AI (30 ECTS)

Intelligent Robotics Research Group develops formal and non-formal methods for intelligent robot behavior, such as visual perception or natural language usage. We are interested in algorithm development as well as interaction and design studies. Are you a fast learner, like to work in a team, have excellent communication skills, and would like to work in Robotics/AI? 

More information
Please contact Thomas Hällström, Professor at the Department of Computing Science via e-mail for a discussion about a suitable thesis project (30 ECTS).

Privacy Issues with Machine Learning in Medical Imaging (30 ECTS)

Machine learning has become very important in medical sciences as a means to automate for instance diagnosis and prognosis, and to personalise medicine and treatments. Machine learning is for instance used in medical imaging to automate parts of the radiation treatment planning after a cancer diagnosis.

We are curious about what exactly is learned about particular patients when building deep convolutional neural networks for medical imaging applications. Since models trained on patient data are commonly and freely shared online, there are potential privacy issues involved that are currently not fully understood. We are therefore looking for Master's thesis students who are interested in projects in this area.

For more information
If this sounds like something for you, please contact Tommy Löfstedt via e-mail: tommy@cs.umu.se for more information, on this and other potential projects.

Interested in working on Responsible AI design and human-centered AI?

The Responsible Artficial Intelligence Group are notably interested in studying the ethical and societal impact of AI and developing tools and methodologies to mitigate adverse effects; as well as agents that can better connect to humans and societies. We are open to a variety of proposals, from ethical assessments, to using our design methodologies and tools for concrete applications; from hard software for demonstrations to scientific activities.

As a specific example of a pursued track, we developed in house a cognitive component for replicating anxiety and its avoidance; and we wish to make a cool demo of it.

What if you would expand a video game with an anxious character? Or make a cool VR game for which the player can parametrize the stress level he/she will likely face instead of the "difficulty rating" of the game? What about helping us pushing the borders of science, and develop the next version of our anxiety-sensitive AI system? 

For more information
If these are activities you may like, please contact Loïs Vanhée, Associate Professor at The Department of Computing Science via e-mail: lois.vanhee@umu.se

AI-Enhanced Knowledge Harvesting via Heterogeneous Data Analytics & Federation (15ECTS or 30ECTS)

Deep Data Mining Group has been working on multimodal heterogeneous data analytics and data federation by applying the techniques of text mining, information retrieval, natural language processing, machine learning, and differential privacy. The main research topic are data driven and application-oriented such as entity-based social network analysis (e.g., sentiment, emotions, political view etc.), personal privacy analysis, and ontology-based knowledge graph construction etc. Additionally, the group has several connections to industry and academia who are interested to co-supervise master thesis projects, where we can conduct some interesting AI related interdisciplinary research (e.g., Covid pandemic event tracking and inference, medical screening, energy consumption and anomaly prediction, genetics&healthcare etc.). We welcome an open discussion regarding your thesis plan.


For more information
If you are interested, feel free to contact Lili Jiang, associate professor at the Department of Computing Science via e-mail: lili.jiang@cs.umu.se about a suitable master thesis project.

Cloud/edge + AI + hyper-distributed applications (30 ECTS)

Autonomous Distributed Systems Lab: The group's main focus is new cloud/edge technologies with enhanced performance enabled by AI for the data processing required to support future hyper-distributed applications. Based on such the cloud/edge, we research data markets and data economy; therefore, we are currently looking for Master's students who want to do their thesis project (30hp) in our group. More specifically, we have several openings in:

a) Data markets allowing for data assets to be discoverable, efficiently and fairly priced, and shared/traded in a compliant way.

b) Scalable and AI-powered data management for data usability in different contexts, covering data provenance, data quality management, improving data interoperability, and metadata management.

For more information
Please contact Antonio Seo, eunil.seo@cs.umu.se, to discuss/define a thesis project.

Improved understanding of the importance of training for deep neural networks (30 ECTS)

When training deep neural networks in machine learning, the standard optimisation method is some variant of stochastic gradient descent. An hypothesis about why deep neural networks can generalise as well as they do, despite them typically having many more parameters than training data, is the utilisation of stochastic gradient descent. The hypothesis says that when using stochastic gradient descent, wide/flat minima are preferred over sharp/narrow ones, that stochastic gradient descent has the ability to "shake itself out" of sharp minima.

The goal of this project is to investigate different ways that stochastic gradient descent can work as a form of regulariser for the model, how it can avoid overfitting the training data. There are several different ways this can be tested, and the project would amount to select one or a few approaches, implement, and test them by comparing theoretical and practical results.

For more information
If this sounds like something for you, please contact Tommy Löfstedt, associate professor at the Department of Computing Science, through e-mail: tommy@cs.umu.se for more information on this, and other potential projects.

The Matrix Chain Problem with symbolic sizes (30hp)

Given a product of 3 or more matrices (a chain), the optimal sequence of multiplications (of two matrices) can be found in polynomial time. In previous work, we considered "generalized matrix chains", i.e., products in which the matrices can also be transposed and/or inverted.
In this project, we aim to solve the problem when the input size of matrix chain is not fully specified, i.e., one or more matrix dimensions are not known "at compile time".

See also "The Generalized Matrix Chain Algorithm"

For more information
Contact Paolo Bientinesi, pauldj@cs.umu.se, 0720 856 180 for more information.

Dimensionality reduction: Organization of electronic dance music (30 ECTS)

Given a large set of music tracks (genre: electronic dance music), we aim to identify a metric to discern "nearby" vs. "far away" tracks.  The metric can then be used as part of a recommendation system in the context of an automatic mixing software. In practice, every track is a point embedded in an extremely high dimensional space, where the
dimensions correspond to music features -to be extracted via digital signal processing algorithms- such as key, beats per minute, drum pattern, etc. The problem consists in finding viable projections from the space of feature down to a low dimensional space.

The natural starting point for the project is the technique known as "t-distributed stochastic neighbor embedding". A survey paper of the subject.

For more information
Contact Paolo Bientinesi, pauldj@cs.umu.se, 0720 856 180 for more information.

High-performance kernels for tensor computations (30 ECTS)

In a recent survey of software for tensor computations, we identified an enormous case of replication of efforts. Indeed, despite the importance and widespread use of tensors, a library of building blocks (i.e., the tensor counterpart of BLAS) is still missing. With this project we make a concrete proposal for a set of high-performance kernels to be used across disciplines in which computations involve tensors. Such disciplines range from machine learning and data science all the way to computational chemistry and computational mechanics.

The survey mentioned above: "The landscape of software for tensor computations".

For more information
Contact Paolo Bientinesi, pauldj@cs.umu.se, 0720 856 180 for more information.

MatchC: A C/C++ library for Pattern Matching

Mathematica (Wolfram) arguably offers the most powerful pattern matching capability of any programming language. A C/C++ counterpart, even if more limited in features and functionality, would be an extremely useful building block for a myriad of applications. This project aims at developing a high-performance pattern matching library, aiming for associative and/or commutative operators, with constraints, as well as single and multiple matches (akin to ./+/* in reg exps), and shared-memory parallelism.

In previous work, we developed MatchPy, a Python library for pattern matching. This constitutes the natural reference and starting point for this project. See also "Efficient Pattern Matching in Python".

For more information
Contact Paolo Bientinesi, pauldj@cs.umu.se, 0720 856 180 for more information.

Do you want to detect and mitigate emerging cyberattacks or protect users' private data or build large-scale ML systems? (30 ECTS)

Autonomous Distributed Systems Lab - This wing of Lab investigates security and privacy, distributed machine learning (ML), trustworthy machine learning issues to build intelligent, adaptive, reliable, and resilient systems that operate in complex and real-world environments. To help in our research, we are currently looking for Master students who want to do their thesis project (30 ECTS) in our group. More specifically, we have several openings in:

  • How to detect emerging cyberattacks (e.g., distributed denial of service) in complex systems?
  • How to mitigate cyberattacks in large-scale and complex systems?
  • How to maximize the discovery capability of (deep) ML algorithms while maintaining data privacy with a minimum amount of resources?
  • How to ensure data uncertainty together with privacy in federated learning?
  • How to optimize performance for ML clusters via scheduling and sprinting policies?
  • How to learn from small data in both constraint and non-constraint environments?
  • How to protect users' private data for learning at the edge?
  • How to build reliable, trustworthy and resilient large-scale ML systems in the presence of adversarial attacks? 
  • How to detect anomalies in multimodal data under non-standard model settings? 


For more information 
Does this seem any problems exciting? Please contact Monowar Bhuyan, Assistant Professor, via e-mail: monowar@cs.umu.se for a discussion about a suitable Master thesis project.

Degree projects – external partners

Image processing and deep learning on tomography data of phosphorus-rich ash

The transition to a sustainable society requires efficient use of materials and energy, where recycling nutrients is an important part. Phosphorus is an important nutrient that today is only recycled in small amounts. This could be improved by resource recovery from residual streams such as ash or biochar formed during the thermal conversion of societal waste streams. The overall aim of our research is to improve knowledge of the ash's chemical and physical properties such as microstructure, porosity, and surface area and to study how these can affect the availability of nutrients that can be used as fertiliser.

The project therefore aims to develop tools for analysing tomography data with so-called deep learning. Computed tomography provides a 3D image of a sample and with the help of advanced image processing, detailed characteristics of the sample can be obtained. For this, deep machine learning can play an important role in streamlining and improving performance in data evaluation. The goal of the project is to develop interactive segmentation methods, and evaluate their performance, by comparing to manual segmentation and applicability to different samples.

For more information
If this sounds interesting, contact Anna Strangberg, from the  Department of Applied Physics and Electronics, through e-mail: anna.strandberg@umu.se for more information.

"Hey Computer! Please read this text and tell me the keywords!"

Adlede works with contextual advertisement for news articles and we use machine learning to analyze thousands of news articles every day. We want to explore methods for finding keywords in an article. E.g. in the sentence "The levels of carbon dioxide in the atmosphere are rising" the words "levels", "atmosphere", and the 2-gram "carbon dioxide" are more descriptive/unique for the sentence than "the", "of", and "are". A common way to calculate the importance of a word is through the technique TF-IDF. We want to explore smarter techniques, e.g. based on BERT embeddings.

The end goal is to create an intelligent keyword extractor for news articles. The student will get to experiment with NLP-related libraries in Python, work with lemmatizing and other pre-processing techniques and, if time permits, be part of integrating the work into our production system.

For more information

Please contact Anton Eklund, anton@adlede.com, for more information

Use of machine learning methods to identify patterns of seedling response to drought treatment with help of time-lapse images

The goal of the proposed project is to apply ML tools to learn predictive patterns of response to drought in Scots pine seedlings exposed to drought conditions with use of time-lapse images. Machine learning (ML) methods such as deep learning, elastic net and lasso have been tested to train models for organisms and object recognition. The focus within this project is to train ML models to identify patterns of Scots pine seedling change in responses to drought. Model training will be done on images obtained by time-lapse imaging during the process of drought treatment.

For more information
If this sounds interesting, contact Rosario Garcia Gil, from the Department of Plant Physiology, through e-mail: m.rosario.garcia@slu.se for more information.

Training machine learning methods to interpret genetic effects on wood properties

Development of cost-efficient DNA sequencing technology offers the possibility to generate genome data for thousands of samples, which opens the possibility to apply machine learning methods to predict relationships between an organism’s genotype and phenotype. A recent study on yeast, rice, and weat showed that almost all standard machine learning methods performed better than methods from classical statistical genomics. The goal of the proposed project is to develop an efficient machine learning method to learn predictive relationships between the exome’s genome data (genetic DNA information) and spruce growth and wood properties.
 
For more information
If this sounds interesting, contact Rosario Garcia Gil, from the Department of Plant Physiology, through e-mail: m.rosario.garcia@slu.se for more information.

 

Do you want to work interdisciplinary with medical technology at Region Västerbotten? (15or 30 ECTS)

Our mission is to conduct research and development in close collaboration with medical researchers. Our expertise in biomedical engineering provides hospital personnel with technical support in different research and development projects. Thesis has always been an important part of the activities of the dept. This uses the student’s theoretical knowledge in practice in a hospital setting and at the same time the student gets to know a future employer. The student helps us with tasks and to find new technical solutions to the challenges of our research.

Interested?
Please contact Helena Grip, Hospital engineer, assoc. prof. in biomedical engineering and adjunct university lecturer. Research and development within medical technology and movement analysis, email: helena.grip@regionvasterbotten.se or telephone: 090-785 40 29

Develop new features for international Evity (15 or 30 ECTS)

Evity is a startup in Umeå that develops an HR tool for small and medium-sized companies. We use automation and form tools to create KPIs and processes in the tool. Now, we are looking for a student who wants to work in .net within Azure where we can find a collaboration to develop new features or improve existing ones. Size of project 15 or 30 ECTS.

We are an international team as our co-founder and CTO is from New Zealand, where he also works from. You can read more about us on www.evity.hr

Interested?
Please contact CEO, Tommy Eriksson, telephone: 070-696 70 22 or email: tommy@evity.hr

 

Interested in learning about the state-of-the-art deep learning models in medical image analysis? (30 ECTS)

NONPI Medical develops and implements software tools for medical image analysis. The proposed project focuses on deep learning methods for medical imaging, e.g. tumor segmentation, super-resolution, or distortion correction.

There are many different frameworks available for implementing deep learning models, which is good for development and research, but also divides the field and published code into fractions, depending on the particular framework that was used. The result is that the field is divided, and the results are difficult to reproduce between framework users. An open source standard for machine learning models has been established (ONNX), however this format is yet to be commonly deployed, and especially so in medical imaging research and development.

To help spread developed models, and the ONNX format, we are currently looking for:

(a) master's thesis student(s) (30 p) who want to establish an open source collection of current state-of-the-art deep learning models and methods for medical imaging, mainly challenge winners, state-of-the-art models, and models from well-cited research papers. The project involves collecting and categorising pre-trained models, converting them from a diverse set of formats to the ONNX format, and testing them on medical image data in MICE Toolkit, a software for medical imaging developed by NONPI Medical AB.

For more information

If you are interested in learning about the state-of-the-art deep learning models and unifying the research community, please contact attila@nonpimedical.com 

 

Deep learning models for medical image processing

NONPI Medical AB develops and implements software tools for medical image analysis. The proposed project focuses on developing tools to design, alter, and train deep learning models and full machine learning pipelines for medical image processing, such as tumour segmentation, super resolution, or distortion correction.

There is much interest within for instance radiation therapy to use deep learning models for automation, but many in such fields lack the background to make substantial changes in existing models, and to develop their own specific models programmatically. With this background, NONPI Medical AB has developed a graphical tool to define the architecture for deep learning models. The existing tool is built on top of Python, which adds an extra layer of complexity since the rest of the program is written in C#, and also has problems whenever the underlying deep learning framework is updated.

The goal of this project it to evaluate different approaches to build deep learning models modularly in C#, and to find efficient graph algorithms to execute different steps of the machine learning pipeline on available hardware resources.

The goal of these developments is to aid the adaptation of existing deep learning models, and to make it possible for more researchers and medical practitioners to develop their own and be able to change or improve existing models and the entire maching learning pipeline. NONPI Medical AB is therefore looking for (a) master's thesis student(s) (30 hp) that wants to develop such tools for use within medical image analysis.

For more information
If you are interested in learning more about the project or how we use state-of-the-art deep learning models, please contact: attila@nonpimedical.com

AI to understand large scale biological data

MIMS has with the help of Johan Henrikssons research group established a new lab to produce single-cell data. With this method it is possible measure thousands (even millions) of cells, one at a time. The enormous datasets enable us to take a new approach to understanding biology, where we use AI to break down the complex measurements into knowledge.

This comes with computational challenges, from "how can we use AI to find a compressed data representation?" to "how do we efficiently handle several terabytes of sparse data?". Like the particle accelerator at CERN, biology now requires us to solve advanced computer scientific problems before we can go back to the biological questions.

We welcome thesis workers on all levels, from those who want fairly isolated problems to those who dare holistic approaches. It is a good thing if you want to learn some biology. Python is currently our standard language but we also use R, Java and C/C++ when the situation calls for it.

For more information

Please contact Johan Henriksson, researcher at MIMS. See also our website at www.henlab.org