Degree Project in Computing Science

Introduction

The goal of the degree project course is to carry out a project that leads to a written thesis that is orally presented and defended at the end of the course. The project idea needs to be approved ahead of time. Therefore, you will need to submit a project proposal. You also need an advisor and an examiner. The advisor guides you through the project, and the examiner sets the grade. Both the advisor and, to a lesser extent, the examiner need to have competence in the project’s topic. We therefore only accept project proposals that fall within the scope of one of the Core Topics listed at the bottom of this page.

Types of Degree Projects

We distinguish between three different types of projects:

• A self-proposed project is based on your own idea.
• An internally proposed project is based on an idea proposed by one of researchers at the Department of Computing Science.
• An externally proposed project is based on an idea proposed by an external partner such as a company, an organization, or another unit within the university.

Process

The process that leads to an assigned project, advisor, and examiner takes place months before the degree project course starts. The following is an outline of the steps involved. The steps are common to all degree project courses, but the timeline is different for each semester and type of degree. The timelines are presented further down.  

1. You apply to a degree project course at Antagning.se.
2. You submit a prioritized list of project proposals to the department.
   1. The proposals are submitted using an online form. A link will be sent to you after you have applied to the course at Antagning.se. 
   2. You will need to fill out a Project Proposal Template (link) for each project proposal.
   3. For externally proposed projects, you will in addition need to fill out an External Partner Agreement (link) together with the external partner.
3. The department gathers project proposals from all students and centrally matches students to projects, advisors, and examiners.
4. You receive the result of the central matching process: A project, an advisor, and an examiner.  

The rest of the process to a finished and defended thesis differs between courses. Consult the course-specific information linked above.

Link to Overview of the Timesteps

Important to choose a subject that is related to the field of Computing Science. The thesis should be an in-depth work on some subject the student are familiar with and also taken some bachelor/advance courses within.

Link to Choosing a Subect

To make a good thesis it should be well defined so both student, supervisor and external actors can get full insight on what should it contain and what not.

Link to Writing a Specification

Core subects with descriptions

Computer Systems

Computer Graphics

Computer Graphics involves the computational creation and manipulation of visual content. It includes techniques for rendering, modeling, and animation, and is widely used in gaming, simulations, and visual effects.

Keywords: rendering, rasterization, ray tracing, shaders, OpenGL, 3D modeling, animation, visualization

Advisors:
Niclas Börlin (Link)
Stefan Johansson (Link)
Zoe Falomir (Link)
Alexandre Bartel (Link)

Computer Networks

Computer Networks study the communication protocols and infrastructure that enable data exchange between devices. It includes routing, switching, and security mechanisms for reliable connectivity.

Keywords: TCP/IP, routing, protocols, LAN, WAN, packet switching, congestion control, network security

Advisors:
Jerry Eriksson (Link)
Oliver Larsson (Link)
Chanh Le Tan Nguyen (Link)
Alexandre Bartel (Link)

Operating Systems

Operating Systems manage hardware and software resources, providing services like process scheduling, memory management, and file systems. They ensure efficient and secure system operation.

Keywords: kernel, processes, threads, memory management, file systems, scheduling, system calls, virtualization

Advisors:
Jerry Eriksson (Link)
Oliver Larsson (Link)
Alexandre Bartel (Link)

Distributed Systems

Distributed Systems consist of multiple interconnected computers working together. They address challenges like fault tolerance, data consistency, and scalability in decentralized environments.

Keywords: concurrency, replication, consistency, fault tolerance, distributed algorithms, consensus, scalability, cloud computing

Advisors:
Feras M. Awaysheh (Link)
Jerry Eriksson (Link)
Monowar Bhuyan (Link)
Paul Townend (Link)
Per-Olov Östberg (Link)
Oliver Larsson (Link)
Chanh Le Tan Nguyen (Link)
Alexandre Bartel (Link)
Erik Elmroth (Link)

Security

Security focuses on protecting systems and data from threats. It includes cryptography, access control, and intrusion detection to ensure confidentiality, integrity, and availability.

Keywords: encryption, authentication, authorization, firewalls, malware, intrusion detection, cybersecurity, access control

Advisors:
Monowar Bhuyan (Link)
Oliver Larsson (Link)
Andreas Brännström (Link)
Chanh Le Tan Nguyen (Link)
Alexandre Bartel (Link)
Vicenç Torra (Link)

Databases

Databases store and manage structured data efficiently. They support querying, indexing, and transactions, and are essential for applications in business, science, and web services.

Keywords: SQL, relational databases, indexing, transactions, normalization, query optimization, NoSQL, data modeling

Advisors:
Timotheus Kampik (Link)
Michael Minock (Link)
Oliver Larsson (Link)
Andreas Brännström (Link)
Chanh Le Tan Nguyen (Link)
Stepán Plachý (Link)
Alexandre Bartel (Link)
Nina Khairova (Link)

Theoretical Computer Science

Formal Languages

Formal Languages define the syntax and structure of programming and computational languages. They are used in compiler design, automata theory, and language processing.

Keywords: regular expressions, grammars, automata, parsing, syntax, language theory, finite state machines, context-free grammars

Advisors:
Anna Jonsson (Link)
Esteban Guerrero Rosero (Link)
Henrik Björklund (Link)
Johanna Björklund (Link)
Martin Berglund (Link)
Suna Bensch (Link)
Adam Dahlgren Lindström (Link)
Andreas Brännström (Link)
Stepán Plachý (Link)
Frank Drewes (Link)
Nina Khairova (Link)

Computational Complexity

Computational Complexity analyzes the resources required to solve problems. It classifies problems based on time and space requirements and explores tractability.

Keywords: P vs NP, Big-O notation, algorithms, tractability, reductions, complexity classes, NP-complete, space complexity

Advisors:
Anna Jonsson (Link)
Henrik Björklund (Link)
Johanna Björklund (Link)
Juan Carlos Nieves Sanchez (Link)
Martin Berglund (Link)
Suna Bensch (Link)
Chanh Le Tan Nguyen (Link)
Stepán Plachý (Link)
Frank Drewes (Link)

Algorithms

Algorithms are step-by-step procedures for solving problems. They are evaluated for efficiency and correctness, and are fundamental to computer science.

Keywords: sorting, searching, graph algorithms, dynamic programming, greedy algorithms, divide and conquer, recursion, optimization

Advisors:
Anna Jonsson (Link)
Henrik Björklund (Link)
Johanna Björklund (Link)
Juan Carlos Nieves Sanchez (Link)
Martin Berglund (Link)
Suna Bensch (Link)
Chanh Le Tan Nguyen (Link)
Stepán Plachý (Link)
Frank Drewes (Link)
Carl C Kjelgaard Mikkelsen (Link)
Jerry Eriksson (Link)
Lars Karlsson (Link)
Niclas Börlin (Link)
Ola Ringdahl (Link)
Stefan Johansson (Link)
Andreas Brännström (Link)
Alexandre Bartel (Link)
Paolo Bientinesi (Link)

Logics

Logics apply formal reasoning to verify and infer properties of systems. They are used in program verification, artificial intelligence, and database theory.

Keywords: propositional logic, predicate logic, model checking, theorem proving, inference, formal verification, temporal logic, logical reasoning

Advisors:
Timotheus Kampik (Link)
Anna Jonsson (Link)
Esteban Guerrero Rosero (Link)
Henrik Björklund (Link)
Johanna Björklund (Link)
Juan Carlos Nieves Sanchez (Link)
Martin Berglund (Link)
Andreas Brännström (Link)
Stepán Plachý (Link)
Frank Dignum (Link)
Frank Drewes (Link)
Patrik Eklund (Link)
Vicenç Torra (Link)
Virginia Dignum (Link)
Julian Mendez (Link)
 

Intelligent Systems

Machine Learning

Machine Learning enables systems to learn from data and improve performance. It includes supervised, unsupervised, and reinforcement learning techniques.

Keywords: supervised learning, neural networks, classification, regression, clustering, reinforcement learning, feature extraction, model evaluation

Advisors:
Timotheus Kampik (Link)
Johanna Björklund (Link)
Juan Carlos Nieves Sanchez (Link)
Lili Jiang (Link)
Loïs Vanhée (Link)
Michael Minock (Link)
Monowar Bhuyan (Link)
Ola Ringdahl (Link)
Polina Kurtser (Link)
Suna Bensch (Link)
Tommy Löfstedt (Link)
Pim Kerkhoven (Link)
Adam Dahlgren Lindström (Link)
Andreas Brännström (Link)
Chanh Le Tan Nguyen (Link)
Gustav Grund Pihlgren (Link)
Maëlic Neau (Link)
Sz-Ting Tzeng (Christine) (Link)
Alexandre Bartel (Link)
Kary Främling (Link)
Patrik Eklund (Link)
Vicenç Torra (Link)
Nina Khairova (Link)
Somayeh Jafaritazehjani (Link)

Natural Language Processing

Natural Language Processing allows computers to understand and generate human language. It involves syntax, semantics, and machine translation.

Keywords: syntax, semantics, transformers, sentiment analysis, language models, tokenization, machine translation, text classification

Advisors:
Timotheus Kampik (Link)
Anna Jonsson (Link)
Henrik Björklund (Link)
Johanna Björklund (Link)
Juan Carlos Nieves Sanchez (Link)
Lili Jiang (Link)
Loïs Vanhée (Link)
Martin Berglund (Link)
Michael Minock (Link)
Polina Kurtser (Link)
Suna Bensch (Link)
Tommy Löfstedt (Link)
Adam Dahlgren Lindström (Link)
Andreas Brännström (Link)
Maëlic Neau (Link)
Stepán Plachý (Link)
Alexandre Bartel (Link)
Frank Dignum (Link)
Frank Drewes (Link)
Vicenç Torra (Link)
Nina Khairova (Link)
Somayeh Jafaritazehjani (Link)

Privacy

Privacy ensures the protection of personal data in computing systems. It includes techniques for anonymization, access control, and compliance with regulations.

Keywords: data anonymization, GDPR, differential privacy, consent, surveillance, access control, data protection, privacy-preserving algorithms

Advisors:
Feras M. Awaysheh (Link)
Lili Jiang (Link)
Monowar Bhuyan (Link)
Tommy Löfstedt (Link)
Pim Kerkhoven (Link)
Andreas Brännström (Link)
Chanh Le Tan Nguyen (Link)
Rachele Carli (Link)
Alexandre Bartel (Link)
Vicenç Torra (Link)

Robotics

Robotics integrates hardware and software to build autonomous machines. It involves sensing, control, and decision-making for interaction with the environment.

Keywords: sensors, actuators, path planning, control systems, autonomy, robot kinematics, navigation, robot perception

Advisors:
Arzu Güneysu (Link)
Juan Carlos Nieves Sanchez (Link)
Kai-Florian Richter (Link)
Ola Ringdahl (Link)
Polina Kurtser (Link)
Zoe Falomir (Link)
Chanh Le Tan Nguyen (Link)
Maëlic Neau (Link)
Thomas Hellström (Link)

Knowledge-Based Systems

Knowledge-Based Systems use structured knowledge to simulate expert decision-making. They include rule-based reasoning and inference mechanisms.

Keywords: expert systems, ontologies, inference engines, rule-based systems, knowledge representation, semantic networks, decision support, reasoning

Advisors:
Timotheus Kampik (Link)
Esteban Guerrero Rosero (Link)
Johanna Björklund (Link)
Juan Carlos Nieves Sanchez (Link)
Kai-Florian Richter (Link)
Lili Jiang (Link)
Michael Minock (Link)
Ola Ringdahl (Link)
Suna Bensch (Link)
Andreas Brännström (Link)
Gustav Grund Pihlgren (Link)
Frank Dignum (Link)
Frank Drewes (Link)
Helena Lindgren (Link)
Kary Främling (Link)
Patrik Eklund (Link)
Vicenç Torra (Link)
Virginia Dignum (Link)
Nina Khairova (Link)
Julian Mendez (Link)

 

Human-Centered Computing

Responsible AI

Responsible AI emphasizes ethical and transparent development of artificial intelligence. It addresses fairness, accountability, and societal impact.

Keywords: bias, fairness, accountability, transparency, ethics, AI governance, trustworthiness, regulation

Advisors:
Timotheus Kampik (Link)
Arzu Güneysu (Link)
Feras M. Awaysheh (Link)
Henrik Björklund (Link)
Juan Carlos Nieves Sanchez (Link)
Kai-Florian Richter (Link)
Loïs Vanhée (Link)
Monowar Bhuyan (Link)
Suna Bensch (Link)
Pim Kerkhoven (Link)
Adam Dahlgren Lindström (Link)
Andreas Brännström (Link)
Gustav Grund Pihlgren (Link)
Rachele Carli (Link)
Sz-Ting Tzeng (Christine) (Link)
Frank Dignum (Link)
Helena Lindgren (Link)
Kary Främling (Link)
Vicenç Torra (Link)
Virginia Dignum (Link)
Nina Khairova (Link)
Julian Mendez (Link)

Social AI

Social AI designs systems that interact socially with humans. It includes emotion recognition, conversational agents, and adaptive behavior.

Keywords: affective computing, social cues, empathy, conversational agents, emotion recognition, human interaction, adaptive behavior, dialog systems

Advisors:
Timotheus Kampik (Link)
Arzu Güneysu (Link)
Feras M. Awaysheh (Link)
Henrik Björklund (Link)
Juan Carlos Nieves Sanchez (Link)
Kai-Florian Richter (Link)
Loïs Vanhée (Link)
Monowar Bhuyan (Link)
Suna Bensch (Link)
Pim Kerkhoven (Link)
Adam Dahlgren Lindström (Link)
Andreas Brännström (Link)
Gustav Grund Pihlgren (Link)
Rachele Carli (Link)
Sz-Ting Tzeng (Christine) (Link)
Frank Dignum (Link)
Helena Lindgren (Link)
Kary Främling (Link)
Vicenç Torra (Link)
Virginia Dignum (Link)
Nina Khairova (Link)
Julian Mendez (Link)

Human-Computer Interaction

Human-Computer Interaction studies how users interact with computers. It aims to improve usability, accessibility, and user experience.

Keywords: UX/UI, usability, interaction design, user studies, accessibility, interface design, cognitive ergonomics, user experience

Advisors:
Arzu Güneysu (Link)
Juan Carlos Nieves Sanchez (Link)
Kai-Florian Richter (Link)
Loïs Vanhée (Link)
Ola Ringdahl (Link)
Polina Kurtser (Link)
Suna Bensch (Link)
Zoe Falomir (Link)
Adam Dahlgren Lindström (Link)
Andreas Brännström (Link)
Chanh Le Tan Nguyen (Link)
Gustav Grund Pihlgren (Link)
Rachele Carli (Link)
Sz-Ting Tzeng (Christine) (Link)
Helena Lindgren (Link)
Kary Främling (Link)
Virginia Dignum (Link)
Nina Khairova (Link)
Somayeh Jafaritazehjani (Link)

Human-Robot Interaction

Human-Robot Interaction explores communication and collaboration between humans and robots. It involves trust, gestures, and shared autonomy.

Keywords: embodiment, trust, gestures, shared autonomy, social robotics, interaction protocols, collaboration, robot behavior

Advisors:
Arzu Güneysu (Link)
Juan Carlos Nieves Sanchez (Link)
Kai-Florian Richter (Link)
Loïs Vanhée (Link)
Ola Ringdahl (Link)
Polina Kurtser (Link)
Suna Bensch (Link)
Zoe Falomir (Link)
Adam Dahlgren Lindström (Link)
Andreas Brännström (Link)
Chanh Le Tan Nguyen (Link)
Maëlic Neau (Link)
Rachele Carli (Link)
Helena Lindgren (Link)
Thomas Hellström (Link)
Somayeh Jafaritazehjani (Link) 

Scientific Computing

Computer Vision

Computer Vision enables machines to interpret visual data. It includes image recognition, object detection, and scene understanding.

Keywords: image recognition, object detection, segmentation, deep learning, cameras, feature extraction, scene understanding, visual tracking

Advisors:
Niclas Börlin (Link)
Ola Ringdahl (Link)
Per-Olov Östberg (Link)
Polina Kurtser (Link)
Tommy Löfstedt (Link)
Zoe Falomir (Link)
Adam Dahlgren Lindström (Link)
Andreas Brännström (Link)
Chanh Le Tan Nguyen (Link)
Gustav Grund Pihlgren (Link)
Maëlic Neau (Link)

Optimization

Optimization finds the best solution under constraints. It is used in operations research, machine learning, and engineering design.

Keywords: linear programming, gradient descent, heuristics, constraints, objective function, convex optimization, metaheuristics, multi-objective optimization

Advisors:
Carl C Kjelgaard Mikkelsen (Link)
Jerry Eriksson (Link)
Niclas Börlin (Link)
Polina Kurtser (Link)
Tommy Löfstedt (Link)
Andreas Brännström (Link)
Chanh Le Tan Nguyen (Link)
Gustav Grund Pihlgren (Link)
Stepán Plachý (Link)
Paolo Bientinesi (Link)
Vicenç Torra (Link)

Matrix Computations

Matrix Computations perform numerical operations on matrices. They are essential in simulations, graphics, and scientific computing.

Keywords: linear algebra, eigenvalues, LU decomposition, sparse matrices, matrix inversion, numerical stability, matrix multiplication, QR decomposition

Advisors:
Carl C Kjelgaard Mikkelsen (Link)
Jerry Eriksson (Link)
Lars Karlsson (Link)
Niclas Börlin (Link)
Stefan Johansson (Link)
Paolo Bientinesi (Link)

High-Performance Computing

High-Performance Computing uses parallel processing and supercomputers to solve complex problems. It supports simulations and data-intensive tasks.

Keywords: parallelism, clusters, GPUs, scalability, simulations, distributed computing, MPI, performance tuning

Advisors:
Carl C Kjelgaard Mikkelsen (Link)
Jerry Eriksson (Link)
Lars Karlsson (Link)
Paolo Bientinesi (Link)