Projektämnen / Project themes
Below is a collection of project areas that are offered by our pool of supervisors. Ideally, you identify different project areas of your interest. You can then develop a research project together with your supervisor in the area of your interest!
Your supervisor's role is to help you navigate in your research efforts with focus on generic aspects of problem formulation, project scoping, planning etc. Keep in mind that it is you who come up with a concrete project idea, acquire required competences, obtain necessary resources (e.g. data), read up on the state of the art --- in other words, you take care of the subject related content, as your supervisor may well not be an expert in the field of your study.
Please fill in a KTH form questionnair to submit your preferred topic options by Monday the 22nd of January, 2024 - noon (12h, midday).
Use this KTH web form to submit your teams: https://www.kth.se/form/65a631029579d50f87fef12e
Anyone who submites before Monday Jan 22, 2024, noon will have the same chances of getting supervisors of choice.
You need to send
-) your group information: two names and two KTH email addresses
-) multiple topic area suggestions: ideally 3 areas in ranked order
If you plan to work and write in Swedish, please indicate this in your form, so we find a supervisor who speaks Swedish.
Any other questions? Please send email to kexjobb.csc@ncslab.se. Thank you!
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Biologically Detailed Neuron Networks (English)
Large-scale simulations of the brain areas and interconnected neural circuits becomes a common tool in the studies of the brain functions and neurological diseases in neuroscience. In order to set up a simulation, the data pre-processing step is needed involving 3D reconstruction of the morphology of individual neurons, cell placement within the simulated brain volume and deciding the connectivity of the nervous cells based on the proximity of their neurites. Numerical methods, data structures and algorithms for the network setup are needed for serial and parallel processing with great scaling properties.
Alexander Kozlov <akozlov@kth.se>
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Computational Modelling and Analysis of Brain Activity (English)
I work in the field of computational neuroscience. We are interested in the activity dynamics and information processing in biological neuronal networks. In particular, we are studying how properties of neural hardware (neurons and synapses) affect the dynamics of neuronal networks. To this end, we use both numerical simulations (computer models) as well as the analytical tools from Physics. In parallel, we are analysizing neuronal activity recorded form behaving animals and characterizing animal behavior into meaningful states. For this work we rely on signal processing tools and machine learning (especially to classify animal behavior). Our approach to data analysis is model-driven not data-driven.
Arvind Kumar <arvkumar@kth.se>
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Simulation and Visualisation of Crowd Behaviour (English)
Crowds of realistically-behaving synthetic characters have many application domains, from special effects for entertainment, where they replace expensive extras and stunt performers, to evacuation and traffic simulations, where the safety and feasibility of designs can be tested and modified prior to the costly construction of real environments. This project area covers all aspects of 3D characters, including graphical methods for rendering and shading characters, simulation of crowd simulation methods and visualisation methods for crowd data. See the following student project page for some examples of previous projects: https://www.csc.kth.se/~chpeters/ESAL/studentprojects.html
Christopher Peters <chpeters@kth.se>
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Multi-Agent Strategic Planning (Swedish / English)
In this project we will investigate how a team of intelligent agents can coordinate their actions to achieve a common objective. More specifically, we will study the notion of knowledge: what information does each agent need to keep during the mission and how is it to be updated. In particular, we are interested in how the ability of the team to achieve objectives is increased if the agents keep nested knowledge of the type "agent A knows that agent B knows that agent A knows that the door is closed". We will study these questions in the context of a simple class of games, called multi-player games over finite graphs.
Dilian Gurov <dilian@kth.se>
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HPC processor architectures (English)
General-purpose architectures of computing devices like CPUs are becoming increasingly less suitable for high-performance computing. They are not providing sufficient performance and/or consume too much power relative to the delivered compute performance. This development sparked a new interest in research on how to optimise such architectures for different applications. Such research is facilitated by performance models or simulation tools that allow investigating how different types of numerical kernels can benefit from new architectures. Another dimension is the exploration of new instructions like the (meanwhile less new) SIMD instructions for Arm or the RISC-V vector instructions.
Dirk Pleiter <pleiter@kth.se>
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Computational Neuroscience and Machine Learning (Swedish / English)
I supervise projects in computational neuroscience, simulations of how neurons operate in the brain. I also supervise projects in machine learning, methods for classification or regression of static (like images) or temporal (like music) data, or methods to obtaining representations like self-supervised learning, self-organizing feature maps or the like.
Erik Fransén <erikf@kth.se>
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Heterogeneous Parallel Computing (English)
Large-scale parallel systems, such as data centers and clusters, employ more and more specialized accelerators, e.g., RISC-V, smartNIC, graphics processing unit (GPU), data processing units (DPU), and potentially quantum co-processing, and heterogeneous memories, e.g., high-bandwidth memory (HBM), non-volatile memory (NVM), and CXL-attached memory pooling, for enabling compute-intensive and memory-intensive workloads. These heterogeneous parallel systems have enabled unprecedented scale and speedup of applications, e.g., distributed machine learning and scientific simulations on multi-node multi-GPU systems. My research interests revolve around designing and developing novel software and algorithms to improve the scalability of applications on heterogeneous computing systems by leveraging advanced hardware features and programming models.
Ivy Bo Peng <bopeng@kth.se>
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Robot planning algorithms (English)
In our research, we design planning, decision making, and control algorithms for autonomous robots. Our goal is to enable safe, purposeful and trustworthy autonomous behavior. The core of our approach is the use of formal methods-inspired techniques to achieve (probabilistic) guarantees that robots behave as they are expected. In the kexjobb projects, we would like to focus on planning for robots that interact with dynamic environment; with each other, with people, or dynamic objects. Example topics may include integration of short-term predictions in long-term planning, multi-robot coordination with unknown robots’ states, etc.
Jana Tumova <tumova@kth.se>
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DigiMat - lead your own project in the largest online course at KTH! (Swedish / English)
Watch the trailer to get an overview: http://digimat.tech/digimat/
Links to an external site.
DigiMat is a unique educational program, unifying math and programming, from preschool to top academic and professional, developed by leading scientists. Drive and build your own projects and initiatives globally based on interactive simulation activities you develop in web-based editable Digital Math JavaScript or Python.
Johan Jansson <jjan@kth.se>
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Neuronal Networks and Neuromorphic Computing (English)
We develop brain-inspired algorithms and hardware for real-world systems, typically embedded sensors, human-machine interfaces, or robots. In the kexjob course projects we want to focus on vision sensors and simulated or real mobile robots for control tasks. Examples for research projects are algorithm design for fast visual classification and tracking of objects, gesture recognition, and motor control (wheeled or legged robots). We might include machine learning on collected data sets when appropriate.
https://www.buzzwrd.me/index.php/2021/02/24/neuromorphic-computing-a-promising-branch-of-computing/
Links to an external site.
https://www.buzzwrd.me/index.php/2021/03/03/the-current-state-of-neuromorphic-computing/
Links to an external site.
https://arxiv.org/abs/2205.13037
Links to an external site.
https://arxiv.org/abs/1904.08405
Links to an external site.
Jörg Conradt <conr@kth.se>
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Machine Learning in Computer Vision and Biomedical Image Analysis (English)
My research area focuses on computer vision and in particular on applications in biology and medicine. We are interested in how machine intelligence can help precisely diagnose conditions, reduce patient risk, choose effective treatments, and further our understanding of biological systems. In addition, we are also interested in traditional problems in computer vision such as segmentation, detection, classification, generative models, as well as network architectures, learning approaches, etc.
Kevin Smith <ksmith@kth.se>
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Technology Enhanced Learning (English)
With each advance in technology we have an opportunity to apply it to the domain of education. The surprising results from recent advances in AI are one such timely example. Educators are looking at both the positive and negative implications, such as threats to learning and assessment on one hand, and automatically generating, explaining and assessing tasks on the other hand. Projects in this space will investigate positive and negative implications in the field of computer science education.
Reading:
*) Shanahan, M. (2022). Talking About Large Language Models. arXiv preprint arXiv:2212.03551.
*) Finnie-Ansley, J., Denny, P., Becker, B. A., Luxton-Reilly, A., & Prather, J. (2022). The Robots Are Coming: Exploring the Implications of OpenAI Codex on Introductory Programming. In Australasian Computing Education Conference (pp. 10-19).
*) Sarsa, S., Denny, P., Hellas, A., & Leinonen, J. (2022). Automatic Generation of Programming Exercises and Code Explanations Using Large Language Models. In Proceedings of the 2022 ACM Conference on International Computing Education Research-Volume 1 (pp. 27-43).
Ric Glassey <glassey@kth.se>
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Programming Quantum Algorithms (English)
Recently, quantum computing has emerged as a promising approach to substitute traditional silicon-based computing for efficiently solving a particular class of problems or enabling new applications. While still in its infancy, quantum computing paradigms are already assisted by an ecosystem of programming languages, frameworks, and simulators that allow researchers to experiment and design algorithms to exploit this new computing model. Examples of such frameworks include IBM's Qiskit, Google's Cirq, and Rigetti’s PyQuil, which provide Python programming interfaces for existing quantum computers and quantum computer simulators for prototyping and debugging purposes. In addition, higher-level frameworks, like TensorFlow Quantum and Pennylane, provide a set of abstractions for optimization problems and quantum machine learning. Existing applications of quantum computing include random number generators, quantum arithmetics, quantum walks, quantum Fourier transform, Shor’s algorithm for integer factorization, Quantum Approximate Optimization Algorithm (QAOA), quantum simulation of the Schrödinger Equation, and quantum neural networks, to name a few. A project example is the design and development of a quantum algorithm using existing programming interfaces and comparison with classical approaches in terms of accuracy and performance. Other examples are the study of noise impact (using noise models from the simulators) on a given quantum algorithm and the comparison of different quantum computing paradigms, e.g., qubit-based vs. continuous variable vs. quantum annealing approaches, for solving the same problem.
Stefano Markidis <markidis@kth.se>
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