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 23rd of January, 2023 - noon (12h), midday.
Use this KTH web form to submit your teams: https://www.kth.se/form/63c51a034ae888e1db8f4475
Anyone who submites before Monday Jan 23 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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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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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 Computing Systems (English)
Large-scale parallel systems, such as data centers and clusters, employ more and more specialized accelerators, e.g., graphics processing unit (GPU) and data processing units (DPU), and heterogeneous memories, e.g., high-bandwidth memory (HBM) and non-volatile memory (NVM), 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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Human-Robot Interaction (English)
In the social robotics group at the Division of Robotics, Perception and Learning, we develop and evaluate systems that enable robots to be more socially intelligent so that they can collaborate with people in a variety of tasks. Recently, we have been interested in developing Explainable Artificial Intelligence (XAI) techniques to make robots communicate to people in natural language their decisions, failure states or the need to gather more information about a certain task/problem.
For more examples of projects from our group, please check the projects with the keywords “human-robot interaction”, “HRI”, or “explainable AI” in this page: https://www.kth.se/social/group/rpl-thesis-proposals/page/thesis-proposals-2/
Iolanda Leite <iolanda@kth.se>
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Reconfigurable Computing Architectures (Swedish / English)
With the impending termination of Moore's law (transistor scaling), there is a dire need to reconsider how to perform computations in the future. One option is to rely on reconfigurable architectures, which can be used to create specialized hardware accelerators tailored to a particular application. Such hardware accelerators can be both faster and more power-efficient than their CPU or GPU counterpart. We investigate how to efficiently model and build such hardware accelerators for emerging applications (e.g., neuroscience, machine learning, numerical methods) using Field-Programmable Gate Arrays (FPGAs) or Coarse-Grained Reconfigurable Architectures (CGRAs).
Recommended readings:
1) "A Survey on Coarse-Grained Reconfigurable Architectures From a Performance Perspective", Podobas et al. (https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9149601
Links to an external site.)
2) "Combined Spatial and Temporal Blocking for High-Performance Stencil Computation on FPGAs Using OpenCL", Zohouri, Podobas, and Matsuoka (https://dl.acm.org/doi/pdf/10.1145/3174243.3174248
Links to an external site.)
3) "Designing and accelerating spiking neural networks using OpenCL for FPGAs", Podobas et al. (https://ieeexplore.ieee.org/abstract/document/8280154
Links to an external site.)
Artur Podobas <podobas@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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Formal Verification of Software (Swedish / English)
When developing software, programmers typically apply debugging to find programming errors, and apply testing to convince themselves that the program behaves as expected. But to be really sure, one has to express the intended function of a program in some precise notation (a "formal specification") and then to prove that the program adheres to this specification ("formal verification"). In this project we will explore different ways to specify and verify programs, depending on the type of program and on the type of properties which we consider relevant. An important notion will be that of a software contract, which defines the obligations of the program, provided the calling environment uses the program according to some given assumptions.
Dilian Gurov <dilian@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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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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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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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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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 Neurocomputing for Technology and Medical Applications (English)
We develop brain-inspired algorithms and hardware for real-world systems, typically portable sensors, human-machine interfaces, or robots. In the kexjob course projects we want to focus on vision sensors and simulated / 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. 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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