Projektämnen / Project themes
Please fill in a KTH form questionnair to submit your preferred topic options by Friday the 28th of January, 2022.
Use this KTH web form to submit your teams: https://www.kth.se/form/61ea9d6cfb205dc4c1470392
Remember 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 write in Swedish, please indicate this in your form, so we find a supervisor who speaks Swedish.
The assignment of students to supervisors is published here: Projektindelning!
Any other questions? Please send email to kexjobb.csc@ncslab.se. Thank you!
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Simulation and Visualisation of Crowd Behaviour
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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Cyber Security: Attack Simulations and Ethical Hacking
In order to deal with the complexity of todays IT environments we develop threat modeling and attack simulation methods. The goal is to create a system model that we can run attack simulations on to find weaknesses and provide mitigation suggestions.
Almost everything is connected to the internet today which gives hackers an opportunity never seen before. We do ethical hacking of different IoT devices, both consumer IoT e.g. drones, locks, and vacuum cleaners, as well as industrial IoT such as PLCs and ECUs. The goal is to find vulnerabilities that can be exploited.
Robert Lagerström <robertl@kth.se>
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Multi-Agent Strategic Planning
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
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
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
With each advance in technology we have an opportunity to apply it to the domain of education. Increasingly the process of learning generates numerous trails of data that can be used to further enhance education. However, the scale and diversity of these trails requires new sets of skills and systems we currently do not have easy access to. Some examples include the data trails from online learning system interaction, using version control systems in assessment, studying eye movements as students solve problems, and so on.
Ric Glassey <glassey@kth.se>
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Computational Modelling and Analysis of Brain Activity
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
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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Emerging Computing Paradigms
Recently, new computing paradigms, such as Quantum and Molecular computing, have emerged as promising approaches to substitute traditional silicon-based computing for efficiently solving a particular class of problems or enabling new applications. While still in their infancy, these new emerging computing paradigms are already assisted by an ecosystem of programming languages and simulators that allow researchers to experiment and design algorithms to exploit these new computing models. My research interest lies in designing programming abstractions and interfaces, algorithms, applications (such as machine learning, molecular docking, … ), and emulators (that also take into account realistic hardware setup) for emerging computing paradigms.
Stefano Markidis <markidis@kth.se>
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DigiMat - lead your own project in the largest online course at KTH!
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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Researching the GISAID data base of SARS-CoV-2 genomes
The world-wide pandemic has led to an unprecedented effort to sequence strains of the SARS-CoV-2 virus and to make them available to the scientific community. The GISAID data base has emerged as the No 1 repository of complete and partial virus genomes. As of January 11 2022, the repository contains 6,983,385 genomic sequences, many of them complete SARS-CoV-2 genomes.
The project topic is to for the student to familiarize herself or himself with GISAID and its interface, to define a research question using GISAID data, and then to carry out the research. As inspiration the student can look at the following two scientific papers, which also contain links and descriptions of useful tools:
1. Mutation frequency time series reveal complex mixtures of clones in the world-wide SARS-CoV-2 viral population, HL Zeng, Y Liu, V Dichio, K Thorell, R Nordén, E Aurell, arXiv preprint arXiv:2109.02962
2. Temporal epistasis inference from more than 3,500,000 SARS-CoV-2 Genomic Sequences, HL Zeng, Y Liu, V Dichio, E Aurell, arXiv preprint arXiv:2112.12957
Erik Aurell <eaurell@kth.se>
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Neuronal Networks and Neurocomputing for Technology and Medical applications
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.
Jörg Conradt <conr@kth.se>
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