Projektämne (Project themes)
Please email your preferred project options to kexjobb.csc@ncslab.se by Friday the 22nd of January, 2021.
Remember to send
-) your group information: two names and two email addresses
-) multiple topic area suggestions: ideally 3 areas in ranked order
If you plan to write in Swedish, please indicate this in your email (so we find a supervisor who speaks Swedish)
The assignment of students to supervisors will be published on Tuesday, January 26!
Any other questions please also use the email above. 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 1
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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High-Performance Scientific Computing
My research area focuses on the design, development, performance evaluation, and optimization of scientific applications for High-Performance Computing Systems. Scientific applications include linear algebra and spectral analysis (matrix-matrix multiplies, linear solvers, FFT, convolutions…), plasma physics (iPIC3D code), computational fluid dynamics (Nek5000 code), and data analysis via deep-learning frameworks (TensorFlow and PyTorch). High-Performance Computing systems include multi-core processors (Intel, AMD, emerging ARM, and RISC-V processors), GPUs, FPGAs via High-Level Synthesis (HLS) tools, and new storage technologies, e.g., object storages. Relevant projects in High-Performance Scientific Computing comprise studies of the impact of different floating-point precisions and formats, e.g., Posit vs. IEEE, design of parallel algorithms of benchmark applications for multicore CPUs and GPUs, performance analysis, and optimization.
Stefano Markidis <markidis@kth.se>
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Security and Privacy in Decentralized Systems
The Tor Project Links to an external site. is an example of a decentralized system focusing on security and privacy. The purpose of the system is privacy (anonymous communication), it's an adversarial setting (someone wants to deanonymize users) so security is essential. There is quite a lot of research Links to an external site. that remains to be done on Tor. And even more considering anonymous communication in general. Links to an external site.
Privacy is important. Another property that is equally important is verifiability. And these are important to have at the same time. Consider (electronic) voting as an example. There are other settings, for instance: verifiability to thwart Twitter influence campaigns. We need to be able to post anonymously, but we must still detect if it's a million users supporting something or just one person with a million accounts.
Daniel Bosk <dbosk@kth.se>
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Technology Enhanced Learning 2
The teacher must know what the students learn and what they struggle with to adapt teaching to optimize learning. We can use technology to aid the teacher in various ways; this can come in the form of automated assessment and analysis (e.g. machine learning on student works), or technical constructions to guide/drive the students' learning better (e.g. provide learning material and facilitate interaction around it better).
One area in this topic that I'm particularly interested in is using decentralized systems for technology enhanced learning. As an example, have interactive videos that don't need (centralized) services like YouTube, where the content owners (video producer, comment/question producers) have better control of their data (GDPR friendly) and don't need infrastructure (YouTube's data centers) to use it.
Daniel Bosk <dbosk@kth.se>
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Adversarial input
There are many great algorithms out there, just open a random page in Knuth's The Art of Computer Programming or revisit a course on Algorithms and Data Structures. Many algorithms are designed to deliver mind-blowing performance at complex tasks. But there is an underlying implicit assumption: the input is benign. Just open a web browser and we can see that this assumption doesn't always hold true. Much suggests that this assumption is actually holds true less and less. This area requires exploring.
See Kenny Paterson's keynote talk at CANS'20 for inspiring examples of what has been done so far:
Probabilistic Data Structures in Adversarial Settings
Links to an external site.
Daniel Bosk <dbosk@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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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 BA 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, brain-computer interfaces (EEG), and motor control. We might include machine learning on collected data sets when appropriate.
Jörg Conradt <conr@kth.se>
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