Specific Instructions for Students at the Department of Intelligent Systems

The information on this page holds for students at the Department of Intelligent Systems (IS) who are planning to conduct their degree project under one of the following course codes: 

This page will give you guidance on practical questions regarding your degree project, like for example,

  • Information Material from the Degree-Project Coordinator 
  • Choice of Topic of Your Degree Project
  • Degree Project Proposals Offered by IS
  • Finding an Examiner

 

Information Material from the Degree-Project Coordinator (for Spring 2022!)

A summary of the degree project process and some guidance for the project proposal are provided in the following video presentations:

The slides can be downloaded here: Slides Links to an external site.

The proposal template and the DIVA publication permission can be found here:

The link to the online-application can be found on this page:

 

Choice of Topic of Your Degree Project

Examiners at the Department of Intelligent Systems cover the topics for degree projects listed below.

For students from the Master programs Machine Learning (DA233X), Industrial Engineering and Management (DA235X), Systems, Control and Robotics (EA/DA236X), and Information and Network Engineering (EA260X), this list limits the range of topics for your degree project. If you plan a project that falls outside theses topics, you may experience difficulties in finding an appropriate examiner. Contact the degree project coordinator Ragnar Thobaben, exjobb-is@eecs.kth.se for guidance in this case.

For double degree students, exchange students, and other students on course codes EA238X and EA250X, examiners from the Department of Electrical Engineering are also available and offer a wider range of topics (e.g., power and energy systems, electromagnetic engineering, fusion and plasma physics). Contact the degree project coordinator Ragnar Thobaben, exjobb-is@eecs.kth.se if questions arise.

  •  Acoustics: Music technology; Music information; Voice
  • Agents and interaction: Embodied virtual agents; Intelligent virtual agents; Multi agent systems; Facial animation; Multimodal interaction; gesture generation
  • Autonomous systems: Autonomous vehicle; Autonomous robot; Drones; Applications of AI; Task and motion planning
  • Computer vision: Object recognition; Eye tracking; Gesture recognition; Image classification; Image analysis; Biomedical image analysis
  • Decision and Control Systems: Dynamical systems; Control theory; Optimization and control; Decision-making; System identification; Learning dynamical systems; Secure control systems; Process control; Modeling biological systems; Control of transport systems; Hybrid systems; Human in the loop control systems
  • Life science: Biomedical image analysis; Neural models and simulation; Neural coding; Biochemical models and simulation; Molecular models and simulation
  • Micro and nanosystems: Microfluidics; Microphysiological models; Microwave technology; Microwave telecommunication; Optical microsystems
  • Networked systems and communication: Information & Communication Theory; Network security; Wireless networks/communications; Cyber security; Cellular networks; Communication systems cyber-physical systems; Internet of things; Machine-learning for/over networks; Physical layer processing
  • Neural networks: Convolutional neural network; Generative adversarial network; Graph neural network; Deep learning; Generative deep networks; Brain-like learning; Self-organising networks
  • Robotics: Human-robot interaction; Social robotics; Perception in robots; Grasping in robots; Multi-robot systems; Applications of robotics
  • Signal processing: Multimedia processing; Pattern recognition; Sensor fusion
  • Software engineering/technology: Computer security; Software technology; Ethical hacking; Formal methods; Privacy; System architecture
  • Speech & language technology (NLP): Speech synthesis; Speech recognition; Conversational Systems; Text generation; Text classification; Text analysis; Information retrieval
  • Statistical modeling: Reinforcement learning; Transfer learning; Bayseian network; Probabilistic deep learning; Classification; Data mining; Graph modeling; Statistical analysis; Anomaly and out-of-distribution detection; Clustering; Time series data

 

Degree Project Proposals Offered by IS

For degree projects at the Department of Intelligent Systems, please frequently check the following:

 

 

Finding an Examiner

Students at IS are highly encouraged to make an attempt to find an examiner on their own before sending in their application for degree project. Since finding an examiner can be a challenge especially in peak times, we will accept and process applications for degree project for Period 3, Spring 2022 (i.e., starting Jan.-Mar. 2022) that come in without a confirmed examiner and supervisor as well.

When you approach prospective examiners, we would like you to respect the following:

  • Make sure the examiner you contact is indeed listed as an examiner on your course code. This information can be found in the course syllabus (linked in the list of courses above). 
  • Always state programme and course code, and include a well written and concise project proposal. Remember that proposals are competing with each other and that proposals that are incomplete, lack important information, or feature a weak research question have it generally more difficult to find an examiner. 
  • Do not flood the examiners with emails and avoid contacting multiple examiners in parallel (i.e., not more than two).
  • Take into account that it may take a few days to receive an answer (especially for popular examiners that have high visibility in the programme). Start early, follow up after 3-5 business days if you do not hear back. Follow up immediately if you have contacted multiple examiners and have come to an agreement with one of them! 
  • If you are interested in working with examiners from 

     always contact these examiners directly before you send in your application for degree project. 

 

To give you guidance, you can find examiner lists per topic in the following excel sheet online:

  Examiner and Supervisor Lists 2022 Links to an external site.

Note: This excel sheet does not provide an exhaustive list, and there are examiners outside that list who also take on degree projects. A complete list of examiners can be found in the course syllabus of your degree project course linked above. The are furthermore PhD students available as supervisors not listed in the tables which will be allocated by the examiners and/or the degree-project coordinator in coordination with the divisions.