Master thesis

Goal of a master thesis.

The master thesis project is the final step in your studies before graduation. As such its intention is to show what you have learned in your studies by applying your knowledge and capabilities in an independent scientific work. In that sense, the master thesis project becomes the crown of your academic career so far and will lay the first cornerstone for your future career.

More formally, the master thesis project is a course like any other with the only distinction that you are working independently, most often as part of a larger team or research group. The aims of the master thesis project are described in detail at the corresponding course plans. Generally, you should show that you can independently plan and carry out a project in Mathematics and present the results, in a written report and orally at a presentation, including a discussion of the prerequisites, methodology, approach and results of the work.

Components of a master thesis project

The main outcome is a project report (master thesis). Here, you will present the findings and results of your project in a scientific manner. However, the goals of a master thesis project are much broader than that: You shall show a number of capabilities, knowledge and attitudes indispensable for a scientist or engineer. Thus, further activities are required where you must show your abilities in mathematical communication. This consists of an oral presentation and discussion of your work and of being an opponent at other thesis presentations.

How to find a project

Once you are eligible for starting a master thesis project you can begin to search for a suitable project. Projects can be carried out at KTH, most often at the Department of Mathematics, or at other collaborating departments at KTH. But many projects are done at a company or other external institutions or authorities. It is also possible that you are carrying out your master thesis project abroad. It is recommended to start the search well ahead of time. Remember, you will not be the only one looking for a project! Also keep in mind that the project must have sufficient scientific level and scope. Before starting the administrative process (master thesis project application, signing of necessary agreements, and much more), you need to submit a project proposal and get it approved by your examiner.

In order to find a project proposal you may consult KTH’s Degree Project Portal . You may also look at company web sites where you will often find proposals, too. Another source of information are your professors. Often, they will have offers or can guide you to colleagues who might have interesting proposals.

More details and the administration

For each of the programs and study tracks, certain special rules apply. Before you start it is important you keep the following in mind:

More information specific to the various directions can be found at the following webpages:

Mathematics (TMAKM)

Examples of typical master theses

Scientific computing (coma), mathematics of data science/statistics (dave), financial mathematics (fima), optimization and systems theory (opsy).

Environmental Engineering (msenvieng)


Practical matters - master of science (msc) in environmental engineering 2-years, nordic five tech.

Practical matters

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The practical aspects of your stay in the Nordic countries depend on the rules that apply in each country. Generally you should check what applies for each of the two countries that you will be studying in, as well as for each of the two universities.

Before your studies


Tuiton fees

Each of the participating universities has different rules regarding tuition fees. Please check what rules apply for each of the two universities that you will be studying at.

Residence permit

Generally EU/EEA citizens do not need a residence permit for the Nordic countries. For details, please check the regulations of each of the two countries that you will be studying in.

Residence permit in Sweden

Please check what rules apply at each of the two universities that you will be studying at.


The process of getting student accommodation differs depending on each participating university. Please check what rules apply for each of the two universities that you will be studying at.

Admissions letter

Chalmers and KTH

Getting started with your studies at Aalto

All admitted students are expected to attend the mandatory enrolment at Chalmers. In addition to the mandatory registration session, you will meet your new classmates and the coordinator responsible for your master's programme on a start-up meeting.

All admitted students must attend the mandatory enrolment at KTH. In addition to the mandatory registration session, you will meet your new classmates and the coordinator responsible for your master's programme at a start-up meeting.

We organize an orientation/ welcome programme for all new international students the week before studies start. This programme is not mandatory, but we strongly encourage all students to attend as they will get a lot of useful information during this week and also get to meet their fellow students.

The entire application process at DTU is digitalized. This means that the applicants need to upload scanned copies of all relevant documentation to the DTU Application Portal

Academic year

Course registration

Course registration aalto.

Students may need to sign up for lectures, assignments and exams separately in the registration system Weboodi. The credits obtained from a course include lectures, exercises and the individual work the students do for a course.

Course registration Chalmers

You will already be registered for the mandatory courses after the enrolment.

If you are supposed to choose elective courses please send your course selection to the administrative coordinator.

Course registration KTH

Course registration ntnu.

Students enrolled in a Nordic 5Tech-programme will be automatically registered for mandatory courses, but they have to register for electives by 15 September on our online system studentweb. Information will be given during the orientation programme.

Course registration DTU

Students will need to register online through DTUs Intranet.

As soon as students have received their DTU student number and password, they can log on to DTUs Intranet.


Aalto will send the second year student acceptance letter, confirmation letter (acceptance of study place) info package including information about accommodation and Welcome guide during April.

Students of year 2 will receive information from the university during the Spring term of year 1. Please send your course selection to the administrative coordinator.

Please send your course selection to the administrative coordinator of your programme.

Admission letters are sent out in late April to students who will start their second year at NTNU with information about housing and orientation programme. The student does not have to apply for credit transfer, but we need a transcript with the results from the first year so that the relevant Faculty can register the courses into NTNU’s system. As transcripts often are not ready until late summer/ early Autumn, the registration process should be done in the Autumn semester. The transcript from the first year should should be sent out to the second year university by 1 October in the third semester.

As a second year student you will be contacted by DTU in March regarding the registration process and how to apply for accommodation.

Credit Transfer

The universities handle the credit transfer from year 1 to year 2.

For issues related to credit transfer you need to contact the coordinator of your programme to make sure that the credits are transferred without any unnecessary delay.

The double master degree includes a 30 ECTS thesis work at the second year university under co-supervision from both institutions to ensure integration of the programme components.

Students at Aalto School of Engineering starts to apply for thesis topic after they have earned 60 ECTS. Students read carefully the Master´s thesis guideline before they start.

Thesis guide

For thesis work please contact your programme coordinator.

For thesis work you need to contact your programme coordinator.

The thesis must be registered in NTNU’s database called DAIM .

After your studies

How to get your diploma.

Aalto School of Engineering students apply for Master´s degree after finishing all their studies.

Aalto School of Science students apply for Master´s degree

Aalto School of Chemical Technology apply for Master´s degree

Aalto School of Arts, Design and Architecture apply for Master´s degree

After completing your Master's studies you can apply for your Degree certificate. Apply in this way.

All courses must be completed before submitting your application. You must attach a certified copy of your Bachelor’s degree. Please note that this is not applicable if you already have presented a complete Bachelor’s degree when applying for admission. In addition you must attach a transcript of records from the partner university when applying. You must apply for your degree to receive a degree certificate. You may submit your application online and the ordinary processing time is 6-8 weeks. Please read the information on the website before applying.

After the courses from the first year has been registered and all course and thesis have been graded, a diploma will be sent to the student’s address. Students do not need to apply for a diploma. Please note that after the thesis has been submitted, it might take up tp three months before the grade for the thesis is registered. Only then can the diploma be issued.

Graduates will receive their Diploma automatically 4-8 weeks after the thesis defense.

Graduation Ceremony

Graduating Aalto School of Engineering students can either join the graduation ceremony which are held 6 times a year or pick up the certificate from Student service desk after the ceremony date. Graduates may authorize the School to mail the degree certificate to them. Please note that degree certificates are mailed outside Finland only in special situations.

Chalmers holds two graduation ceremonies each year to honour the graduating students who have received their degrees from Chalmers.

The KTH graduation ceremony is held twice in May and twice in December for newly graduated architects, masters of science in engineering and master's degree holders from KTH, the Royal Institute of Technology.

There is no official graduation ceremony at NTNU.

Graduates are invited to a graduation ceremony in October or March depending on when they graduate. Students will be contacted directly regarding the registration.

Career service

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Search for dissertations about: "KTH Royal Institute of Technology"

Showing result 1 - 5 of 141 swedish dissertations containing the words KTH Royal Institute of Technology .

1. Designing with Urban Sound : Exploring methods for qualitative sound analysis of the built environment

Author : Nina Hällgren ; Katja Grillner ; Björn Hellström ; Gunnar Sandin ; Konstfack ; [] Keywords : HUMANITIES ; HUMANIORA ; HUMANITIES ; HUMANIORA ; HUMANIORA ; HUMANITIES ; Architecture ; urban sound design ; urban sound planning ; soundscape ; urban planning ; artistic research ; architectural research ; Architectue ;

Abstract : The licentiate thesis Designing with Urban Sound explores the constitution and qualitative characteristics of urban sonic space from a design-oriented and practice-based perspective. The act of lifting forth and illuminating the interaction between architecture, the creation of sound and a sonic experience aims to examine and develop useful tools and methods for the representation, communication and analysis of the exterior sonic environment in complex architectural spaces. READ MORE

2. Technological knowledge and technology education

Author : Per Norström ; Sven Ove Hansson ; Per Sandin ; Peter Kroes ; KTH ; [] Keywords : HUMANITIES ; HUMANIORA ; HUMANIORA ; HUMANITIES ; philosophy of technology ; epistemology of technology ; technology education ; technological knowledge ; rule of thumb ; explanation ; Filosofi ; Philosophy ;

Abstract : Technological knowledge is of many different kinds, from experience-based know-how in the crafts to science-based knowledge in modern engineering. It is inherently oriented towards being useful in technological activities, such as manufacturing and engineering design. READ MORE

3. Ethics of Imprisonment : Essays in Criminal Justice Ethics

Author : William Bülow ; Till Grüne-Yanoff ; Göran Duus-Otterström ; KTH ; [] Keywords : HUMANITIES ; HUMANIORA ; HUMANIORA ; HUMANITIES ; Collateral Harms ; Communicative Theory of Punishment ; Consequentialism ; Criminal Justice Ethics ; Doctrine of Double Effect ; Electronic Monitoring ; Imprisonment ; Legal Punishment ; Moral Education Theory of Punishment ; Privacy ; Philosophy of Punishment ; Retributivism ; Filosofi ; Philosophy ;

Abstract : This licentiate thesis consists of three essays which all concern the ethics of imprisonment and what constitutes an ethically defensible treatment of criminal offenders.Paper 1 defends the claim that prisoners have a right to privacy. I argue that the right to privacy is important because of its connection to moral agency. READ MORE

4. Philosophical controversies in the evaluation of medical treatments : With a focus on the evidential roles of randomization and mechanisms in Evidence-Based Medicine

Author : Alexander Mebius ; John Cantwell ; Jeremy Howick ; Sven Ove Hansson ; Julian Reiss ; KTH ; [] Keywords : MEDICAL AND HEALTH SCIENCES ; MEDICIN OCH HÄLSOVETENSKAP ; HUMANITIES ; HUMANIORA ; MEDICIN OCH HÄLSOVETENSKAP ; HUMANIORA ; MEDICAL AND HEALTH SCIENCES ; HUMANITIES ; Evidence ; randomized controlled trials ; observational studies ; systematic reviews ; meta-analysis ; methodology ; process assessment ; outcome assessment ; medical care ; randomization ; evidence-based medicine ; selection bias ; philosophy of medicine ; philosophy of science ; mechanisms ; quality of evidence ; animal studies ; treatment effect ; causation by absence ; medical technology ; Filosofi ; Philosophy ;

Abstract : This thesis examines philosophical controversies surrounding the evaluation of medical treatments, with a focus on the evidential roles of randomised trials and mechanisms in Evidence-Based Medicine. Current 'best practice' usually involves excluding non-randomised trial evidence from systematic reviews in cases where randomised trials are available for inclusion in the reviews. READ MORE

5. Geoid Model of Tanzania from Sparse and Varying Gravity Data Density by the KTH method

Author : Prosper Ulotu ; Lars Sjöberg ; Hossein Nahavandchi ; KTH ; [] Keywords : NATURAL SCIENCES ; NATURVETENSKAP ; Geoid ; sparse gravity data ; gravity database ; GGM validation by gravity ; corrector surface ; hybrid geoid ; KTH-LSMS with AC ; Mt. Kilimanjaro ; Tanzania. ; Earth sciences ; Geovetenskap ;

Abstract : Developed countries are striving to achieve a cm geoid model. Most developing countries/regions think that the situation in their areas does not allow even a few decimetre geoid model. GNSS, which provides us with position, is one of the greatest achievements of the present time. READ MORE

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Data Science (DSC)

Entry & Exit points

Entry points, 1st year, Common Courses

Academic year 2023/2024

KTH Royal Institute of Technology (KTH)

Programme website

Programme Lead: Henrik Boström,  [email protected]



Technical University of Madrid (UPM)

Programme Lead: Marta Patiño

Université Côte d’Azur (UCA)

Programme Lead: Francoise Baude,  [email protected]

Polytechnic University of Milan (POLIMI)

Programme Lead: Paolo Cremonesi,  [email protected]


Notes: - The I&E Minor will be increasingly offered in a blended format, with integrative online modules and innovative teaching methods (blended I&E Education). - In evaluating students’ previous BSc studies, the Admission Committee at POLIMI can provide minor directions on the individual study plans, regarding the choice of courses to be attended.

Aalto University (Aalto)

Programme Lead: Wilhelmiina Hämäläinen;  [email protected] 

Total for the whole year: 60 ECTS

Note for exit year at partner university: According to Finnish legislation, a master's thesis is a public document and its contents cannot be confidential. Therefore, the material of the thesis must be chosen so that it does not include any information that could be classified as a business secret of the financing company. More information about Master's thesis process for Aalto entry students  here .

Eötvös Loránd University (ELTE)

Programme Lead: Tomáš Horváth,  [email protected]

University of Rennes 1 (UR1)


University of Twente (UT)

Programme Lead: Dr. Maurice van Keulen:

University of Turku (UTU)

The University of Turku provides an entry point for the EIT Digital master program in Data Science starting from the academic year 2022-2023.

The entry year courses are the following:

Exit points, 2nd year, Specialisation

Academic year 2024/2025

SPECIALISATION: Distributed Systems & Data Mining for Big Data

The Distributed Systems & Data Mining for Big Data specialisation focuses on providing students with analytical and programming skills to be able to build systems that efficiently process big data. After completing the specialisation at KTH, the students will be able to effectively design and implement systems to handle data at any stage in the data mining process, from batch-oriented to real-time stream processing. Students will be able to write efficient programs that extract useful information from big data. They will acquire deep skills in subfields of data mining such as mining graphs, text and streaming data as well as scalable learning algorithms.

The students work with well-known platforms such as Hadoop, Flink, Spark, GraphLab, Mahout, and H20. There are frequent guest lectures from the many companies that are active in data science in the Stockholm region (some of which are listed below).

For the 2nd semester, students are offered practical industrial experience in cooperation with Stockholm-based companies, such as Ericsson, Spotify AB, Ltd, Scania and Oracle (MySQL), or research-oriented projects, e.g., at RISE SICS and Ericsson Research. These companies and organisations, as well as several fast-moving start-ups, already cooperate with KTH on data science projects and in the context of this master's programme.


Henrik Boström is Professor of Computer Science - data science systems at KTH Royal Institute of Technology. His research focuses on machine learning algorithms and applications, in particular ensemble learning, prediction with confidence (conformal prediction) and interpretable and explainable machine learning. He is action editor of the journals Machine Learning and Data Mining and Knowledge Discovery. He regularly acts as senior programme committee member of some of the most prominent conferences in the area, including SIGKDD, IJCAI, AAAI.

SPECIALISATION: Infrastructures for Large Scale Data Management and Analysis

The Infrastructures for Large Scale Data Management and Analysis specialisation focuses on how to use large scale data management and big data infrastructures for processing, storing and analysing huge amounts of data as well as building new applications on top of them. Students will learn:

Students will be able to do internships and cooperate with large companies like Telefonica, Indra, Atos and also with startups from UPM in the area of Big Data such as Localidata and LeanXcale. Localidata focuses on the value data chain. LeanXcale provides a leading-edge Real-Time Big Data Analytics platform.

Marta Patiño is professor at UPM. She is Distributed Systems co-director and co-founder of LeanXcale startup on Real-Time Big Data analytics. She is also funder member of the research center for Open Middleware. She is co-inventor of 3 patent applications. She has coordinated several national projects and the EU funded projects LeanBigData, CoherentPaaS, and CumuloNimbo. She is co-author of the book Database replication and published over 100 papers in international conferences and journals such as SIGMOD, VLDB Journal, ACM Trans. On Database Systems, ACM Trans. On Computer Systems, IEEE Trans. On Parallel and Distributed Systems, etc. Her research areas include scalable transactional processing, scalable complex event processing, online analytical processing, cloud computing, big data, fault-tolerance.

SPECIALISATION: Multimedia and Web Science for Big Data

All courses are taught in English despite the title in French. Students need to get at least an average mark of 10/20 in a block (please see the * sign) in order to get the corresponding ECTS.

SPECIALISATION: Machine Learning, Big Data Management and Business Analytics



Programme Lead: Tomáš Horváth,  [email protected]

SPECIALISATION: Real-time Data Analytics 

The Real-time Data Analytics specialisation focuses on state-of-the-art solutions for supporting real-time data driven decision making. The (research) areas covered by the specialisation concern stream mining, sensor data analytics, complex event processing and network analysis.

Real-time data analytics can be found in many application domains ranging from Industry 4.0 through business to healthcare, playing a crucial role in areas such as control systems of self-driving cars, prediction of assembly line malfunctions, fraud detection in financial transactions or early recognition of anomalies in health monitoring. Students will be familiarised with:

Students will learn to use and utilise open-source technologies such as Hadoop, Spark, Flink, ElasticSearch, Kibana or Tableau, just to name a few. In addition to good theoretical basics of clustering, prediction, pattern mining and pattern recognition techniques, students will have a chance to gain practical experiences during internships at our industrial partners with whom we have well-established cooperation.

Tomáš Horváth is the head of the Data Science and Engineering Department, established in September 2016 by Deutsche Telekom, of the Faculty of informatics of the Eötvös Loránd University in Budapest, Hungary. He received his MSc and PhD degrees at the Pavol Jozef Šafárik University in Košice, Slovak Republic, in 2002 and 2008, respectively. He was on a post-doc internship at the Information Systems and Machine Learning lab of the University in Hildesheim, Germany, from 2009 to 2012. From 2015 to 2016 he received a post-doc grant at the Department of Computer Science, University of São Paulo in São Carlos, Brazil. His research interests include relational learning, rule-based and monotone classification techniques, pattern mining, recommender systems and personalization. Recently, he is focusing his work on meta-learning techniques and automated machine learning approaches.

University of Trento (UNITN)

Contact: [email protected]

SPECIALISATION: Big Data Variety and Veracity

Data Science has increasingly become an integrated approach where modern scientists analyse data collected from many different sources. Before this data can be leveraged by data analytics, it has to be cleaned, transformed and integrated. This kind of data preparation is challenging, laborious, time consuming and error-prone. It is estimated to cost data scientists 50% and 80% of their time and effort. The reason is the heterogeneity and quality issues that inherently exist in the data. It, thus, comes as no surprise that Variety and Veracity are considered two fundamental characteristics of Big Data (alongside Volume and Velocity).

The Big Data Variety and Veracity specialisation at the University of Trento aims at providing the students with all the necessary knowledge to be able to understand, use, and develop tools, techniques and methodologies for efficiently and effectively coping the variety and the veracity of big datasets. Throughout the courses the students will learn about the different kinds of challenges faced in real scenarios, the existing algorithmic approaches, the software solutions that are available, the commercial tools that one can use, and the evaluation methodologies that can be applied. At the end of the programme, they will be able to identify the data management challenges in real-world situations, select the best solution for the task at hand, and apply that solution successfully.

In the first semester, the students with take a number of technical courses that will provide them with the necessary specialisation foundations. The lectures are often enriched by external experts from industry and academia. In the second semester the students will obtain their industrial experience by performing their internship in a company and also materializing their thesis work, which may be on a separate topic than the internship or a related topic that can be seen as an extension of the internship work.

SPECIALISATION:    Artificial Intelligence & Data Mining for Business Intelligence

Our exit year focuses on artificial intelligence methods in general, and data mining in particular, to address the challenges of business intelligence. Those challenges include numerous tasks that are covered by our courses, such as pre-processing and storing decisional data in data ware-houses, managing Big Data in the cloud, learning models and perform inferences, mining knowledge out of data, and visualizing both data and learned knowledge. The focus is put on the data science methodology rather on the arcane details of specific methods, although the courses will bring fundamental knowledge about the main AI & data mining methods. The students will learn to analyse a business intelligence problems, and to make the appropriate choices among the numerous existing methods and tools. They will also learn to conduct the data science workflows, and to analyse the results in cooperation with domain experts. At Univ. Rennes 1, we have already built for decades a strong enterprise culture where students acquire competencies for communicating with non-IT domain experts, especially in business. Those competencies are highly valued and recognized by companies, who are also involved in the continuous improvement committee of the existing cursus on which this master is founded. Our specialisation is backed by IRISA, one of the biggest computer science research lab in France with 800 people. Two research departments and 12 research groups are related to data science. Most teachers in the master are also researchers in one of those groups, ensuring quickly evolving course contents.

Programme Lead: Dr. Maurice van Meulen,

SPECIALISATION:    Data Science for Persona Information

Main topics in courses as well as in final projects are covered such as: health and sports, wellbeing, biometrics and privacy.

With this master program, the students will get acquainted with and work on the following topics: big data, data analytics, information inference, machine learning, context-aware applications, smart services. With data science, one learns how to dig for value in data by analysing various data sources. With service engineering, one learns how to design services that effectively use system capabilities to satisfy user needs and requirements. Information systems that can use the results of data science to get more value out of data and become context-aware may turn traditional services into smart services. Applications of this in various domains such as pervasive health, well-being, intelligent transportation, logistics, and business intelligence.

The variety of subjects arises from the fact the program has different flavours and hence allows the students to have an orientation towards each of them, being a mathematical, a computer science, an electrical engineering and a business information flavour. 

The exit point will provide, in-depth theoretical and technical skills for becoming:

Apart from the final project, elective courses are available such as

Education and research in this setting of Data Science requires a fundamental inter-disciplinary approach bridging fields like computational statistics, machine learning, image and signal processing, information retrieval, and data processing and management. Therefor a group was formed to focus on this. The mission of the group is to work on explainable data science by developing methods for autonomous, reliable and robust gathering, preparation, and analysis of the data, to enable relevant, trustworthy and explainable results.

The second-year at the University of Turku offers a graduation project (Final Degree Project). The graduation project includes an internship at a company or a research institute and results in a master’s thesis with a strong innovation and entrepreneurship dimension.

SPECIALISATION: Medical Data Science

The Medical Data Science specialization brings together skills and principles from computer science, data analytics, and machine learning as well as from medical sciences to build theoretical understanding and practical competences in the field. On the practical side, a special emphasis is put on both traditional biomedical foci on signal processing, and medical machinery and contemporary health-related internet-of-things technologies and gives tools to work with the near future ICT technologies. This track has a strong focus on data analytics and medical Internet-of-Things while leveraging from machine learning, software engineering, and embedded electronics that are strongly represented at the University of Turku.

The students graduating from this cross-disciplinary program will have acquired scientific and analytical skills, expertise in present theories, and up-to-date technologies as well as practical skills, including teamwork, leadership, and interpersonal skills in an international environment. The acquired methodological skills and advanced knowledge allow the students to continue their career paths in academia or industry.

At the University of Turku, the EIT students select 24 ECTS from the following set of courses, taking into account the content of the entry year studies:

Budapest University of Technology and Economics (BME)

Programme Lead: Péter Antal, [email protected]

SPECIALISATION: Human-centred intelligent data analysis (HCIDA)

The declared focus of the European Union on ethical data analysis and ethical artificial intelligence (AI), exemplified by GDPR and the AI Act requires entrepreneurs to understand the moral, legal, and regulatory aspects, in addition to mastering the technology of a product. The one-year curriculum provides solid foundations for interdisciplinary dimensions and corresponding theoretical concepts and technologies. The HCIDA programme is part of the 'Data Science and Artificial Intelligent' (DS-AI) specialization in the Computer Science Engineering Master’s programme at the BME. The curriculum also overlaps* with the Human Centered Artificial Intelligence Masters (HCAIM) programme ( ); thus, its accomplishment can grant the BME HCAIM certificate.



Total credits for the whole exit year: 60 ETCS

Péter Antal founded the Computational Biomedicine Laboratory (ComBineLab) in 2009, and has been the head of the Artificial Intelligence Group since 2019.  He has published around 150 papers in intelligent data analysis, Bayesian computation, causality research, decision support, bioinformatics, and chemoinformatics.

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Co-Funded by the European Union

Daniel Lundén PhD student @ KTH Royal Institute of Technology


Visiting Address

Postal address.

I am a PhD student at the School of Electrical Engineering and Computer Science , Division of Software and Computer Systems , KTH Royal Institute of Technology .

I do research in probabilistic programming , an interdisciplinary field with influences from computer science, probability theory, statistics, machine learning, and artificial intelligence. In my research, I focus on developing mathematical foundations and efficient compilers for probabilistic programming languages. I am particularly interested in programming language theory , compilers , and static program analysis .

My principal supervisor is David Broman (KTH) , and my assistant supervisors are Lawrence Murray (Uber AI) and Joakim Jaldén (KTH) .

I carry out research as part of the ASSEMBLE project. See the ASSEMBLE web page for more information.

KTH Royal Institute of Technology Stockholm

Doctor of Philosophy (PhD) July 2017 –

Degree programme in Information and Communication Technology. Expected graduation in spring 2023.

Master of Science August 2015 – June 2017

Bachelor of Science August 2012 – June 2015

Master of Science in Engineering (Civilingenjör) August 2012 – June 2017

Degree Programme in Computer Science and Engineering (Datateknik).

SICS Swedish ICT Stockholm

Researcher June 2016 – February 2017

I worked with the Unison project: a code generator using a combined constraint model of register allocation and instruction scheduling to generate potentially optimal code. My task was to update the target description of a processor to the most recent version within the project.

Designingenjörerna Stockholm

Software Developer June 2015 – August 2015

I worked as a front-end Android and back-end PHP developer.

Software Developer June 2014 – August 2014

I worked with both front-end and back-end web development in JavaScript and PHP.

My Academy Stockholm

Study Coach May 2013 – May 2016

I assisted high school students with mathematics and related topics during the semesters.

European Symposium on Programming

Distinguished Artifact Award 2022

Commissions of Trust (Förtroendeuppdrag)

HSB Bostadsrättsförening Östra Polhem 4:2 Järfälla

Board Member (Styrelseledamot) 2020 –

Doctoral Student Representative in the ICT Doctoral Program Council 2018 –

Program Committees and Reviewing Assignments

Program Committee Member 2021

Reviewer 2021

Course Responsible for IS1200 Computer Hardware Engineering Spring 2021

Teacher in IS1200 Computer Hardware Engineering

Teacher in IS1500 Computer Organization and Components August 2017 –

Teaching Assistant in DD1361 Programming Paradigms

Teaching Assistant in DD2395 Computer Security

Teaching Assistant in DD1368 Database Technology September 2014 – September 2016

Conference and Journal Articles

Workshop Extended Abstracts

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