Education

KTH Royal Institute of Technology

B.Sc in Information Technology

M.Sc in Computer Science

2016-2021

At KTH, I'm majoring in Computer Science with a specialization in Data Science. During my B.Sc education, I studied the foundations that make up the computer science field. Courses in mathematics, programming, algorithms, and data structures led me to develop a broad knowledge of the area of computer science. During my master's, I've chosen to deepen my understanding in the domain of data science, deep learning, and data mining.

Experience

Software Development Intern

Nasdaq

Summer 2020

I worked as an intern on the NCSD (Nasdaq Central Security Deposit) development team responsible for the development and support of NMH (Nasdaq Message Hub). The primary responsibility of NMH is message conversion between external formats (ISO 15022, ISO 20022) and internal formats supported by the backend systems. I was tasked with message validation, bug fixing, and general improvement of the system.

Founder & Software Developer

Nextline AB

2019-Present

I founded Nextline AB to work as a software development consultant while attending KTH. The experience of establishing and running a company has been a great learning opportunity for me. It's been valuable to learn about building a business from the ground up, and I've come to appreciate my knowledge of Industrial Economics since I'm in charge of the bookkeeping. Since it's establishing, Nextline has been on contract with Fordonsbolaget AB.

Software Developer

Fordonsbolaget AB

2019-2020

Fordonsbolaget is a premium car reseller and a Honda retailer striving to modernize the car purchasing experience. I was part of an international development team working in scrum to build a customer-facing web application and an internal tool used by purchasing and the workshop. The frontend of both applications is developed in Angular JavaScript, and the backend is implemented in Java with Spring Boot. SQL is used to query the databases, and the APIs are built according to the RESTful specification.

Lab Assistant

Royal Institute of Technology

2018-2019

I was a lab assistant for the course Computer Hardware Engineering (IS1200). During lab sessions of the course, I examined students on their solutions to the labs. The subject of the labs ranged from assembler and C to programming microprocessors. Lunch office hours were held during lunch to give the students the option to ask for help with the subject. The ability to break down complex concepts and explaining them in a simple but accurate manner certainly improved my communication skills.

Projects


Spotify Viral Visualization

Spotify Virality Visualizer is a visualization tool used to gain insight into modern music trends on a global scale. The data used is the top 50 songs for each week and country from Spotify's viral playlists from winter 2017 to winter 2020. A viral song is a song that has a high amount of social media shares, song plays, and other web exposure in a short period. The attributes visualized are aggregated by Spotify and consists of a song's core elements.


Deep Learning Image Colorization

Image Colorization by Deep Neural Networks is a challenging problem that is actively researched. The project included building a Deep Neural Network with associated data pipelines by applying state of the art image colorization techniques and comparing them with more classic deep learning approaches based on regression. Through this, I gained a deeper understanding of the strengths and weaknesses of both. Results aligned with earlier works in the field was found.


Performance Evaluation of Imitation Learning Algorithms with Human Experts

I, together with a coursemate did this project as our B.Sc thesis and was built with Python and the artificial neural networks used Tensorflow. The project aimed to evaluate three different imitation learning algorithms that used human experts. The learning environment was TORCS, a car racing simulator. We found that for human experts, one of the algorithms outperformed the others by a large margin. More information about the project can be found on GitHub.


Concurrent N-Body Simulation

N-Body simulation is a simulation of a dynamical system of particles under the forces of gravity. This project contains two different methods of approximating such a system both with sequential and parallel implementations and was made for a course in concurrent programming. I also patched a visualization tool made in OpenGL.


Mandelbrot Visualization

The Mandelbrot set is a set of complex numbers received from iterating a function until convergence. It is often visualized with hypnotic zooming that dives infinitely deep into the set. The project is entirely written in Elixir, a functional programming language based on Erlang.


Neural Network Implementation in Numpy

I implemented an artificial neural network in Python with just Numpy for a project to an introductory course in artificial intelligence. Since digit recognition is one of the most simple test cases for machine learning, the network implementation was evaluated with the MNIST dataset that contains a large number of written digits. An accuracy of 96% was achieved with some parameter tweaking.



Languages

C/C++

Python

Java

JavaScript

SQL

Elixir

HTML 5

CSS 3


Technologies

Tensorflow

Git

AngularJS

ReactJS

Jupyter Notebook

Scikit-Learn

NumPy

Bash

Docker


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