Hello! My name is Henrik Holm.

I have a M.Sc Eng. in Digital Media Engineering from Technical University of Denmark, specializing in Data Science. I currently work at Tonsser as a full-stack developer.

Beside work I'm interested in new technology, data science (big data, machine learning, data mining), Internet of Things (IoT) and the public debate. In my spare time I'm spending time with my girlfriend, newborn baby and friends. I like to play and watch soccer, as well as go to fitness. I like electronic music, snowboarding, TV series and travelling the world!

As a person I'm always eager to strengthen my skills and learn new stuff. I like to have influence on the product that I am developing as i believe it is the best way to create innovation and motivation.

Things I Can Do

  • Python Programming
  • iOS/Swift Programming
  • VBA Programming (Excel)
  • Database Programming (MySQL, MongoDB)
  • Machine Learning
  • Data Mining
  • Natural Language Processing
  • API's (REST, SOAP)
  • Agile & Lean Development
  • Mobile Prototyping
  • Mobile Communication
  • Linear/Integer Programming
  • User Experience

Project Portfolio

Below are listed some of the projects I have done during my studies and in my spare time.

Mobile App Prototype for Heart Disease Patients Support using HealthKit

This thesis have looked beyond the traditional telemedcine solutions by introducing a new data-driven system, which utilizes mobile apps, smart health devices and rule-based algorithms. The data-driven system may offer several bene ts compared to the existing telemedicine solutions on the market, as it is much more flexible and dynamic. By combining continuous collection of data from the patient, no matter where he or she is, with an algorithm that automatically detects anomalies and suggest when to up-titrate medication, we hope the treatment time and readmission percentage can be reduced significantly.

Winner of Climate Price at DTU Big Data Hackathon 2014

65 students developed Smarter City solutions for Lyngby-Taarbæk Kommune (LTK) over 48 hours, using data provided by LTK. The Hackathon had four prizes, where one of them was a special prize for projects with focus on climate solutions (sponsered by Climate-KIC Nordic). This prize was awarded to the Byens Bedste Bygninger project by Henrik Holm, Mads F. Engels & Simon B. Jørgensen.


Medieoverblik.dk gathers articles from 11 danish news sites in order to analyze the sentiment and topics of the danish news media. The solution is programmed in Python, hosted on an Amazon EC2 server and visualized on Wordpress. The project is done in collaboration with Mads F. Engels & Simon B. Jørgensen.

Buysmart - iOS App

This project investigates the viability of a mobile application that bridges the gap between consumer and offer, by letting the user know where items on a shopping list can be purchased on sale nearby. A prototype have been developed on the iOS platform by using the Swift programmning language. The app is currently not available on the AppStore. The source code is available at Github. The project is done in collaboration with Ulf Aslak. Here is a link to the full project report.

Information.dk - Gender Identification

In this project, raw comments from information.dk are mined in order to try predicting gender identification. Various machine learning and NLP (Natural Language Processing) algorithms have been used in this project. The project is created in collaboration with Rasmus L. Christiansen and is coded in Python. The source code is available at Github.

Realistic Twitter Bot

In this project we wanted to understand what makes people more likely to follow strangers. To do that, we created a social media bot in Python, who appears to be human. For collecting all the necessary data, Twitter API was used. For the analysis we used natural language processing, machine learning, and statistics tools, in order to differentiate the people who followed us, from those who didn’t. This project was done in collaboration with Patrick Jørgensen.

Implementation and operation of Cloud RAN systems for next generation mobile networks

In my bachelor thesis, I looked into solutions on how to optimize the Radio Access Network (RAN) in the mobile network today by using Cloud RAN architecture.

Pusblished Articles

During my studies at DTU I have published two peer review articles about C-RAN, which is a novel mobile network architecture. These are shown below.

Optimizing small cell deployment by the use of C-RANs

IEEE - European Wireless 2014

A Cloud Radio Access Network (C-RAN) is a novel mobile network architecture that has the potential to support extremely dense mobile network deployments enhancing the network capacity while offering cost savings on baseband resources.

Optimal Assignment of Cells in C-RAN Deployments with Multiple BBU Pools

European Conference on Networks and Communications 2015

In the above article, for larger scale deployment we recommend to divide the area into multiple BBU Pools. In this paper we show how to optimally assign cells to different BBU Pools in such a scenario. By using Integer Linear Programming (ILP) method we derive engineering guidelines for minimizing the CAPital EXpenditure (CAPEX) of C-RAN deployment.

Contact Me

Feel free to contact me by email or using the contact form below if you find my profile interesting for an open source project, a job opportunity or something completely different :)