Hackathons

Hackathons – Dutch Data Science Week

During Dutch Data Science Week, there are several hackathons where data scientists can apply their skills to solve real societal challenges on topics like reducing energy waste, improving healthcare, and enhance city life.

How to Reduce The Waste of Energy by Disaggregating the Energy Signal – Eneco

Work on unique data sets from the Toon smart thermostat to create smart data applications that help consumers to prevent wasting energy consumption. Eneco provides the data set but also provides insight in the way they disaggregate electricity signals to recognize individual appliances, like dishwasher, washing machine, water cooker, and frying pan.

Anomaly Detection on 4d Heart Scans – UMC Groningen

In this hackathon, we build upon recent advancements in the field of automated segmentation on the 4D (spatio-temporal) MRI data. This work is currently performed in collaboration with the cardiovascular department of the University Medical Center Groningen, in order to alleviate the medical staff from having to manually paint the regions of the left- and right- heart chambers and the heart muscle. The deformation of the heart regions during a heartbeat is indicative of deciding for a pacemaker or assessing the outcome of medicine usage. Automating the image segmentation task and the extraction of characteristic metrics of the heart function would, therefore, be extremely useful.

We will process cardiovascular MRI and patient data to assist in the formulation of a medical diagnosis. Using deep learning, image segmentation is performed on the MRI data such that metrics can be derived that potentially are predictive indicators for certain diseases and physical conditions. Patient data consists of variables such as age, gender, earlier diagnosis, medicine usage, etc.

In the hackathon, we focus on feature extraction from the segmentation masks and combining those features with patient data. In preparation for the hackathon, we will deliver the segmentation masks (the training of deep models is computationally not feasible for a single day session) and propose several us

Using Image Recognition to Track Urban Developments

Litter, asset, and building management are among the most important issues for Amsterdam’s citizens.

We are at the beginning of using image recognition to track urban developments. Did you know that the whole city of Amsterdam is openly available as high-resolution panoramic images? This has inspired some companies to improve their ownimage recognition tools. The opportunities are endless. In the US for example, researchers have tracked gentrification. In this hackathon, we aim to explore image recognition techniques, in order to track urban developments the city government should respond to. We will use

  • Neighborhood developments (gentrification)
  • Civic reporting (meldingen)
  • Asset management

We will use data of the city of Amsterdam and other available open data. We aim to get insights so Amsterdam can grow to become more of a responsive city. Image recognition is used to spot what the local government should do to make Amsterdam’s residents happier.