Kristin Kaltenhäuser

My research is at the intersection of marchine learning (ML), computer-supported cooperative work (CSCW) and human-computer interaction (HCI). I’m interested in exploring new and interdisciplinary methods to draw meaning from quantitative data and use statistical and ML tools in creative ways to study real-life phenomena and concepts. I get excited about new ways of enabling various communities to take part in the development and implementation of AI into our society.

I have a BA in Media and Culture Studies, a MSc in Software Design, specialising in algorithms and data science from the IT University in Copenhagen, Denmark, and an MA in Intercultural Communication and Gender Studies from the European University in Frankfurt/Oder, Germany. Additionally, I have several years of work experience: I worked as a diversity analyst at CERN, Switzerland for two years. During my three years at Ørsted, a wind energy company in Denmark, I worked as a data scientist and model engineer, building, deploying and maintaining machine learning models to predict failures in large machinery. Currently, I am a PhD student with the Software, Data, People & Society section (SDPS) at the Department of Computer Science of the University of Copenhagen, Denmark and part of the Confronting Data Co-Lab.

My PhD project is part of the interdisciplinary and international research project NordASIL spanning law, computer science and medicine. The project is looking to answer two questions: What factors shape the production of national asylum decisions in Denmark, Sweden and Norway? and Why do asylum outcomes across similar cases differ so much from one another?

Drawing on the unique access to large datasets of asylum case summaries, my interest lies with exploring new and interdisciplinary methods to draw meaning from numerical and text data and integrating human values into data science practice. Using techniques from participatory design, statistics and natural language processing, my PhD project is about grounded sensemaking of data in asylum decision-making in the Nordic countries. My aim is therefore to drive the agenda of human-centered data science, data feminism, machine learning, as well as participatory NLP.

I am a public speaker on topics such as my research and diversity and inclusion.

In my free time, I like to do weight lifting, yoga and the occasional run. I also enjoy to meditate, to knit and immersing myself in art exhibitions. I passionately teach Python and data analysis to beginners, which I used to do for ReDI School of Digital Integration in 2021 and Ukrainian refugees in 2022.

I am also a certified NLP (neuro-linguistic programming) practitioner, trained by the amazing Tristan Soames.