About me
I am an assistant professor of social data science at the University of Copenhagen at both the Department of Psychology and SODAS. I am interested in combining psychological theory and machine learning methodologies to better understand human behaviour, with applications in social good domains. I am particularly interested in how data-driven methods can help to inform or challenge existing theories in the social sciences. Current work includes looking at how big data can be leveraged to better understand the predictors of educational attainment, cooperation, and sustainable behaviour. Previous work at The Alan Turing Institute used machine learning and AI to help prevent modern slavery and human trafficking.
WORKSHOPS
Are your research group or department interested in how you can use big data and machine learning in your work but don't know where to start?
Together with PhD candidates Babette Bühler and Hannah Deininger (University of Tübingen), we offer workshops for complete novices who want to know more about big data, machine learning, (Explainable) AI, and Networks. We offer an accessible introduction for anyone with a social science, natural science, or humanities background. We can conduct the workshop using R, Python, or with no coding.
We can be flexible to your interests, needs, and context — so please don’t hesitate to get in contact!
Feedback from our "Introduction to Machine Learning in Education" workshop at Goethe University Frankfurt (May, 2023)
RECENT WORK
INTERNSHIPS
June 2019 - August 2019
DATA SCIENCE FOR SOCIAL GOOD
Fellowship
I was a fellow on the Data Science for Social Good (DSSG) 2019 ran by Imperial College London and in partnership with The University of Chicago, The Alan Turing Institute, and Warwick University. This programme looks at how we can harness the power of data science to enable charities, start-ups, NGOs and governmental departments operating in the social good space to grow. I worked on a project in partnership with IEFP in Portugal, building a recommender system to improve employment outcomes in mainland Portugal.
July 2015 - October 2015
BIG DATA PSYCHOLOGY
Internship
A lot of our daily purchases are driven by various psychological factors. For example, people can buy chocolates either because of habit or because of lapses in self-control when facing a pack of Maltesers at the till. Whilst a wealth of decision-making literature studied psychological mechanisms of impulsive and habitual behaviours in the lab, there is still not enough research translating decision-making theories into real world choices. The Digital Economy opens up a new era in the research of human behaviour, as information about what we buy, where we travel and even what we eat can be recorded “online”, producing evidence that is more accurate than self-reported logs. This project analysed how we categorize and understand different clusters of individual daily decisions and how to interpret the outputs of these analyses.
July 2013 - August 2013
DEVELOPMENTAL PSYCHOLOGY
Internship
During the 6 week Developmental Psychology internship at the University of Nottingham I actively helped to plan and run the university's Summer Scientist Week (SSW). SSW is a fun filled week for children, which also serves as a pool of participants for the developmental psychology researchers working in and around Nottingham. My role involved event organisation, setting up the data base, testing the children's vocabulary ability using BPVS (including autistic children), calibrating the test scores, as well as analyzing the data.
PUBLICATIONS & PROFESSIONAL ACHIEVEMENTS
Publications:
Lavelle-Hill, R., Smith, G., & Murayama, K. (2023). Machine Learning Meets Traditional Statistical Methods in Psychology: Challenges and Future Directions. https://doi.org/10.31219/osf.io/6xt82
Lavelle-Hill, R., Lichtenfeld, S., Sakaki, M., Goetz, T., Frenzel, A., Smith., G, Marsh, H., Pekrun, R. & Murayama, K. (2023). Using Machine Learning to Understand how the Predictors of Maths Ability Change over Time. 10.31219/osf.io/upb7f
Zitzmann, S., Wagner, W., Lavelle-Hill, R., Jung, A., Jach, H., Loreth, L., ... & Hecht13, M. (2023). On the role of variation in measures, the worth of underpowered studies, and the need for tolerance among researchers: Some more reflections on Leising et al. from a methodological, statistical, and social-psychological perspective. preprint.
Campos, D. G., Fütterer, T., Gfrörer, T., Lavelle-Hill, R. E., Murayama, K., König, L., ... & Scherer, R. (2023). Screening Smarter, Not Harder: A Comparative Analysis of Machine Learning Screening Algorithms and Heuristic Stopping Criteria for Systematic Reviews in Educational Research. 10.31234/osf.io/fpwc2
Jach, H., Cools, R., Frisvold, A., Grubb, M., Hartley, C., Hartmann, J., ... Lavelle-Hill, R. ... & Gottlieb, J. Curiosity in cognitive science and personality psychology: Individual differences in information demand have a low dimensional structure that is predicted by personality traits. 10.31234/osf.io/aj3rp
Bardach, L., Oczlon, S., Schumacher, A., Lavelle-Hill, R., Lüftenegger, M., & Steffen Zitzmann. (under review). Teaching and Learning in a Culturally Diverse World: A Meta-Analysis on Cultural Diversity Climate in K-12 Schools.
Deininger, H., Lavelle-Hill, R., Parrisius, C., Pieronczyk, I., Colling, L., Meurers, D., ... & Kasneci, G. (2023, June). Can you solve this on the first try?–Understanding exercise field performance in an intelligent tutoring system. In International Conference on Artificial Intelligence in Education (pp. 565-576). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-36272-9_46
Lavelle-Hill, R., Harvey, J., Smith, G., Mazumder, A., Ellis, M., Mwantimwa, K., & Goulding, J. (2022). Using mobile money data and call detail records to explore the risks of urban migration in Tanzania. EPJ data science, 11(1), 28. https://doi.org/10.1140/epjds/s13688-022-00340-y
Lavelle-Hill, R. & Mazumder, A. (2022). AI for Detecting Sexual Exploitation Online in the UK: A Review of Indicators and Ethics. (accepted).
Lavelle-Hill, R., Smith, G., Mazumder, A., Landman, T., Goulding, J. (2021) Machine learning methods for “wicked” problems: exploring the complex drivers of modern slavery. Nature Humanities and Social Science Communications 8, 274. https://doi.org/10.1057/s41599-021-00938-z
Lavelle-Hill, R., Goulding, J., Smith, G., Clarke, D. D., & Bibby, P. A. (2020). Psychological and demographic predictors of plastic bag consumption in transaction data. Journal of Environmental Psychology, 72, 101473. https://doi.org/10.1016/j.jenvp.2020.101473
Lavelle-Hill, R. E., Skatova, A., Goulding, J., Bibby, P., & Clarke, D. (2020). Buying what people like you buy: Personality Homophily and Well-being in Consumer Behaviour. https://osf.io/nxsy9
Tomas, C., Whitt, E., Lavelle-Hill, R., & Severn, K. (2019, September). Modeling holistic marks with analytic rubrics. In Frontiers in Education (Vol. 4, p. 89). Frontiers Media SA. https://doi.org/10.3389/feduc.2019.00089
External Workshops delivered:
Introduction to Machine Learning in Psychology and Education Research (R).
Goethe University Frankfurt (in person). May 2023.
Introduction to Machine Learning for Educational Assessment (Python).
ZIB academy, DIPF Frankfurt (in person). Sept 2022.
Introduction to Machine Learning in Psychology and Education Science (Python).
FDZ academy Berlin (online). March 2022.
Conferences (selected):
EARLI 2023, Thessaloniki, Greece. "Using Machine Learning to Understand how the Predictors of Maths Ability Change over Time."
Data for Policy 2020, London (virtual). "Using Machine Learning Methods to Better Understand the Complexities of Modern Slavery."
UCL LIDo PhD Programme, London 2020. "Big Data Psychology"
World Conference of Personality, Hanoi 2019. "Bags of money or bags of Impulsiveness? Psychological and Demographic Predictors of Plastic Bag Consumption in Big Data."
European Conference of Personality, Zadar 2018. "Using Machine Learning Techniques to Examine the Relationship between Money, Personality, and Well-being."
GovTechLab Knowledge Transfer Consortium, London, 2018. "Big Data for Social Good"
Awards:
Winner of CosMo conference "Science Pitch" competition. Tübingen, 2022.
Awarded The University of Nottingham Travel Prize, 2019.
Awarded scholarship and funding by The European Conference of Personality, 2018.
Media Engagements:
Interviewed on NottsTV about research on the predictors of plastic bag purchasing in Jan 2022.
See news coverage from The Independent, The Guardian, and the Daily Mail.
Turing blog post. "Black Friday 2020 survival guide: Will recession or AI save us from impulse buying this year?"
Interviewed on BBC World Service on the psychological and demographic predictors of buying plastic bags in Oct 2020.
Speaker at DSSG Data Fest 2019
EDUCATION & EMPLOYMENT
University of Copenhagen
Assistant professor (tenure-track) of Psychology and Social Data Science
2023 - Present
University of Tübingen
Post-doctoral Researcher
Machine Learning in Education
2021 - 2023
The Alan Turing Institute
Post-doctoral Researcher
Using AI to Prevent Modern Slavery
2019 - 2021
University of Nottingham
2015 - 2020
University of Nottingham
PhD in "Big Data Psychology" with
N/LAB, Business School and the School of Psychology
1st class (HONS) in BSc Psychology (with international study)
Incl. Cognitive Psychology, Biological Psychology, Neuroscience, Developmental Psychology, Personality and Individual Differences
2011 - 2015
Lund University
2013 - 2014
Universitas 21 Study Abroad Program
Incl. Swedish Language, Cultural Perspectives on Health, Scandinavian History, Evolutionary Psychology, Violence Gender and Culture, Politics in The Middle East, History of the Holocaust
MY IMAGES
CosMo 2022
DSSG 2019
Data Fest 2019
DSSG Project Team
Data Fest 2019
Data Fest 2019
Data Fest 2019
Data Fest 2019
World Personality Conference 2018