Human Data Science (HDS)


Dr. Daniel Oberski

Daniel Oberski holds a joint appointment as professor of data science methodology at Utrecht University, department of Methodology & Statistics, and at the University Medical Center Utrecht (UMCU), department of Biostatistics. His work focuses on latent variable modeling and data science applications in the social, behavioral, and biomedical sciences. He leads the social data science team at the national research infrastructure for the social sciences in the Netherlands, ODISSEI. He is also lead data scientist of UMCU’s “digital health” program, which works to implement data science in clinical care at the hospital.

For more see his Utrecht University page, LinkedIn and Twitter.

Dr. Laura Boeschoten
Assistant Professor

Laura Boeschoten is an assistant professor who specializes in the fields of measurement errors, missing data, and latent variables. Her current focus lies in extracting valuable data from social media platforms to drive research advancements. As the project lead of the D31 Team, she is deeply committed to developing a user-friendly Digital Data Donation platform, catering to researchers engaged in data donation studies. Through her work, she tackles intricate methodological challenges and strives to design intuitive workflows that benefit both researchers and participants.

For more see her Utrecht University page, LinkedIn and Twitter.

Dr. Erik-Jan van Kesteren
Assistant Professor

Erik-Jan van Kesteren is an assistant professor of applied data science, and researches how to incorporate high-dimensional data into structural equation models. He also works as a part-time programmer at JASP, where he develops easy-to-use interfaces for the wide array of latent variable models available within structural equation modeling.

For more see his Utrecht University pageGithub, LinkedIn and Twitter.

Dr. Ayoub Bagheri
Assistant Professor

Ayoub Bagheri is an assistant professor of applied data science at Utrecht University. His PhD-dissertation unites machine learning, text mining and applied data science. His interdisciplinary research contributes to designing decision support systems based on statistical learning algorithms, and to leverage machine learning and text mining methods in application to social sciences and healthcare.

For more see his Utrecht University page, Github, LinkedIn and Twitter.

Dr. Anastasia Giachanou
Assistant Professor

Anastasia Giachanou is an assistant professor with an interest in natural language processing, social media analysis and computational social science. Her research interests include fake news detection, bot detection, fact checking, and sentiment analysis. She received her Ph.D. from the Faculty of Informatics at the University of Lugano, in Switzerland, working on the topic of tracking sentiment over time.

For more see her Utrecht University pagepersonal website and LinkedIn.

Dr. Javier Garcia-Bernardo
Assistant Professor

Javier Garcia-Bernardo is an assistant professor of social data science. He applies computational models to understand social and economical systems. He is particularly interested in how interactions between agents create and reflect socioeconomical inequalities. Garcia-Bernardo completed his PhD in Political Economy at the CORPNET group (University of Amsterdam), and his MSc in Computer Science at the University of Vermont.

For more see his Utrecht University page and LinkedIn.

Dr. Mahdi Shafiee Kamalabad
Assistant Professor

Mahdi Shafiee Kamalabad is an assistant professor of applied data science at Utrecht University. His research interests lies primarily in developing new statistical and machine learning methods for analyzing complex data of complex systems. He has developed various novel models such as (non-homogeneous) Dynamic Bayesian Network Models, and Change point Relational event Model which are flexible enough to analyze various types of time-series data that routinely arise in a variety of disciplines, such as Social and Behavioral Sciences, and Life Sciences. As a Postdoc researcher, Mahdi worked on Modelling of Dynamic Social Networks at Tilburg School of Social and Behavioral Sciences. He received his Ph.D. from the University of Groningen in Applied Statistics.

For more see his Utrecht University page and LinkedIn.

Qixiang Fang
PhD Candidate

Qixiang Fang is a PhD candidate under the supervision of Dr. Daniel Oberski and Dr. Dong Nguyen. His research falls at the intersection of statistics, natural language processing and machine learning, and focuses on improving measurement and causal inference with high-dimensional text data.

For more see his Utrecht University page, personal website, LinkedIn and Twitter.

Mehran Moazeni
PhD Candidate

Mehran Moazeni is a PhD candidate under supervision of Daniel Oberski and Dr. Emmeke Aarts. His research interests are summarised in applying Machine Learning algorithms alongside Statistical models in the Healthcare domain. Other research interests are pattern recognition, dynamic monitoring, prediction, and latent variable models. Previously he received his master’s in Socioeconomic system analysis, and his thesis was to detect and monitor brain tumours.

For more see his Utrecht University page and LinkedIn.

Dr. Stephanie Eckman
Visiting Researcher
Stephanie Eckman is a Fellow at RTI International conducting research into data quality and the social construction of data. She has a PhD from the Joint Program in Survey Methodology. She visited M&S in Spring 2022.


For more see her personal website and LinkedIn.

Thijs Carrière
Junior Researcher

Thijs Cornelis Carrière finished his Masters in Methodology and Statistics at Utrecht University in 2022. His master thesis focused on using response times as explanation factors for differential item functioning and was part of an internship at Cito, an institute for educational testing. His research interests are on psychometrics, SEM, and use for digital para-data.

For more see his personal website, LinkedIn and Github.

Thom Volker
PhD Candidate

Thom Volker is a PhD candidate researching different techniques for creating privacy-preserving synthetic data sets, under the supervision of Dr. Erik-Jan van Kesteren, Dr. Peter-Paul de Wolf and Prof. dr. Stef van Buuren. He aims to work at the intersection of social-scientific research and cutting-edge statistical techniques, to get the most out of expensively collected research data. His other research interests include multiple imputation for missing data, hypothesis evaluation using Bayes factors and research synthesis.

For more see his Utrecht University page, personal website, Twitter, GitHub and LinkedIn.

Elena Candellone
PhD Candidate
Elena Candellone, a PhD candidate supervised by Dr. Javier Garcia Bernardo, Dr. Peter Gerbrands, Dr. Mahdi Shafiee Kalamabad, and Prof. dr. Daniel Oberski, focuses her research on the self-organization of groups on social networks. With an MSc degree in Physics of Complex Systems from Politecnico di Torino and Université Paris Cité, as well as a BSc degree in Physics from Università degli Studi di Torino, her interests lie in understanding opinion formation and polarization on online social media and identifying patterns in economic crime networks.

For more see her Utrecht University page, personal website, Twitter, GitHub and LinkedIn.

Hadi Mohammadi
PhD Candidate
Hadi Mohammadi is a Ph.D. candidate under the supervision of Dr. Ayoub Bagheri and Dr. Anastasia Giachanou and the promotion of Prof Daniel Oberski. His research interests are Explainability in Natural language processing (NLP), especially Large Language Models (LLMs) and their applications in social science. Other research interests are Deep and Reinforcement Learning, Fairness in ML, Human Rationalizations, and Data-Driven Decision Science. Previously he received his master’s in industrial engineering – Macro Systems, and his thesis was about Statistical Reinforcement Learning with Application in a Dynamic Pricing Problem.

For more see her Utrecht University page, personal website, GitHub and LinkedIn.

Mohammad Behbahani
PhD Candidate
Mohammad Behbahani is a PhD candidate in applied data science methodology at Utrecht University, specifically in the department of Methodology & Statistics. His research primarily focuses on enhancing statistical and machine learning methods for Social Network analysis. He is working under the guidance of Dr. Mahdi Shafiee Kamalabad, and Dr. Emmeke Aarts. Mohammad’s research interests encompass various areas, including Network Science, Relational Event Model, and Hidden Markov Model. Through his work, he aims to advance the understanding and application of these methods in analyzing complex social networks.

For more see his Utrecht University page, personal website, LinkedIn, and GitHub.

Daniel Anadria
PhD Candidate
Daniel Anadria is a PhD candidate researching ways to implement fair and transparent automated decision-making systems in healthcare. His research focuses on natural language processing systems for clinical decision-making. His academic background is in psychology, methodology and statistics for behavioral, biomedical, and social sciences, as well as applied data science. Daniel Anadria is supervised by Dr. Anastasia Giachanou and Prof. dr. Daniel Oberski.

For more see his personal website and GitHub.

Sebastian Mildiner Moraga
PhD Candidate
Sebastian is a PhD candidate under the supervision of Dr. Emmke Aarts. The main goal of his PhD project is to develop methodological tools that enable applied researchers to effectively utilize the wealth of information contained in intense multilevel longitudinal (time-series) data. Specifically, he focuses on extending Bayesian multilevel hidden Markov models to make them a better fit to model social and behavioural dynamics. Prior to this, he completed a MSc in methods and statistics at Utrecht University, and a BSc in biological sciences at University of Buenos Aires.

For more see his Utrecht University page, LinkedIn, and GitHub.

Dr. Apollinaire Batoure
Visiting Researcher

Apollinaire Batoure is a senior lecturer in Applied Mathematics at University of Ngaoundere, Cameroon. He has a PhD in Decision Support System from University of Ngaoundere. His Postdoc at Utrecht University is based on Fundamentals and Applied Data Science. His research focuses on Time Series Forecasting for Epidemic Preparedness.

For more see his Utrecht University page.

Sofia Chelmi
Erasmus+ intern

Sofia Chelmi is a Erasmus+ intern working for ODISSEI Social Data Science Team. She completed her bachelor’s degree in Mathematics from the University of Crete. Her research project concerns analyzing co-voting networks in order to detect organized groups in social media.

For more see her Utrecht University page.