Daniel Oberski holds a joint appointment as associate 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.
Dr. Laura Boeschoten
Laura Boeschoten is an assistant professor with an interest in measurement errors, missing data and latent variables. Her PhD-research from Tilburg University focused on detecting measurement errors in surveys and introduced a method on how to solve these issues. Currently Boeschoten is working on making data downloads from social media possible to use in research.
Dr. Erik-Jan van Kesteren
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.
Dr. Ayoub Bagheri
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.
Dr. Anastasia Giachanou
Anastasia Giachanou is a post-doc researcher 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.
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.
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.
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.