Meet The Team

WHO: I am Stéphanie van den Berg and I am an associate professor in the area of research methodology and data analytics. I have a PhD in psychology. My research interests are in the field of machine learning, psychometrics, Bayesian statistics and modelling time-intensive data. I teach courses in data science and statistics at the University of Twente.

ROLE: I am the head of BDSI.

NEED HELP: I can help you with statistical modelling, learning and using R, applying statistical/machine learning, power analysis, measuring behavioural traits, discussing the research methodology of your project and writing applications for research grants. I also have extensive experience with quantitative genetic models and Bayesian data analysis.

WHO: I am Anna. My current focus is text analysis with deep learning models. I have worked for several years as data scientist in market research, but my background is in physics, where I got a PhD in statistical physics/network science. In addition to natural language processing I’m also interested in explainable AI and causal modeling.

ROLE: I’m a data scientist at BDSI.

NEED HELP: I can help you with Deep Learning, Machine Learning and Big Data Analysis in python and with data scraping. Just ask me anything and I’ll do my best to help.

WHO: I’m a computational statistician and data scientist at BDSI. I expect to obtain a PhD on adaptive learning environments in the coming months. I have experience with adaptive testing, large scale simulations, natural language processing and adaptive learner models. I’ve also authored several R packages, and have experience with Python, C# and JavaScript/TypeScript. I’m excited about the prospects of computational statistics, statistical learning and machine learning for BMS, and would love to help you leverage these technologies for your own research.

ROLE: I am a data scientist at BDSI.

NEED HELP: I can help you with data scraping, data tidying, interactive visualizations (e.g. d3.js, shiny), designing and performing simulations, natural language processing, statistical learning and machine learning. If you’re at all interested in big data or machine learning, but don’t know what they could add to your research, or how to implement them, please do contact me. I’d be happy to discuss with you how these techniques might be relevant for your research.

WHO: I am Abhishta. I make use of empirical data to help businesses understand the need for investment in cyber security. I have developed methods to measure the economic impact of cyber attacks. This helps me to get a more straight forward view of how cyber attacks may damage the current and future revenues of an organisation.

ROLE: I represent the high-tech business and entrepreneurship (HBE) cluster in BDSI.

NEED HELP: I can help you with implementation of montecarlo simulations, web-scraping, data collection from social media, bigdata (sensor data etc.) analysis and selection of data storage/processing facilities.

WHO: I am Karin Groothuis-Oudshoorn and I am a (bio)statistician, researcher, data scientist. My background is from mathematical statistics. My research interests are in the field of statistical learning, prediction models, missing data and measuring / analyzing preferences. I teach courses in data science and statistical learning at the University of Twente.

ROLE: I represent the technology, policy and society (TPS) group in BDSI.

NEED HELP: I can help you with statistical modelling, learning and using R, applying statistical/machine learning, discussing the statistical methodology of your project.

WHO: My background is in Human Factors research, which is about fitting technology to the human mind. In my present research I look into how people learn complex motor skills, such as in surgery or driving, in training simulators. Powerful statistical models are essential for my research. My conviction is that what has been called New Statistics makes empirical research more valid, more interesting and much better to report. Much of what I have learned so far, can be read in my book New Statistics for the Design Researcher. In my courses around the year, around 101 Psychology students learn to program in Python or R.

ROLE: I am here to represent LDT (Learning, Data Analytics and Technology) group, which comprises the departments CPE, OMD, ELAN, IST and OWK.

NEED HELP: The best moment to start asking me is before you plan your study. Together, we can look into your research problem and find a consistent and efficient research design and develop a statistical model upfront. Even if you ask me just to do the analysis for you, I will always make sure that you fully understand the process and the results. My favorite statistical approach is Generalized Linear Multi-level Models (GLMM), which is an extremely flexible family. Even if you have never heard of it, chances are good, that a GLMM can translate your precious data into real answers. If you, as a group, need a solution for a specific research situation (or you are just curious about New Statistics), we can organize a workshop or live demo, together.

WHO: I’m a researcher and all-round data and methodology enthusiast. Much of my research is focused on health outcomes analysis and predictive modelling. I also have extensive applied experience in developing and validating tests and questionnaires, using both modern and classic psychometric analysis techniques. I teach courses in assessment and advanced research methods, such as experience sampling (longitudinal intensive measurement). I have experience with different statistical software environments and programs, including R and Mplus.

ROLE: I represent the department of Technology, Human and Institutional Behaviour (HIB) in BDSI.

NEED HELP: I’m happy to help you with questions about the optimal design, statistical analysis and power calculations for your observational or intervention studies. I can also support you with measurement development and evaluation projects and with different types of multilevel and latent variable modelling analyses such as structural equation modelling.