PhD Candidate @ Utrecht University
Research Data Scientist @ ASReview
I am a PhD candidate at Utrecht University, where I am combining applied data science, open science, AI, and behavioral science in various projects under the supervision of Rens van de Schoot and Lars Tummers. Prior to starting my PhD, I worked as a researcher for the open-source software ASReview, where I remain a maintainer and developer. I have also worked as a teaching assistant for the University's Summer School, teaching the course Applied Text Mining. In my Master's thesis, I explored the implementation and use of complex neural networks as text classification models for open-source software. I have presented at conferences and festivals including Oxford University, the Summit AI and Predictive Health, the Netherlands National Open Science Festival, and the Data Science & Artificial Intelligence for Society Day at Utrecht University. I have received the Hofvijverkring Fellowship Grant and am proficient in Python programming. ~ GPT3.5 after reading this website.
A presentation concerning the use of Machine learning in systematic reviews and the importance of correct use of Datasets.
A presentation considering inclusive training datasets for text mining purposes. Using three use-cases, (ASReview, AI translating tools, OpenAlex) the presenters demonstrated the importance of high quality training data for text mining purposes. The discussion focused on the importance of inclusive training datasets covering more than just the western world.
A presentation considering the use of hybrid models and a new dataset benchmarking platform (ODSS).
At the Data Science & AI for Society Day you will get insights into the research and collaborations of your colleagues, which will inspire you for your own research. You will also learn more about the three focus areas, and you will be able to explore the options for cooperation, support, and funding. The focus areas have calls for proposals to be awarded seed money funding for new research initiatives every year.
Hundreds of young researchers at Utrecht University are working on innovative scientific research to help solve social issues. Issues include climate change, infectious diseases, ageing, social cohesion and security. The Hofvijverkring supports this new generation of scientists with a special scholarship programme. The aim is to select five researchers each year for an extra financial boost to enter international collaboration and broaden their international network.
I am the first Ph.D. candidate following the Applied Data Science Master's at Universiteit Utrecht. I will combine applied data science, open science, AI, and behavioral science in many different projects for the next four years with Rens van de Schoot and Lars Tummers as doctoral supervisors and Ayoub Bagheri and Matthieu Brinkhuis as co-supervisors.
In the time between finishing my Master's and starting my Ph.D., I worked as a researcher for ASReview. With this project I would later start my PhD. Now, remain a maintainer and developer the project.
During the Summer of 2021, I worked for the University's Summer School as a teaching assistant for the course Applied Text Mining. The course teaches the basic and advanced concepts and ideas in text mining and natural language processing.
In this project, I joined a Utrecht University-based research team in exploring the implementation and use of complex neural networks as text classification models for open-source software used by tens of thousands of researchers worldwide.
Deep learning is often used in text classification tasks for its efficiency and proficiency in modelling nonlinear processes. However, using this type of machine learning takes more time and processing power than using shallow learning algorithms. This study aims to determine if a combination of shallow and deep learning techniques can increase the classification performance of automated systematic review software. Since deep learning networks excel at finding difficult connections, this study aims to find situations in which they outperform shallow networks. To find these situations, simulations were run on a prepared dataset using different classifiers, switching from shallow to deep networks. While at first results showed no improvement, continued exploratory research resulted in support for the hypothesis, as situations were found in which the neural network outperforms the shallow network classifier.
The thesis can be HERE, and the code accompanying my thesis can be found here:
This year, Charles Meijer and I scored the 32th spot out of the 430 participating teams for the 2020 version of the annual Christmas Puzzle made by the Dutch General Intelligence and Security Service.
My research thesis on Process Mining, Architecture Mining and Visualisation as part of my Bachelor's degree.
The process mining technique contains powerful tools to extract processes from big event logs. However, real world applications are more likely to produce snapshots of the process over only a smaller period, keeping the absolute process a secret. This research supplies a framework that can create a controlled environment from anagram absolute event log in which these snapshots, called samples, can be mined and analyzed, while having full control over the degree of variation in sampling. Having the absolute process and the samples derived from it provides a new perspective on the relations between sampling rates and techniques, and the assessments of the samples created by them. The framework is created using readily available tools such as ProM and RapidMiner. The sampler techniques used are derived from the ''Measuring the Behavioral Quality of Log Sampling''. The result is a flexible framework, split up into 3 components. Each component, i.e. the Sampler, Miner, and Analyzer, applies algorithms related to sampling and process mining to transform the original log into samples, models and measurements, ready to used for further research.
Apple Authorised Service Provider as a parttime occupation next to my study. I stopped here in 2021 after moving to ASReview as Researcher
During my Bachelor's, I spent a year rowing for ORCA's Men's eight team, following an intense training and dietary schedule.
Data are everywhere. In today’s world, it is becoming more and more important to put these data effectively and safely to use. The new Master’s programme Applied Data Science enables you to become a data science professional with excellent analytic capabilities.
You are able to apply these capabilities across a wide number of domains, such as health science, geo science, social and behavioural science and media studies. The programme is taught in English and was set up in co-creation with the industry. This Master’s programme is intended for students who have a clear and ambitious interest in Applied Data Science. Applicants can come from a wide range of fields. You must have an academic understanding of statistics and programming.
Information Science offers an interesting mix of psychology, communication studies, organisational studies and computing sciences. Through this combination of aspects students will be uniquely qualified to build bridges between people and technology, and between the needs of companies and IT solutions.
My research thesis is on Process Mining, Architecture Mining and Visualisation as part of this degree. Subjects I enjoyed during this Bachelor were Data Analytics, Information systems and Architecture modelling.
For my pre-Uni education I chose for the Nature and Technology branch. The curriculum I picked was a course in Latin and Music history and theory.