Education

My education was an exciting, challenging adventure, in which I learnt many skills that have helped me develop professionally as well as personally.

2023 - 2025 - Master Data Science and Technology - University of Twente

  • Master Thesis:
    • OOD detection in medical imagery using Vision Language Models – Master Thesis
      Research determining the applicability of CLIP based Vision Language Models and whether they can accurately detect data that deviates largely from their training dataset, specifically in biomedical images, such as X-rays and CT-scans, and internal body imagery.
    • Supervisors: Dr. Alexia Briassouli and Dr. Faizan Ahmed
    • The paper can be found here.

  • Courses:
    • During my specialisation of Computer Science, I have gotten acquainted with several different approaches of Machine Learning, Artificial Intelligence and data processing techniques. I have completed several projects in teams as well as solo, which are listed here. And, more importantly, I have also excelled in courses surrounding the ethics concerned with the use of computers, AI and Data. Mainly, the subjects in this Master included several different aspects and domains in which data is integral and where machine learning and AI can be applied readily. Below is a brief list to illustrate my technical skillset:

    1. Data Science: A general course in which any of the university’s students can get acquainted with the many aspects of Data Science as a research field. In particular, I followed 4 tracks: Data Processing and Visualisation, Data Mining, Data Integration and Geospatial Information Systems (GIS)
    2. Machine Learning I and II: A set of courses designed to teach students the basics surrounding Machine Learning and AI (such as Neural Networks, Classification and Regression, and different model types), and allowing students to apply their knowledge to more advanced forms of machine learning (Deep Learning, Reinforcement Learning, Large-Language Models).
    3. Probabilistic Programming: A course on a special paradigm of coding aimed at simulating random events, with a specific submodule dedicated to working with probabilistic databases.
    4. Research Experiments in Databases and Information Retrieval: A course focussed on teaching the process of setting up and carrying out experiments properly in the context of databases or information retrieval (subdomains of Data Science). As part of this course, I published a paper with a specific focus on Data Imputation, the publication of which can be found here.
    5. Managing Big Data: A course all about learning how to use Big Data and how to keep code that uses it performant. When we talk “Big Data”, we do not mean several hundred gigabytes, but rather data so large that it is too large to deal with normally. Therefore, students also have to learnt how to work with (Hadoop) Clusters and PySpark.
    6. Information Theory and Statistics: A much more mathematically intensive course, aimed at teaching the foundations of the statistics and mathematics surrounding Data Science, as well as give students insights into the field of information theory (such as encoding/decoding, noisy signals, etc.).
    7. Image Processing and Computer Vision: A hands-on course in which students learn how to process images and videos in such a way that useful information can be extracted from or added to them. Examples include: creating colour masks of images, sharpening or blurring images to enhance algorithms or acquiring the lines of a tennis court to paste virtual advertisements in a convincing way.
    8. Speech Processing: A hands-on course aimed at teaching students the working of the human voice with a specific focus in how one can use that information to infer emotion, heritage or other information from human speech through automation with a computer. Additionally, time was spent to introduce the concept of artificial voice production (such as Text-To-Speech and (AI) Voice Deep-Fakes).
    9. Internship: I did a research internship at Yamagata University between the 30th of April and the 4th of August 2024. Details of the project can be found on the My Projects page.

2019 - 2023 - Bachelor Computer Science - University of Twente

  • Design Project:
  • DumpingMapper – Detection and Classification of Illegal Dumping Using Deep Learning and Computer Vision
    • Using a mix of CNNs for detection and classification of several types of illegally dumped trash, an impressively performant dumping detection model was created, accompanied by a dashboard and database. I was mainly responsible for the data acquisition and synthetic data creation using Blendr code.

 

  • Research Project/Bachelor Thesis:
  • Real-time Pitch Detection using a resource constrained IoT Device
    • For my Bachelor’s Thesis, I combined my passion for music with my professional direction, creating a system that uses Machine Learning to identify notes, chords and intervals in pieces of audio produced using different musical instruments. The main challenge was to make this system functional on a so-called resource constrained Internet of Things (IoT) Device. In my case, a mobile phone was used.
    • The link to the thesis can be found here.

  • Major Courses

    1. Software Systems: A course on the implementation and design of several different software systems and what parts they consist of. For example, students designed a software system for an automated car parking payment system based on a use-case, and for a different project, implemented an online multiplayer version of the boardgame Abalone.
    2. Discrete Structures And Efficient Algorithms: A more theoretical set of courses where the complexity of algorithms and the design of more complicated optimisations for common problems in Computer Science play a prevalent role. The project consisted of tackling the complicated problem of finding graph isomorphisms in an efficient manner.
    3. Several General Mathematics Courses: Calculus 1A and 1B, Linear Algebra, Abstract Algebra, Discrete Mathematics and Probability Theory and Statistics.
    4. Ethics courses: IT & Law, Ethics.
    5. Academic Skills: A module teaching students how to behave according to academic conduct, with an important emphasis on plagiarism, how to avoid committing it and how to deal with it as well.

  • Minor courses and Electives

    1. Web Science: An elective course within Computer Science, with an express emphasis in working with and modelling several applications that work on the world wide web. Mathematically, it is important to know graph theory concepts and modelling interactions between users.
    2. Programming Paradigms: A challenging elective course all about 4 different programming paradigms: Functional Programming in Haskell, Logic Programming in Prolog, Concurrent Programming in R and Compiler Construction in Java (using antlr). All four of these subcourses had a project related to each of them.
    3. Serious Gaming: A proper minor course open to any study at the university, hosted by the Psychology study. It is concerned with the design of pervasive games: games which help people achieve change that they need/want or educational games. Most interestingly, it is important to learn about balancing fun with pervasive goals.

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2012 - 2019 - VWO (atheneum) - Ulenhof College

  • Profile: Natuur & Techniek (Nature and Technology)
  • Notable Courses i.a:. Computer Science, Mathematics B, Physics, Chemistry, Music
  • Research Project: The evolution of musical chords.