Hello! 👋

I'm Tim J. Schoonbeek

PhD Student in Computer Vision & Machine Learning

About Me

I am Tim, a PhD student at ASML and TU/e graduating in Nov 2025. During my PhD, I develop multimodal AI for computer vision and augmented reality in industrial settings. My work spans scalable video understanding, defect detection, and human–AI collaboration. I combine industrial research experience at Microsoft, ASML, and Honda with a strong publication record and patents, translating cutting-edge AI into practical solutions.

Outside the lab, I am usually on (or tinkering with) a motorcycle, traveling, or learning Portuguese.

Computer Vision Machine Learning AI for Manufacturing Augmented Reality Human-Computer Interaction Video Understanding Semi-Supervised Learning Domain Adaptation Imbalanced Classification Foundation Models

Experience

ASML Research, Eindhoven logo

PhD Researcher

ASML Research, Eindhoven

Sept. 2021 - Nov. 2025
  • PhD project on automatic extraction of relevant and meaningful information from procedural actions within an industrial setting
  • Filed patents, published peer-reviewed articles, and contributed to the ASML Technology Conference, the world's largest developer event
  • Held lectures on efficient video recognition and supervised three graduate students' thesis projects
Microsoft Research, Redmond logo

Research Intern

Microsoft Research, Redmond

June 2025 - Sept. 2025
  • Research group: Interactive Multimodal AI Systems
  • Developing real-time multimodal systems with LLM-powered perception to create benchmarks for situated intelligence for task assistance
  • Building models of social cues (turn-taking, engagement, attention) from large datasets while collaborating with cross-disciplinary teams
Honda Research Institute Europe GmbH, Frankfurt logo

Internship

Honda Research Institute Europe GmbH, Frankfurt

Mar. 2020 - July 2020
  • Interaction-Aware Trajectory Prediction Using Graph Neural Networks
  • Researched spatio-temporal graph neural networks for trajectory prediction of surrounding vehicles in highway driving scenarios
Automotive Technology InMotion, Helmond logo

Race Engineer - Part-time (Student Team)

Automotive Technology InMotion, Helmond

Sept. 2018 - Sept. 2019
  • Performed and optimized drive cycle simulations for an electric endurance racing car

Education

Eindhoven University of Technology, Eindhoven logo

PhD

Eindhoven University of Technology, Eindhoven

Sept. 2021 - Nov. 2025
  • Title: Advanced Augmented Reality Solutions for AI-based Servitization
  • Electrical Engineering Faculty
  • Doctoral project funded by ASML on industrial procedure understanding using computer vision and machine learning algorithms
University of Cambridge, Cambridge logo

Visiting Researcher

University of Cambridge, Cambridge

Oct. 2024 - April 2025
  • Conducted research on enhancing human-robot interaction, focusing on AI systems to assist industrial operators in procedure execution
  • Designed computer vision algorithms for quality inspection and AR support systems; conducted user studies with AR glasses
  • Reached the finals (top 2.5%) in the OxBridge AI Startup Challenge, a competition between students from Cambridge and Oxford
International Summer School, Matera logo

Extended Reality and Artificial Intelligence

International Summer School, Matera

July 2023
  • Completed courses on the intersection of AI & extended reality (XR) from Prof. Rita Cucchiara and Prof. Joaquim Jorge, amongst others
  • Pro-actively took up the role as a link between the programmers and designers in a multidisciplinary project, besides my technical roles
  • Won the best project award for our work on an XR app for engagement with cultural heritage
Eindhoven University of Technology, Eindhoven logo

M.Sc. in Automotive Engineering

Eindhoven University of Technology, Eindhoven

Feb. 2019 - May 2021
  • Specialization: Mobile Perception Systems
  • Earned the cum laude honours distinction for a 4.0 GPA (8.5/10)
  • Master thesis 'Learning to Predict Collision Risk from Simulated Optical Flow' awarded a 9.0/10.0
  • Published paper, singled out among the top 5% of submissions for oral presentation at the 2022 IEEE Intelligent Vehicles Symposium
Eindhoven University of Technology, Eindhoven logo

B.Sc. in Electrical Engineering

Eindhoven University of Technology, Eindhoven

Sept. 2015 - Feb. 2019
  • Automotive Track
  • Bachelor thesis on depth estimation from disparity and segmented images awarded a 8.5/10

Publications

Patents

IP1

Robust Defect Identification and Segmentation with no need for labeled data

Tiago Botari, Tim J. Schoonbeek, Dan Lehman, Alexandru Onose and Jan Jitse Venselaar

EP Patent Filing 24223558.8. 2024

Patent filed for robust defect identification and segmentation techniques that do not require labeled training data, enabling more scalable and cost-effective quality control in manufacturing.

Defect DetectionSemi-Supervised LearningIndustrial AI
IP2

Contrastive deep learning for SEM defect inspection

Daniel Faatz, Tiago Botari and Tim J. Schoonbeek

EP Patent Filing 23211337.3 and WO Patent Filing PCT/EP2024/080483. 2023

Patent filed for contrastive deep learning approaches applied to scanning electron microscopy (SEM) defect inspection, improving accuracy and efficiency in semiconductor manufacturing quality control.

SEM InspectionContrastive LearningSemiconductor Manufacturing