CV

Welcome to my CV page. Here, you will find a comprehensive overview of my academic and professional achievements, as well as my skills and qualifications in the field of computer science.

Education

• BSc in Computer Science, Delft University of Technology, Intelligent Data Analysis Track, 2020
• MSc in Computer Science, Delft University of Technology, Artifial Intelligence Track, 2023 (thesis grade 9.5 / 10)
• PhD in Computer Science, Delft University of Technology, Artificial Intelligence, 2023 - current (ongoing)

Teaching

• As a result of pursuing an educational minor during my Bachelor’s degree, I am a certified second degree high school mathematics teacher.
• I am head TA for the course CS4240 Deep Learning edition 2023 - 2024.
• I am head TA for the course DSAIT4005 Machine and Deep Learning 2024 - 2025.
• I gave lecture on Vision Transformers as part of TU Delft Course CS4245 Seminar Computer Vision by Deep Learning.

• I gave lecture on Vision Transformers as part of TU Delft Course DSAIT4125 Computer Vision (2024/25 Q3).

• Graduated MSc students:
  • Paul Frolke: Diffusion techniques for Optical Flow
  • Pascal Benschop: When is depth useful for image segmentation?
  • Joost Luijmes: Processing Local Implicit Neural Representations
  • Thomas Streefkerk: Optical Flow Out-Of-Frame Objects
• Currently supervising MSc students
  • Byran He: Implicit CNN’s
  • Alexandra Neagu: Implicit Depth
  • Petter Reijalt: Optical Flow Evaluations

Publications

Optical Flow Upsamplers Ignore Details: Neighborhood Attention Transformers for Convex Upsampling

A.S. Gielisse. (2023). Master Thesis. TU Delft Repository.

Color Equivariant Convolutional Networks

arXiv:2310.19368 Attila Lengyel, Ombretta Strafforello, Robert-Jan Bruintjes, Alexander Gielisse, Jan van Gemert

Local Attention Transformers for High-Detail Optical Flow Upsampling

Gielisse, Alexander, Nergis Tömen, and Jan van Gemert. Local Attention Transformers for High-Detail Optical Flow Upsampling. arXiv preprint arXiv:2412.06439 (2024).

ARC: Anchored Representation Clouds for High-Resolution INR Classification

Luijmes, Joost, et al. "ARC: Anchored Representation Clouds for High-Resolution INR Classification." arXiv preprint arXiv:2503.15156 (2025).

End-to-End Implicit Neural Representations for Classification

Gielisse, Alexander, and Jan van Gemert. "End-to-End Implicit Neural Representations for Classification." arXiv preprint arXiv:2503.18123 (2025).