PhD defence Una Kelly | Vulnerability of face recognition to morphing: A latent space perspectiveUna Kelly is a PhD candidate in the Department of Data Management & Biometrics. Promotors are prof.dr.ir. R.N.J. Veldhuis and dr.ir. L.J. Spreeuwers from the Faculty of Electrical Engineering, Mathematics and Computer Science.Read more
Women in Computer VisionEstefanía Talavera co-organized the 13th edition of the Women in Computer Vision (WiCV) workshop, held as part of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). This workshop aims to increase the visibility of women researchers in computer vision, provide opportunities for junior researchers to present their work and travel awards, as well as share experiences and professional advice.Read more
PhD defence Una Kelly | Vulnerability of face recognition to morphing: A latent space perspective
Women in Computer Vision
Kevin Iskandar, succesfully defended his Master thesis.AI Musician, Do You Have a Place in this World? An Empirical Study
Best Student Paper Award – SITB 24 Symposium
PhD defence Zahra Atashgahi | Advancing Efficiency in neural networks through sparsity and feature selection
Noor Mansour, succesfully defended his Master thesis. Facilitating Industrial B2B e-Auctions through Multi-Agent and Retrieval Augmented Large Language Models
Kes Olga Greuter, succesfully defended her Master thesis. A Repository for Testing Compliance to the Internet of Things (IoT) Security Standards
DMB Collaborates with Police Academy, Netherlands Forensic Institute and Saxion on
Muthu Priyadharshini Shanmuganathan, succesfully defended her Master thesis. A strategic Decision Analysis Framework for Software-as-a-Service Selection
Marjolein Bolten, succesfully defended her Master thesis. Monitoring training load and identifying fatigue in young elite speed skaters using machine learning methods
Jordy Weening, succesfully defended his Master thesis. Classifying Companies Based on Textual Webpage Data
Chin Ying Lin, succesfully defended her Master thesis. Enhancing Cancer Treatment Planning: A Combined Approach of Process Mining and Machine Learning with a Focus on Colorectal Cancer (CRC)