Latest News


  • [September 2022] Marcel Worring received a grant (€1.5M) from NWO about the project of "AI4Intelligence: from Multimodal Data to Trustworthy Evidence in Court". More information here.

  • [September 2022] Our group has moved from Science Park 904 to the new LAB42 building nearby (Science Park 900).

  • [September 2022] Carlo Bretti will join the group as a PhD student in the AI4FILM project.

  • [September 2022] Nanne van Noord received a grant (€387K) from ClickNL about the AI4FILM project to develop novel AI techniques that are tailored to film by learning from analysis, production practice, and theory.

  • [September 2022] Yen-Chia Hsu was invited to participate in the 2022 Heidelberg Laureate Forum.

  • [September 2022] Yen-Chia Hsu was invited to a panel discussion at the 2022 Internet Governance Forum, where he will participate in the discussion of AI transparency and communities.

  • [September 2022] Ivona Najdenkoska published an article "Uncertainty-aware report generation for chest X-rays by variational topic inference" in the Medical Image Analysis journal.

  • [August 2022] Website Launch.

Vacancies


  • [Application Deadline 1 October 2022] PhD position AI4Forensics: Forensic Detection and Understanding of Activities in Heterogenous Data

Team


Alumni


Paticipation in ICAI Labs


MultiX participates in the following labs of the Innovation Center for Artificial Intelligence:

  • The AIM Lab (AI for Medical Imaging) is a collaborative initiative of the Inception Institute of Artificial Intelligence from the United Arab Emirates and the University of Amsterdam. It focuses on using artificial intelligence for medical image recognition.

  • The National Police Lab AI is a collaborative initiative of the Dutch Police, Utrecht University, University of Amsterdam, and TU Delft. They aim to develop state-of-the-art AI techniques to improve safety in the Netherlands in a socially, legally, and ethically responsible way.

List of Funded Grants


AI4Intelligence: from Multimodal Data to Trustworthy Evidence in Court

  • Primarily funded by NWO and co-funded by others (€1.5M), 09/2022 - 09/2028
  • Law enforcement has to process huge amounts of data derived from online platforms, digital marketplaces, or communication services, where AI can serve as a solution. In AI4Intelligence we allow AI tool development, the use of these tools by investigators, and legal regulations to go hand in hand so that investigations can lead to trustworthy evidence that is admissible in court.
  • Partners: Nationale Politie, NFI, Sustainable Rescue Foundation, TNO, Microsoft, SynerScope BV, CFLW Cyber Strategies, DuckDuck0Goose, ZiuZ Forensics BV, BG.legal, The Hague Court of Appeal, Innovation Team Testlab OM
  • Link to the News

AI4FILM

  • Funded by ClickNL (€387K), 09/2022 - 09/2026
  • With this project, we aim to develop novel AI techniques that are tailored to film by learning from analysis, production practice, and theory.
  • Partners: Kaspar

VisXP: Interactive Visual Exploration of Media Archives

  • Funded by ClickNL (€391K), 02/2021 - 12/2023
  • This project aims to make AI technology widely applicable within media archives based on interactive learning interfaces that enable users to explore the data visually. This requires research into combining the different types of data sources within archives, both for analysis and for displaying and visualizing with an interface.
  • Partners: The Netherlands Institute for Sound & Vision, RTL Nederland
  • Project Link

List of Demos


TindART: A Personal Visual Arts Recommender
TindART is a web-based visual artwork reccomendation system. The system has visual analytics controls that allow users to gain a deeper understanding of their art taste and refine their personal recommendation. (website link, paper link)

OmniArt: A Large-scale Artistic Benchmark
OmniArt is a large scale artistic benchmark dataset aggregated from multiple collections around the world. It is designed for easy data handling and fast integration with popular deep learning frameworks. (website link, paper link)

GCNIllustrator: Illustrating the Effect of Hyperparameters on Graph Convolutional Networks
GCNIllustrator is a visual analytics tool for illustrating the effect of hyperparameters on graph convolutional networks (GCNs). It addresses one of the most tedious steps in training GCNs: the choice of hyperparameters and their influence on performance. (video demo and paper link)