Mariya Hendriksen

I am a Research Fellow at the University of Oxford, working with ʻŌiwi Parker Jones and Philip Torr. My work focuses on multimodal machine learning for applications in neuroscience.

I completed my PhD in Artificial Intelligence at the University of Amsterdam, advised by Maarten de Rijke and Paul Groth. I also hold an MSc in AI from KU Leuven and a BSc in Computational Linguistics from Novosibirsk State University.

During my academic journey, I interned at Microsoft Research (Cambridge), the Google Gemini team, Bloomberg AI, Amazon Alexa, LIIR at KU Leuven, and ETH Zurich.

Alongside my research, I am committed to fostering diverse and inclusive research communities. As such I organize the WiML Mentorship Program, served as the General Chair for the WiML at ICML 2025, and mentored through the Inclusive AI initiative.

Email  /  Scholar  /  GitHub  /  Bsky  /  X

profile photo

News

Publications

Milestones & Activities

  • (Sep 2025) Started as a Research Fellow at the University of Oxford, working with Philip Torr and ʻŌiwi Parker Jones.
  • (Apr 2025) Serving as the General Chair for the Women in Machine Learning symposium at ICML 2025.
  • (Sep 2024) Started a research internship at Microsoft Research Cambridge on the Game Intelligence team.
  • (Apr 2024) Started a research internship at Google Zurich on the Gemini team.
  • (Jul 2023) Started a research internship at Bloomberg AI in London on the Question Answering team.

Research

BarPlotsUniverse Adapting Vision-Language Models for Evaluating World Models.
Mariya Hendriksen, Tabish Rashid, David Bignell, Raluca Georgescu, Abdelhak Lemkhenter, Katja Hoffman, Sam Devlin*, Sarah Parisot*
Under Submission, 2025

We address the challenge of automated evaluation for world model rollouts by introducing a structured protocol and UNIVERSE, a method for adapting vision-language models through unified fine-tuning to assess temporal and semantic fidelity.

VennDiagram Demonstrating and Reducing Shortcuts in Vision-Language Representation Learning.
Maurits Bleeker*, Mariya Hendriksen*, Andrew Yates, Maarten de Rijke (co-first author)
TMLR, 2024
arXiv / bibtex / Github

We propose a framework to examine the shortcut learning problem in the context of Vision-Language contrastive representation learning with multiple captions per image. We show how this problem can be partially mitigated using a form of text reconstruction and implicit feature modification.

VennDiagram Multimodal Learned Sparse Retrieval with Probabilistic Expansion Control.
Thong Nguyen*, Mariya Hendriksen*, Andrew Yates, Maarten de Rijke (co-first author)
ECIR, 2024
arXiv / Github

We propose a framework for multimodal learned sparse retrieval.

VennDiagram Scene-Centric vs. Object-Centric Image-Text Cross-Modal Retrieval: A Reproducibility Study.
Mariya Hendriksen, Svitlana Vakulenko, Ernst Kuiper, Maarten de Rijke
ECIR, 2023
arXiv / Github

VennDiagram Extending CLIP for Category-to-Image Retrieval in E-commerce.
Mariya Hendriksen, Maurits Bleeker, Svitlana Vakulenko, Nanne van Noord, Maarten de Rijke
ECIR, 2022
arXiv / Github

VennDiagram Analyzing and Predicting Purchase Intent in E-commerce: Anonymous vs. Identified Customers.
Mariya Hendriksen, Ernst Kuiper, Pim Nauts, Sebastian Schelter, Maarten de Rijke
SIGIR eCom, 2020
arXiv


Build upon Jon Barron's template.