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