Mariya Hendriksen

I am a Research Fellow at the University of Oxford, working with Philip Torr and ʻŌiwi Parker Jones. My broader research interests span multimodal machine learning, with a focus on representation learning, evaluation, and trustworthiness. I am a member of the ELLIS society, an Encode AI for Science Fellow, and an incoming Junior Research Fellow at Kellogg College.

I earned my PhD in Artificial Intelligence at the University of Amsterdam, advised by Maarten de Rijke and Paul Groth and hold an MSc in AI from KU Leuven where I was advised by Marie-Francine Moens. Along the way, I held research positions at Microsoft Research, Google DeepMind (on the Gemini team), Bloomberg AI, Amazon Science, LIIR at KU Leuven, and ETH Zurich.

Alongside my research, I am committed to diversity and inclusion in machine learning: I organize the WiML Mentorship Program, served as General Chair for the WiML at ICML 2025, and mentored through the ELLIS Inclusive AI initiative.

Email / Scholar / GitHub / Bsky / X

Updates

(Mar 2026) Presenting 'Adapting Vision-Language Models for Evaluating World Models' at CoAStaL NLP at the University of Copenhagen.
(Nov 2025) Presenting 'Adapting Vision-Language Models for Evaluating World Models' at NeurIPS LAW 2025 as an Oral.
(Sep 2025) Presenting 'Adapting Vision-Language Models for Evaluating World Models' at CMU as a Spotlight.
(Sep 2025) Presenting 'Image-Seeking Intent Prediction for Cross-Device Product Search' at RecSys Gen AI for E-commerce 2025 as an Oral.
(Aug 2025) Started as a Research Fellow at the University of Oxford, working with Philip Torr and Oiwi Parker Jones.
(Apr 2025) Our paper 'Benchmark Granularity and Model Robustness for Image-Text Retrieval' accepted at SIGIR 2025.
(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 on the Game Intelligence team.
(Jul 2024) Our paper 'Demonstrating and Reducing Shortcuts in Vision-Language Representation Learning' accepted at TMLR.
(Apr 2024) Started a research internship with the Gemini team.
(Jul 2023) Started a research internship at Bloomberg AI with the Question Answering team.
(Oct 2022) Started a research internship at Amazon Science.

Research

	Adapting Vision-Language Models for Evaluating World Models. Mariya Hendriksen, Tabish Rashid, David Bignell, Raluca Georgescu, Abdelhak Lemkhenter, Katja Hoffman, Sam Devlin, Sarah Parisot Oral at NeurIPS LAW 2025; Under submission arXiv / GitHub We focus on the challenge of automated evaluation for world model rollouts and introduce a structured semantic protocol for this domain. We also propose UNIVERSE, a method for efficient adaptation of VLMs to this scenario.
	Demonstrating and Reducing Shortcuts in Vision-Language Representation Learning. Maurits Bleeker, Mariya Hendriksen, Andrew Yates, Maarten de Rijke 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.
	Multimodal Learned Sparse Retrieval with Probabilistic Expansion Control. Thong Nguyen, Mariya Hendriksen, Andrew Yates, Maarten de Rijke ECIR, 2024 arXiv / GitHub We propose a framework for multimodal learned sparse retrieval.
	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
	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
	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 / related work