Mariya Hendriksen

I am intern with the Game Intelligence team at Microsoft Research Cambridge. I completed my PhD at the University of Amsterdam where I worked on multimodal machine learning for information retrieval under the supervision of Maarten de Rijke and Paul Groth.

I received my Master’s in Artificial Intelligence from KU Leuven and Bachelor's in Computational Linguistics from Novosibirsk State University. Throughout my academic journey, I've interned at several AI labs, including the Gemini team at Google, Bloomberg AI, Amazon Alexa, LIIR at KU Leuven, and ETH Zurich.

I was born in Ukraine and grew up in the Far North of Siberia, Russia. My background motivated my commitment to building diverse and inclusive workspaces. As such, I am a mentor within the Inclusive AI initiative.

Feel free to reach out for research discussions, collaborations or just to have a chat!

Email  /  Scholar  /  GitHub  /  Bsky  /  X

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News

Publications

Milestones & Activities

  • (Sep 2024) Started a research internship at Microsfot Research Cambridge with the Game Intelligence team.
  • (Apr 2024) Started a research internship at Google Zurich with the Gemini team.
  • (Mar 2024) Attended the ELLIS Winter School on Foundation Models.
  • (Jul 2023) Started a research internship at Bloomberg AI in London.
  • (Nov 2022) Started a research internship at Alexa, Amazon Science in London.
  • (Jun 2022) Gave a talk on ‘Extending CLIP for Category-to-Image Retrieval in E-commerce’ at Amazon Luxembourg.
  • (Apr 2020) Attended MLSS 2020.

Research

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


Thanks to Jon Barron for the template :)