Aleksandar (Suny) Shtedritski

Hi, I am currently a third-year DPhil (PhD) student at Visual Geometry Group, Oxford, and am lucky to be advised by Prof. Andrea Vedaldi and Prof. Christian Rupprecht. This summer I am interning at Google DeepMind with Olivia Wiles.

Before that, I graduated with an MEng in Engineering Science from the Univesity of Oxford. During my PhD I started a student research group within the AI society in Oxford, OxAI Labs , where we do research on fairness and bias.

My nickname is to be pronounced like "Sunny" ☀️ despite missing an "n".

Email / Google Scholar / LinkedIn / Github

Research

I'm interested in vision + language, un/self-supervised computer vision, 3D, and fairness.

	SHIC: Shape-Image Correspondences with no keypoint supervision. Aleksandar Shtedritski, Christian Rupprecht, Andrea Vedaldi ECCV, 2024 arXiv / website / code / demo We present an unsupervised method for shape-to-image correspondenes.
	HelloFresh: LLM Evaluations on Streams of Real-World Human Editorial Actions across X Community Notes and Wikipedia edits Tim Franzmeyer, Aleksandar Shtedritski, Samuel Albanie, Philip Torr, Joao F. Henriques, Jakob E. Foerster, ACL, 2024 arXiv / website / code A living benchmark for LLMs that addresses issues like test data contamination and benchmark overfitting.
	BioPlanner: Automatic Evaluation of LLMs on Protocol Planning in Biology Odhran O'Donoghue, Aleksandar Shtedritski, John Ginger, Ralph Abboud, Ali Essa Ghareeb, Justin Booth, Samuel G Rodriques EMNLP, 2023 arXiv / code A framework for evaluation of LLMs on long planning tasks, with an application in biology.
	What does CLIP know about a red circle? Visual prompt engineering for VLMs Aleksandar Shtedritski, Christian Rupprecht, Andrea Vedaldi ICCV, 2023 (Oral Presentation) arXiv / code We discover an emergent ability of CLIP, where drawing a red circle focuses the global image description to the region inside the circle.
	Learning Universal Semantic Correspondences with No Supervision and Automatic Data Curation Aleksandar Shtedritski, Andrea Vedaldi, Christian Rupprecht ICCV Workshop ion Representation Learning with Limited Data, 2023 (Oral Presentation) paper / We present a method for learning robust and generalizable semantic correspondences.
	VisoGender: A dataset for benchmarking gender bias in image-text pronoun resolution Siobhan Mackenzie Hall, Fernanda Gonçalves Abrantes, Hanwen Zhu, Grace Sodunke, Aleksandar Shtedritski, Hannah Rose Kirk NeurIPS Datasets and Benchmarks, 2023 arXiv / code A new dataset for benchmarking gender bias in vision-language models.
	Balancing the Picture: Debiasing Vision-Language Datasets with Synthetic Contrast Sets Brandon Smith, Miguel Farinha, Siobhan Mackenzie Hall, Hannah Rose Kirk, Aleksandar Shtedritski, Max Bain NeurIPS Workshop SyntheticData4ML, 2023 arXiv / code We demonstrate that the datasets used to evaluate the bias of VLMs are themselves biased. We propose to debias these datasets using synthetic contrast sets.
	A prompt array keeps the bias away: Debiasing vision-language models with adversarial learning Hugo Bergh, Siobhan Mackenzie Hall, Wonsuk Yang, Yash Bhalgat, Hannah Rose Kirk, Aleksandar Shtedritski, Max Bain AACL-IJNCLP, 2022 arXiv / code We propose a lightweight method to debias CLIP.
	Bias out-of-the-box: An empirical analysis of intersectional occupational biases in popular generative language models Hannah Rose Kirk, Yennie Jun, Haider Iqbal, Elias Benussi, Frederic A. Dreyer, Aleksandar Shtedritski, Yuki M. Aasano NeurIPS, 2021 arXiv / code An in-depth analysis of instersectional biases of GPT-2.

Services

Reviewer

CVPR 2023, 2024, ICCV 2023, ECCV 2024 NeurIPS 2023, 2024

Teaching Assistant

Information Engineering tutorial classes, Engineering Department, University of Oxford, 2021-22
Image and Signal Processing lab, Engineering Department, University of Oxford, 2022
Machine Learning tutorial classes, Department of Computer Science, University of Oxford, 2020-21
Machine Learning revision classes, Magdalen College, University of Oxford, 2020-21
Control Systems lab, Engineering Department, University of Oxford, 2021

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