Aleksandar (Suny) Shtedritski

Hi, I am Suny! I am joining Microsoft AI as Member of Technical Staff. Before that, I did a DPhil (PhD) at the Visual Geometry Group, Oxford, where was lucky to be advised by Prof. Andrea Vedaldi and Prof. Christian Rupprecht. During my PhD, I was a summer intern at Google DeepMind with Olivia Wiles.

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

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

Email  /  Google Scholar  /  LinkedIn  /  Github

profile photo
Research

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

b3do SynCity: Training-Free Generation of 3D Worlds.
Paul Engslter*, Aleksandar Shtedritski*, Iro Laina, Christian Rupprecht, Andrea Vedaldi,
* Equal contribution.

ArXiv preprint, 2025  
arXiv / website / code / demo

We present a training-free and optimisation-free method for generating 3D worlds.

b3do Highlight: Learning Visual Prompts for Vision-Language Models.
Jana Zeller Aleksandar Shtedritski, Christian Rupprecht,
Coming soon  
website

We automatically discover visual prompts for CLIP. Interestingly, they look like red circles.

b3do 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.

b3do 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.

b3do PaperQA: Retrieval-Augmented Generative Agent for Scientific Research
Jakub Lala, Odhran O'Donoghue, Aleksandar Shtedritski, Sam Cox, Samuel G Rodriques, Andrew White
Technical report, 2023  
arXiv / code

We present a RAG agent for scientific research, and a benchmark for information retrieval in sciences.

b3do 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.

b3do 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.

b3do 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.

b3do 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.

b3do 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.

b3do 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.

b3do 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

NeurIPS, ICLR, CVPR, ICCV, ECCV

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

This website template is borrowed from Jon Barron. Thanks!