Ondrej Bohdal
I'm a senior machine learning researcher at Samsung
Research, where I focus on personalization of generative AI models. Before joining Samsung, I
was a postdoctoral researcher at the
University of Edinburgh, working on topics such as multimodal large language models, diffusion
models, fairness, uncertainty calibration and out-of-distribution generalization.
I did my PhD on Meta-Learning Algorithms and Applications at the
University of Edinburgh, advised by Timothy
Hospedales.
I was a research intern at Samsung AI
Center, Cambridge and Amazon Web Services, Berlin, and
also did part of my
studies at the Alan Turing Institute in London.
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London, UK
Research Overview
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Publications
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Background
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Research Topics
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I've worked on diverse topics within deep learning, including generative AI (large language models
and diffusion models), parameter-efficient fine-tuning, multimodality, meta-learning, data
efficiency, domain adaptation, out-of-distribution generalization, uncertainty calibration, fairness.
I work
with images (computer vision) and text (natural language processing).
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Selected Publications
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Efficient Compositional Multi-tasking for On-device Large Language Models
Ondrej Bohdal, Mete Ozay, Jijoong Moon, Kyeng-Hun Lee, Hyeonmok Ko, Umberto Michieli
EMNLP, 2025
paper
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project page
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code
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LoRA.rar: Learning to Merge LoRAs via Hypernetworks for Subject-Style Conditioned Image
Generation
Donald Shenaj, Ondrej Bohdal, Mete Ozay, Pietro Zanuttigh, Umberto Michieli
ICCV, 2025
paper
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project page
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code
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VL-ICL Bench: The Devil in the Details of Benchmarking Multimodal In-Context Learning
Yongshuo Zong*, Ondrej Bohdal*, Timothy Hospedales
* Joint first authors
ICLR, 2025
paper
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project page
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code
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data
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Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn
Ondrej Bohdal*, Yinbing Tian*, Yongshuo Zong, Ruchika Chavhan, Da Li, Henry Gouk, Li Guo, Timothy
Hospedales
* Joint first authors
CVPR, 2023
paper
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project page
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code
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video
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slides
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poster
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EvoGrad: Efficient Gradient‑Based Meta‑Learning and Hyperparameter Optimization
Ondrej Bohdal, Yongxin Yang, Timothy Hospedales
NeurIPS, 2021
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blog
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code
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video
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slides
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poster
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