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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Research Overview / Publications / Background

profile photo

Research Topics

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).

Selected Publications

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 / project page / code
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 / project page / code
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 / project page / code / data
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 / project page / code / video / slides / poster
EvoGrad: Efficient Gradient‑Based Meta‑Learning and Hyperparameter Optimization
Ondrej Bohdal, Yongxin Yang, Timothy Hospedales
NeurIPS, 2021
paper / blog / code / video / slides / poster

Design and source code from Jon Barron's website