Adel Bibi is a senior researcher in machine learning and computer vision at the Department of Engineering Science of the University of Oxford, a Research Fellow (JRF) at Kellogg College, and a member of the ELLIS Society. Bibi is an R&D Distinguished Advisor with Softserve. Previously, Bibi was a senior research associate and a postdoctoral researcher with Philip H.S. Torr since October 2020. He received his MSc and PhD degrees from King Abdullah University of Science & Technology (KAUST) in 2016 and 2020, respectively, advised by Bernard Ghanem. Bibi was awarded an Amazon Research Award in 2022 in the Machine Learning Algorithms and Theory track. Bibi received four best paper awards; a NeurIPS23 workshop, an ICML23 workshop, a 2022 CVPR workshop, and one at the Optimization and Big Data Conference in 2018. His contributions include over 30 papers published in top machine learning and computer vision conferences. He also received four outstanding reviewer awards (CVPR18, CVPR19, ICCV19, ICLR22) and a Notable Area Chair Award in NeurIPS23.
Currently, Bibi is leading a group in Oxford focusing on the intersection between AI safety of large foundational models in both vision and language (covering topics such as robustness, certification, alignment, adversarial elicitation, etc.) and the efficient continual update of these models.
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[Note!] I am always looking for strong self-motivated PhD students. If you are interested in Trustworthy Foundation Models that Continually Learn, reach out!
[Consulting Expertise] I have consulted in the past on projects spanning core machine learning and data science, computer vision, certification and AI safety, optimization formulations for matching and resource allocation problems, among other areas.
PhD in Electrical Engineering (4.0/4.0); Machine Learning and Optimization Track, 2020
King Abdullah University of Science and Technology (KAUST)
MSc in Electrical Engineering (4.0/4.0); Computer Vision Track, 2016
King Abdullah University of Science and Technology (KAUST)
BSc in Electrical Engineering (3.99/4.0), 2014
Kuwait University
~~ End of 2023 ~~
~~ End of 2022 ~~
~~ End of 2021 ~~
~~ End of 2020 ~~
~~ End of 2019 ~~
~~ End of 2018 ~~
~~ End of 2017 ~~
~~ End of 2016 ~~
~~ End of 2015 ~~
We explore the impact of training with more diverse datasets, characterized by the number of unique samples, on the performance of self-supervised learning (SSL) under a fixed computational budget. Our findings consistently demonstrate that increasing pretraining data diversity enhances SSL performance, albeit only when the distribution distance to the downstream data is minimal. Notably, even with an exceptionally large pretraining data diversity achieved through methods like web crawling or diffusion-generated data, among other ways, the distribution shift remains a challenge. Our experiments are comprehensive with seven SSL methods using large-scale datasets such as ImageNet and YFCC100M amounting to over 200 GPU days.
For medical imaging AI models to be clinically impactful, they must generalize. However, this goal is hindered by (i) diverse types of distribution shifts, such as temporal, demographic, and label shifts, and (ii) limited diversity in datasets that are siloed within single medical institutions. While these limitations have spurred interest in federated learning, current evaluation benchmarks fail to evaluate different shifts simultaneously. However, in real healthcare settings, multiple types of shifts co-exist, yet their impact on medical imaging performance remains unstudied. In response, we introduce FedMedICL, a unified framework and benchmark to holistically evaluate federated medical imaging challenges, simultaneously capturing label, demographic, and temporal distribution shifts. We comprehensively evaluate several popular methods on six diverse medical imaging datasets (totaling 550 GPU hours). Furthermore, we use FedMedICL to simulate COVID-19 propagation across hospitals and evaluate whether methods can adapt to pandemic changes in disease prevalence. We find that a simple batch balancing technique surpasses advanced methods in average performance across FedMedICL experiments. This finding questions the applicability of results from previous, narrow benchmarks in real-world medical settings.
Despite the widespread adoption of prompting, prompt tuning and prefix-tuning of transformer models, our theoretical understanding of these fine-tuning methods remains limited. A key question is whether one can arbitrarily modify the behavior of a pretrained model by prompting or prefix-tuning it. Formally, whether prompting and prefix-tuning a pretrained model can universally approximate sequence-to-sequence functions. This paper answers in the affirmative and demonstrates that much smaller pretrained models than previously thought can be universal approximators when prefixed. In fact, prefix-tuning a single attention head is sufficient to approximate any continuous function making the attention mechanism uniquely suited for universal approximation. Moreover, any sequence-to-sequence function can be approximated by prefixing a transformer with depth linear in the sequence length. Beyond these density-type results, we also offer Jackson-type bounds on the length of the prefix needed to approximate a function to a desired precision.