Adel Bibi

Senior Research Fellow

University of Oxford

Kellogg College


I am a senior research fellow in machine learning and computer vision at the Department of Engineering Science of the University of Oxford with the Torr Vision Group led by Prof Philip Torr. I am also a Junior Research Fellow (JRF) of Kellogg College. Prior to that, I was a postdoctoral researcher at the same group for a year since October 2020. I received my MSc and PhD degrees from King Abdullah University of Science & Technology (KAUST) in 2016 and 2020, respectively, where I was part of the Image and Video Understanding Lab (IVUL) advised by Bernard Ghanem. In 2018, I was a visiting PhD intern for 6 months at Intel Labs in Munich working with Vladlen Koltun. I have worked on a variety of problems; problems that I personally find interesting and challenging.


  • Computer Vision
  • Machine Learning
  • Optimization


  • 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


  • [Dec 1st, 2021]: I got promoted to a senior researcher of the Torr Vision Group (TVG) at the University of Oxford.
  • [Dec 1st, 2021]: Two papers, Combating Adversaries with anti-adversaries and DeformRS: Certifying Input Deformations with Randomized Smoothing, are accepted in AAAI22.
  • [Nov 18th, 2021]: We were awarded KAUST’s Competitive Research Grant with a total of > 1.05M$ (one million USD). This is a 3 years collaboration between KAUST and Oxford.
  • [Oct 15th, 2021]: Rethinking Clustering for Robustness is accepted in BMVC21.
  • [June 22nd, 2021]: Anti Adversary paper is accepted in Adversarial Machine Learning Workshop @ICML21.
  • [June 13th, 2021]: I have been elected as a Junior Research Fellow of Kellogg College, University of Oxford. Appointment starts in October 2021.
  • [March 28th, 2021]: ETB robustness paper is accepted in RobustML Workshop @ICLR21.
  • [November 8th, 2020]: New paper on robustness is on arXiv.
  • [November 8th, 2020]: New paper on randomized smoothing is on arXiv.
  • [October 15th, 2020]: I joined the Torr Vision Group working with Philip Torr at the University of Oxford.
  • [July 2nd, 2020]: Gabor layers enhance robustness paper accepted to ECCV20 arXiv.
  • [June 30th, 2020]: One paper is out on new expressions for the output moments of ReLU based networks with various new appliactions arXiv.
  • [June 24th, 2020]: New paper with SOTA results, backed with theory, on training robust models through feature clustering arXiv.
  • [March 31st, 2020]: I have sucessfully defended my PhD thesis.
  • [Dec 20th, 2019]: One paper accepted to ICLR20.
  • [Nov 11th, 2019]: One spotlight paper accepted to AAAI20.
  • [Sept 25th, 2019]: Recognized as outstanding reviewer for ICCV19. Link.
  • [August 5th, 2019]: I was invited to give a talk about the most recent research in computer vision and machine learning from the IVUL group at PRIS19, Dead Sea, Jordan. I also gave a 1 hour long workshop about deep learning and pytorch. Slides1/ Slides2/ Material.
  • [July 6th, 2019]: I was invited to give a talk at the Eastern European Conference on Computer Vision, Odessa, Ukraine. Slides.
  • [June 28th, 2019]: I gave a talk at the Biomedical Computer Vision Group directed by Prof Pablo Arbelaez, Bogota, Colombia. Slides.
  • [June 15th, 2019]: Attended CVPR19.
  • [June 9th, 2019]: Recognized as an outstanding reviewer for CVPR19. This is the second time in a row for CVPR. Check it out. :)
  • [May 26th, 2019]: A new paper is out on derivative free optimization with momentum with new rates and results on continuous controls tasks. arXiv.
  • [May 25th, 2019]: New paper! New provably tight interval bounds are derived for DNNs. This allows for very simple robust training of large DNNs. arXiv.
  • [May 11th, 2019]: How to train robust networks outperforming 2-21x fold data augmentation? New paper out on arXiv.
  • [May 6th, 2019]: Attended ICLR19 in New Orleans.
  • [Feb 4th, 2019]: New paper on derivative-free optimization with importance sampling is out! Paper is on arXiv.
  • [Dec 22nd, 2018]: One paper accepted to ICLR19, Louisiana, USA.
  • [Nov 6th, 2018]: One paper accepted to WACV19, Hawaii, USA.
  • [July 3rd, 2018]: One paper accepted to ECCV18, Munich, Germany.
  • [June 19th, 2018]: Attended CVPR18 and gave an oral talk on our most recent work on analyzing piecewise linear deep networks using Gaussian network moments. Tensorflow, Pytorch and MATLAB codes are released.
  • [June 17th, 2018]: Received a fully funded scholarship to attend the AI-DLDA 18 summer school in Udine, Italy. Unfortunately, I won’t be able to attend for time constraints. Link
  • [June 15th, 2018]: New paper out! “Improving SAGA via a Probabilistic Interpolation with Gradient Descent”.
  • [April 30th, 2018]: I’m interning for 6 months at the Intel Labs in Munich this summer with Vladlen Koltun.
  • [April 22nd, 2018]: Recognized as an outstanding reviewer for CVPR18. I’m also on the list of emergency reviewers. Check it out. :)
  • [March 6th, 2018]: One paper accepted as [Oral] in CVPR 2018.
  • [Feb 5, 2018]: Awarded the best KAUST poster prize in the Optimization and Big Data Conference.
  • [Decemmber 11, 2017]: TCSC code is on github.
  • [October 22, 2017]: Attened ICCV17, Venice, Italy.
  • [July 22, 2017]: Attened CVPR17 in Hawaii and gave an oral presentation on our work on solving the LASSO with FFTs, July 2017.
  • [July 16, 2017]: FFTLasso’s code is available online.
  • [July 9, 2017]: Attended the ICVSS17, Sicily, Italy.
  • [June 15, 2017]: Selected to attend the International Computer Vision Summer School (ICVSS17), Sicily, Italy.
  • [March 17, 2017]: 1 paper accepted to ICCV17.
  • [March 14, 2017]: Received my NanoDegree on Deep Learning from Udacity.
  • [March 3, 2017]: 1 oral paper accepted to CVPR17, Hawai, USA.
  • [October 19, 2016]: ECCV16’s code has been released on github.
  • [October 8, 2016]: Attended ECCV16, Amsterdam, Netherlands.
  • [July 11, 2016]: 1 spotlight paper accepted to ECCV16, Amsterdam, Netherlands.
  • [June 26, 2016]: Attended CVPR16, Las Vegas, USA. Two papers presented.
  • [May 13, 2016]: ICCVW15 code is now avaliable online.
  • [April 11, 2016]: Successfully defended my Master’s Thesis.
  • [March 2, 2016]: 2 papers (1 spotlight) accepted to CVPR16, Las Vegas, USA.
  • [November 20, 2015]: 1 paper acceted to ICCVW15, Santiago, Chile.
  • [June 8, 2015]: Attended CVPR15, Boston, USA.

Recent Publications

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DeformRS: Certifying Input Deformations with Randomized Smoothing

​​Deep neural networks are vulnerable to input deformations in the form of vector fields of pixel displacements and to other parameterized geometric deformations e.g. translations, rotations, etc. Current input deformation certification methods either 1. do not scale to deep networks on large input datasets, or 2. can only certify a specific class of deformations, e.g. only rotations. We reformulate certification in randomized smoothing setting for both general vector field and parameterized deformations and propose DeformRS-VF and DeformRS-Par, respectively. Our new formulation scales to large networks on large input datasets. For instance, DeformRS-Par certifies rich deformations, covering translations, rotations, scaling, affine deformations, and other visually aligned deformations such as ones parameterized by Discrete-Cosine-Transform basis. Extensive experiments on MNIST, CIFAR10, and ImageNet show competitive performance of DeformRS-Par achieving a certified accuracy of 39% against perturbed rotations in the set [-10,10] degrees on ImageNet.

Combating Adversaries with Anti-Adversaries

​​Deep neural networks are vulnerable to small input perturbations known as adversarial attacks. Inspired by the fact that these adversaries are constructed by iteratively minimizing the confidence of a network for the true class label, we propose the anti-adversary layer, aimed at countering this effect. In particular, our layer generates an input perturbation in the opposite direction of the adversarial one and feeds the classifier a perturbed version of the input. Our approach is training-free and theoretically supported. We verify the effectiveness of our approach by combining our layer with both nominally and robustly trained models and conduct large-scale experiments from black-box to adaptive attacks on CIFAR10, CIFAR100, and ImageNet. Our layer significantly enhances model robustness while coming at no cost on clean accuracy.

Expected Tight Bounds for Robust Deep Neural Network Training

​​Training Deep Neural Networks (DNNs) that are robust to norm bounded adversarial attacks remains an elusive problem. While verification based methods are generally too expensive to robustly train large networks, it was demonstrated in Gowal et. al. that bounded input intervals can be inexpensively propagated per layer through large networks. This interval bound propagation (IBP) approach lead to high robustness and was the first to be employed on large networks. However, due to the very loose nature of the IBP bounds, particularly for large networks, the required training procedure is complex and involved. In this paper, we closely examine the bounds of a block of layers composed of an affine layer followed by a ReLU nonlinearity followed by another affine layer. In doing so, we propose probabilistic bounds, true bounds with overwhelming probability, that are provably tighter than IBP bounds in expectation. We then extend this result to deeper networks through blockwise propagation and show that we can achieve orders of magnitudes tighter bounds compared to IBP. With such tight bounds, we demonstrate that a simple standard training procedure can achieve the best robustness-accuracy trade-off across several architectures on both MNIST and CIFAR10.


  • Invited to give a talk at SIAM Discrete Math Conference at Georgia Institute of Technology on our work on the decision boundaries from a tropical geometric perspective

  • PRIS19, Dead Sea, Jordan. A Basket of Computer Vision Research Problems Slides

  • EECVC19, Odessa, Ukranine. Optimization Approach to a Block of Layers and Derivative Free Optimization Slides

  • CVPR18, Utah, USA. Analytic Expressions for Probabilistic Moments of PL-DNN With Gaussian Input Slides

  • CVPR17, Hawaii, USA. FFTLasso: Large-Scale LASSO in the Fourier Domain Slides

  • Optimization and Big Data Conference 2018, KAUST, Saudi Arabia. High Order Tensor Formulation for Convolutional Sparse Coding

  • ECCV16, Amsterdam, Netherlands. Target Response Adaptation for Correlation Filter Tracking Slides