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 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.

Currently, I am interested in large scale offline and online robust continual learning. Robustness, in both aspects empirical and provably certifiable, here refers to deep models under $\ell_p$ bouded additive and geoemtric attacks. Moreover, continual learning refers to the learning from a stream of data in stringent memory and computational settings.

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  • 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


  • [August 30th, 2022]: My grant proposal with Phil on theoretical extensions to randomzied smoothing was awarded $20,000 AWS credits by Amazon Research Awards (ARA).
  • [August 28th, 2022]: Our paper ANCER was accepted in Transactions on Machine Learning Research (TMLR).
  • [August 15th, 2022]: Our paper on Tropical Geometry was accepted to appear in the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).
  • I will serve as a Senior Program Committee (Area Chair/Meta Reviewer) for AAAI23.
  • [July 11th, 2022]: I will serve as a Senior Program Committee (Area Chair/Meta Reviewer) for AAAI23.
  • [May 26th, 2022]: Three papers accepted to the Adversarial Machine Learning Frontiers ICML2022 workshop! Papers will be coming on arXiv soon.
  • [May 16th, 2022]: Our paper titled Data Dependent Randomzied Smoothing is accepted to UAI22.
  • [April 22nd, 2022]: I was selected as the Highlighted Reviewer of ICLR 2022 and received a free conference registration.
  • [Dec 1st, 2021]: I got promoted to a senior researcher of the Torr Vision Group (TVG) at the University of Oxford.
  • [Dec 1st, 2021]: Two papers, Combating Adversaries with anti-adversaries and DeformRS: Certifying Input Deformations with Randomized Smoothing, are accepted in AAAI22.
  • [Nov 18th, 2021]: We were awarded KAUST’s Competitive Research Grant with a total of > 1.05M$ (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

(2022). ANCER: Anisotropic Certification via Sample-wise Volume Maximization. In TMLPR22.

PDF Code

(2022). On the Decision Boundaries of Neural Networks: A Tropical Geometry Perspective. In PAMI22.


(2022). Data Dependent Randomized Smoothing. In UAI22.

PDF Code

(2022). DeformRS: Certifying Input Deformations with Randomized Smoothing. Oral in AAAI22.


(2022). Combating Adversaries with Anti-Adversaries. In AAAI22.

PDF Code Slides

Recent & Upcoming Talks

Computer Vision and Machine Learning; Introduction, Applications, and Challenges
Recent Advances in Randomized Smoothing
Trust Worthy AI -- Verified
From Adversarial Robustness to Randomized Smoothing
Analyzing a Block of Layers in Deep Neural Networks


  • 20.16, Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ