I am currently a Postdoctoral Associate with the Center for Machine Learning (CML) in the University of Maryland Institute for Advanced Computer Studies (UMIACS). I work under the supervision of Tom Goldstein.
I received my PhD in Computer Science at University of Maryland, Baltimore County. I was advised by Hamed Pirsiavash. In my dissertation, I studied ways in which state-of-the-art deep learning methods for computer vision are vulnerable to backdoor attacks and proposed defense methods to remedy the vulnerabilities.
During my PhD, I have worked as a Machine Learning Research Intern at Bosch Center for AI, an Applied Scientist Intern at Amazon Rekognition, and a Machine Learning Intern at Matroid.
Prior to this, I was a Software Engineer at Samsung Research Institute Bangalore, India where I was part of the DRAM Group of Samsung Semiconductor India Research.
My hobbies include photography, writing, playing football and chess. I support Manchester United FC. See what I am currently reading.
Complete list on Google Scholar.
Backdoor Attacks on Self-Supervised Learning Paper Slides Code
Aniruddha Saha, Ajinkya Tejankar, Soroush Abbasi Koohpayegani, Hamed Pirsiavash
CVPR 2022 (Oral)
Role of Spatial Context in Adversarial Robustness for Object Detection Paper Slides Video Code
Aniruddha Saha*, Akshayvarun Subramanya*, Koninika Patil, Hamed Pirsiavash
CVPR 2020 Workshop on Adversarial Machine Learning in Computer Vision (Long Paper)
*equal contribution
An Adaptive Foreground-Background Separation Method for Effective Binarization of Document Images Paper
Bishwadeep Das, Showmik Bhowmik, Aniruddha Saha, Ram Sarkar
Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016)
Backdoor Attacks on Vision Transformers Paper
Akshayvarun Subramanya, Aniruddha Saha, Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash
(April 2023)
Department of Computer Science and Engineering, Indian Institute of Technology Delhi
Host: Chetan Arora
Slides
(March 2023)
NSF-IEEE Workshop: Toward Explainable, Reliable, And Sustainable Machine Learning In Signal & Data Science
Slides
(May 2022)
Johns Hopkins Mathematical Institute for Data Science
Host: René Vidal
Slides
Conferences: ICCV 2023, CVPR 2023, ECCV 2022, ICPR 2022, CVPR 2022, ICCV 2021*, ICPR 2020
Workshops: CVPR 2022, ICLR 2022, ICCV 2021, ICML 2021, CVPR 2021, ICLR 2021, ECCV 2020, CVPR 2020, ICLR 2020
Journals: IEEE TPAMI, IEEE TIFS, IEEE TETCI, IET Computer Vision
*Outstanding Reviewer Award
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