I am on the job market.
Please reach out, if you think a research opportunity (academia or industry) would be a good fit for me.
Most recently, I was a Postdoctoral Associate at the Center for Machine Learning (CML) in University of Maryland, College Park. I was advised by 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.
A Closer Look at Robustness of Vision Transformers to Backdoor Attacks Paper
Akshayvarun Subramanya, Aniruddha Saha*, Soroush Abbasi Koohpayegani*, Ajinkya Tejankar, Hamed Pirsiavash
WACV 2024
*equal contribution
Revisiting Image Classifier Training for Improved Certified Robust Defense against Adversarial Patches Paper
Aniruddha Saha*, Shuhua Yu*, Mohammad Sadegh Norouzzadeh, Wan-Yi Lin, Chaithanya Kumar Mummadi
Transactions on Machine Learning Research (TMLR) Oct 2023
*equal contribution
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
NEFTune - Noisy Embeddings Improve Instruction Finetuning Paper
Neel Jain*, Ping-yeh Chiang*, Yuxin Wen*, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein
ICLR 2024
*equal contribution
On the Reliability of Watermarks for Large Language Models Paper
John Kirchenbauer*, Jonas Geiping*, Yuxin Wen, Manli Shu, Khalid Saifullah, Kezhi Kong, Kasun Fernando, Aniruddha Saha, Micah Goldblum, Tom Goldstein
ICLR 2024
*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)
Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text Paper
Abhimanyu Hans*, Avi Schwarzschild*, Valeriia Cherepanova, Hamid Kazemi, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein
*equal contribution
Baseline Defenses for Adversarial Attacks Against Aligned Language Models Paper
Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping-yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, Tom Goldstein
*equal contribution
Bring Your Own Data! Self-Supervised Evaluation for Large Language Models Paper
Neel Jain*, Khalid Saifullah*, Yuxin Wen, John Kirchenbauer, Manli Shu, Aniruddha Saha, Micah Goldblum, Jonas Geiping, Tom Goldstein
*equal contribution
(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:
ICLR 2024
NeurIPS 2023,
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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