Aniruddha Saha

(awe-knee-rue-though sha-haa)

Postdoctoral Associate at University of Maryland

Iribe Center 5124

anisaha1 [at] umd [dot] edu

News
Bio

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.

Publications

Complete list on Google Scholar.

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)

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

Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs Paper Webpage Slides Video Code

Soheil Kolouri*, Aniruddha Saha*, Hamed Pirsiavash, Heiko Hoffmann

CVPR 2020 (Oral)

*equal contribution

Hidden Trigger Backdoor Attacks Paper Slides Poster Code

Aniruddha Saha, Akshayvarun Subramanya, Hamed Pirsiavash

AAAI 2020 (Oral)

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)

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

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

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

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

Backdoor Attacks on Vision Transformers Paper

Akshayvarun Subramanya, Aniruddha Saha, Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash

Timeline
Talks
Service & Leadership
Program Committee (Reviewer)

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

Program Chair (Organizer)
Backdoors in Deep Learning Workshop at NeurIPS 2023.
Acknowledgement

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