Ashish Hooda
I am a Ph.D. student at University of Wisconsin-Madison majoring in Computer Sciences. My research interests lie in the intersection of Machine Learning and Security. I work with Prof. Somesh Jha and Prof. Kassem Fawaz at MADS&P, and with Prof. Earlence Fernandes. I did my undergraduate at Indian Institute of Technology Delhi, majoring in Electrical Engineering with minor in Computer Science.
Drop me an email if you want to chat!
Recent News
- Feb 2024: Do Large Code Models Understand Programming Concepts? A Black-box Approach , preprint of work done during Google internship!
- Nov 2023: Experimental Analyses of the Physical Surveillance Risks in Client-Side Content Scanning accepted at NDSS 2024! 🎉
- Oct 2023: D4: Detection of Adversarial Diffusion Deepfakes Using Disjoint Ensembles accepted at WACV 2024! 🎉
- Aug 2023: Presented work on attacks against Stateful Defenses at Google Research!
- Aug 2023: Stateful Defenses for Machine Learning Models Are Not Yet Secure Against Black-box Attacks accepted at CCS 2023! 🎉
- July 2023: Started Internship at Google Research with Mihai Christodorescu and Miltos Allamanis working on evaluating LLMs!
Work Experience
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Internship with the Android Security and Learning for Code teams. Worked on evaluating program
semantics understanding of Large Language Models for Code.
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Internship with the AWS Security Analytics and AI Research team. Worked on efficient training of
Graph Neural Network for intrusion detection on billion node scale graphs.
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Research
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Ashish Hooda*, Neal Mangaokar*, Jihye Choi,
Shreyas Chandrashekaran,
Kassem Fawaz, Somesh Jha, Atul Prakash
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Ashish Hooda, Mihai Christodorescu, Miltos Allamanis, Aaron Wilson, Kassem Fawaz, Somesh
Jha.
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Experimental Analyses of the Physical
Surveillance Risks in Client-Side Content Scanning
NDSS 2024 (Network and Distributed System Security Symposium)
Ashish Hooda, Andrey Labunets, Tadayoshi Kohno,
Earlence Fernandes.
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D4: Detection of Adversarial Diffusion Deepfakes
Using Disjoint Ensembles
WACV 2024 (IEEE/CVF Winter Conference on Applications of Computer Vision) |
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Stateful Defenses for
Machine Learning Models Are Not Yet Secure Against Black-
box Attacks
CCS 2023 (ACM Conference on Computer and Communications Security) |
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Theoretically Principled Trade-off for
Stateful Defenses against Query-Based Black-Box Attacks
ICML Workshop 2023 (AdvML-Frontiers'23) |
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SkillFence: A Systems
Approach to Mitigating Voice-Based Confusion Attacks
IMWUT / UBICOMP 2022 (ACM Interactive, Mobile, Wearable and Ubiquitous Technologies)
Ashish Hooda, Matthew Wallace, Kushal Jhunjhunwalla,
Earlence Fernandes , Kassem Fawaz.
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Invisible Perturbations: Physical Adv Examples Exploiting the Rolling Shutter Effect
CVPR 2021 (Conference on Computer Vision and Pattern Recognition)
Ashish Hooda*, Athena Sayles*, Mohit Gupta, Rahul Chatterjee, Earlence Fernandes.
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