基本信息
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职业迁徙
个人简介
Detailed Research interests
AI security
The Deep Learning models that have revolutionized the fields of Computer Vision, Speech and Language Processing, can behave strangely out of their training domain. Agents with malicious intents can actively create these contexts in order to exploit deployed Artificial Intelligence (AI) systems : force self-driving cars to confuse signs and crash, military drones to mistake hospitals for military bases, personal assistants or smartphones to reveal private user information, etc.
In my thesis I work on formalizing, evaluating and mitigating these threats, such as adversarial perturbations, data poisoning, privacy attacks, etc. My long-term research goal is to contribute to the safe development of Artificial Intelligence, so that society can benefit and not suffer from it.
I also believe that security is a fascinating and central aspect of AI from a theoretical perspective. People who create these models want to replicate aspects of human intelligence, and security threats arise precisely when models behave differently from humans (in a way that attackers can control). I would argue that making models safer is equivalent to making them better.
Speech Recognition
Some aspects of AI security are common to all models, but other are specific to certain applications and architectures. When investigating the latter I focus on Speech processing applications, and in particular Automatic Speech Recognition. I am a member of the Machine Learning and Signal Processing research group at CMU. I have also conducted two internships in the Alexa Hybrid Science team in Pittsburgh, where I investigated attacks against and defenses for Amazon Alexa’s speech-to-text models.
AI security
The Deep Learning models that have revolutionized the fields of Computer Vision, Speech and Language Processing, can behave strangely out of their training domain. Agents with malicious intents can actively create these contexts in order to exploit deployed Artificial Intelligence (AI) systems : force self-driving cars to confuse signs and crash, military drones to mistake hospitals for military bases, personal assistants or smartphones to reveal private user information, etc.
In my thesis I work on formalizing, evaluating and mitigating these threats, such as adversarial perturbations, data poisoning, privacy attacks, etc. My long-term research goal is to contribute to the safe development of Artificial Intelligence, so that society can benefit and not suffer from it.
I also believe that security is a fascinating and central aspect of AI from a theoretical perspective. People who create these models want to replicate aspects of human intelligence, and security threats arise precisely when models behave differently from humans (in a way that attackers can control). I would argue that making models safer is equivalent to making them better.
Speech Recognition
Some aspects of AI security are common to all models, but other are specific to certain applications and architectures. When investigating the latter I focus on Speech processing applications, and in particular Automatic Speech Recognition. I am a member of the Machine Learning and Signal Processing research group at CMU. I have also conducted two internships in the Alexa Hybrid Science team in Pittsburgh, where I investigated attacks against and defenses for Amazon Alexa’s speech-to-text models.
研究兴趣
论文共 17 篇作者统计合作学者相似作者
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CoRR (2024)
引用0浏览0EI引用
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Muhammad Ahmed Shah,Roshan Sharma,Hira Dhamyal,Raphael Olivier,Ankit Shah,Dareen Alharthi, Hazim T Bukhari,Massa Baali,Soham Deshmukh, Michael Kuhlmann,Bhiksha Raj,Rita Singh
CoRR (2023)
ICML 2023 (2023)
引用1浏览0EI引用
1
0
arXiv (Cornell University) (2022)
CoRRpp.4394-4398, (2022)
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作者统计
#Papers: 17
#Citation: 155
H-Index: 4
G-Index: 8
Sociability: 3
Diversity: 1
Activity: 8
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