What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer security
- Cryptography
His scientific interests lie mostly in Computer security, Cryptography, Computer network, Encryption and Wireless sensor network.
His Computer security research incorporates themes from Android and Protocol.
His study in Cryptography is interdisciplinary in nature, drawing from both Secrecy and Cipher.
His Static routing study, which is part of a larger body of work in Computer network, is frequently linked to Mimicry, bridging the gap between disciplines.
David Wagner has researched Wireless sensor network in several fields, including Key distribution in wireless sensor networks, Optimized Link State Routing Protocol and Zone Routing Protocol.
His work deals with themes such as Ciphertext and Server, which intersect with Block cipher.
His most cited work include:
- Towards Evaluating the Robustness of Neural Networks (3170 citations)
- Secure routing in wireless sensor networks: attacks and countermeasures (2764 citations)
- Practical techniques for searches on encrypted data (2323 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Computer security, Cryptography, Block cipher, Cryptanalysis and Internet privacy.
His research in Computer security tackles topics such as Protocol which are related to areas like Radio-frequency identification.
His work carried out in the field of Cryptography brings together such families of science as Hash function, Key, State and Secrecy.
His research integrates issues of Discrete mathematics, Theoretical computer science, Arithmetic and Block cipher mode of operation in his study of Block cipher.
His research in Cryptanalysis intersects with topics in Cryptosystem, Side channel attack and Public-key cryptography.
In his research on the topic of Internet privacy, World Wide Web is strongly related with Android.
He most often published in these fields:
- Computer security (47.74%)
- Cryptography (14.98%)
- Block cipher (12.54%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (6.97%)
- Adversarial system (6.62%)
- Computer security (47.74%)
In recent papers he was focusing on the following fields of study:
David Wagner mainly focuses on Artificial intelligence, Adversarial system, Computer security, Artificial neural network and Robustness.
His Artificial intelligence study combines topics in areas such as Machine learning, Computer vision and Pattern recognition.
His Adversarial system research is multidisciplinary, incorporating perspectives in Computer engineering, Construct, Norm, Simple and Modular design.
In the field of Computer security, his study on Threat model overlaps with subjects such as Stateful firewall.
David Wagner has included themes like Overhead, Inference, Bounded function and Identification in his Artificial neural network study.
His Robustness research incorporates elements of Algorithm, Entropy minimization and k-nearest neighbors algorithm.
Between 2015 and 2021, his most popular works were:
- Towards Evaluating the Robustness of Neural Networks (3170 citations)
- Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples (956 citations)
- Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods (776 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Programming language
- The Internet
David Wagner mostly deals with Adversarial system, Artificial neural network, Artificial intelligence, Computer security and Machine learning.
His Adversarial system research includes elements of Construct and Computer engineering.
Computer security is represented through his Masking, Threat model, Information privacy, Mobile privacy and Privacy software research.
His biological study spans a wide range of topics, including Adversary, Voice Tag, Protocol, Voice command device and Input method.
His Machine learning research integrates issues from Simple and Robustness.
His Robustness study integrates concerns from other disciplines, such as Adversarial machine learning and Malware.
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