2008 - IEEE Fellow For contributions to statistical signal processing in communication systems and networks
His primary areas of study are Computer network, Algorithm, Artificial intelligence, Adversarial system and Estimation theory. Ananthram Swami has included themes like Wireless, Throughput, Sensor node and Markov process in his Computer network study. His Markov process study combines topics in areas such as Cognitive radio and Frequency allocation.
His Algorithm research integrates issues from Electronic engineering, Antenna array, Delay spread and Uniqueness. In his study, Sample, Adversary and Malware is inextricably linked to Machine learning, which falls within the broad field of Artificial intelligence. His Estimation theory study integrates concerns from other disciplines, such as Estimator, Mathematical optimization and Applied mathematics.
Ananthram Swami mainly investigates Algorithm, Computer network, Communication channel, Distributed computing and Mathematical optimization. Ananthram Swami studied Algorithm and Estimator that intersect with Applied mathematics. The study incorporates disciplines such as Cognitive radio, Wireless network and Throughput in addition to Computer network.
His research integrates issues of Electronic engineering and Synchronization in his study of Communication channel. His Distributed computing research is multidisciplinary, incorporating perspectives in Node, Network topology, Wireless sensor network and Wireless ad hoc network. His research on Mathematical optimization frequently links to adjacent areas such as Estimation theory.
His primary areas of investigation include Artificial intelligence, Machine learning, Theoretical computer science, Adversarial system and Distributed computing. His Artificial intelligence study incorporates themes from Flow and Algorithm. The Machine learning study combines topics in areas such as Sample, Training set, Malware and Feature extraction.
His Adversarial system research incorporates elements of Adversary, Robustness and Real time video. His Distributed computing research is multidisciplinary, incorporating elements of Mobile ad hoc network, Network topology, Computer network, Interdependent networks and Optimization problem. Ananthram Swami works in the field of Deep learning, focusing on Adversarial machine learning in particular.
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Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: A POMDP framework
Qing Zhao;Lang Tong;Ananthram Swami;Yunxia Chen.
IEEE Journal on Selected Areas in Communications (2007)
The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot;Patrick McDaniel;Somesh Jha;Matt Fredrikson.
ieee european symposium on security and privacy (2016)
Hierarchical digital modulation classification using cumulants
A. Swami;B.M. Sadler.
IEEE Transactions on Communications (2000)
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
Nicolas Papernot;Patrick McDaniel;Xi Wu;Somesh Jha.
ieee symposium on security and privacy (2016)
Practical Black-Box Attacks against Machine Learning
Nicolas Papernot;Patrick McDaniel;Ian Goodfellow;Somesh Jha.
computer and communications security (2017)
metapath2vec: Scalable Representation Learning for Heterogeneous Networks
Yuxiao Dong;Nitesh V. Chawla;Ananthram Swami.
knowledge discovery and data mining (2017)
A Survey on Trust Management for Mobile Ad Hoc Networks
Jin-Hee Cho;A. Swami;Ing-Ray Chen.
IEEE Communications Surveys and Tutorials (2011)
Joint Design and Separation Principle for Opportunistic Spectrum Access in the Presence of Sensing Errors
Yunxia Chen;Qing Zhao;A. Swami.
IEEE Transactions on Information Theory (2008)
Decentralized cognitive mac for dynamic spectrum access
Q. Zhao;L. Tong;A. Swami.
First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005. (2005)
Cross-layer optimization for energy-efficient wireless communications: a survey
Guowang Miao;Nageen Himayat;Ye (Geoffrey) Li;Ananthram Swami.
communications and mobile computing (2009)
Profile was last updated on December 6th, 2021.
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