

Adversarial Machine Learning - Sreevallabh Chivukula, Aneesh
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Présentation Adversarial Machine Learning Format Relié
- Livre InformatiqueAuteur(s) : Sreevallabh Chivukula, Aneesh - Yang, Xinghao - Zhou, Wanlei - Liu, Wei - Liu, BoEditeur : Springer International Publishing AgLangue : AnglaisParution : 01/03/2023Format :...
Biographie: Dr. Aneesh Sreevallabh Chivukula is currently an Assistant Professor in the Department of Computer Science & Information Systems at the Birla Institute of Technology and Science (BITS), Pilani, Hyderabad Campus. He has a PhD in data analytics and machine learning from the University of Technology Sydney (UTS), Australia. He holds a Master Of Science by Research in computer science and artificial intelligence from the International Institute of Information Technology Hyderabad, India. His research interests are in Computational Algorithms, Adversarial Learning, Machine Learning, Deep Learning, Data Mining, Game Theory, and Robust Optimization. He has taught subjects on advanced analytics and problem solving at UTS. He has been teaching academic courses on computer science at BITS, Pilani. He has industry experience in engineering, R&D, consulting at research labs and startup companies. Hehas developed enterprise solutions across the value chains in the open source, Cloud, & Big Data markets. Dr. Wei Liu is the Director of Future Intelligence Research Lab, and an Associate Professor in Machine Learning, in the School of Computer Science, the University of Technology Sydney (UTS), Australia. He is a core member of the UTS Data Science Institute. Wei obtained his PhD degree in Machine Learning research at the University of Sydney (USyd). His current research focuses are adversarial machine learning, game theory, causal inference, multimodal learning, and natural language processing. Wei's research papers are constantly published in CORE A*/A and Q1 (i.e., top-prestigious) journals and conferences. He has received 3 Best Paper Awards. Besides, one of his first-authored papers received the Most Influential Paper Award in the CORE A Ranking conference PAKDD 2021. He was a nominee for the Australian NSW Premier's Prizes for Early Career Researcher Award in 2017. He has obtained more than $2 million government competitive and industry research funding in the past six years. Dr. Bo Liu is currently a Senior Lecturer with the University of Technology Sydney, Australia. His research interests include cybersecurity and privacy, location privacy and image privacy, privacy protection and machine learning, wireless communications and networks. He is an IEEE Senior Member and Associate Editor of IEEE Transactions on Broadcasting. Dr. Wanlei Zhou received the Ph.D. degree from Australian National University, Canberra, ACT, Australia, in 1991, all in computer science and engineering, and the D.Sc. degree from Deakin University, Melbourne, VIC, Australia, in 2002. He is currently a Professor and the Head of School of Computer Science at the University of Technology Sydney. He served as a Lecturer with the University of Electronic Science and Technology of China, a System Programmer with Hewlett Packard, Boston, MA, USA, and a Lecturer with Monash University, Melbourne, VIC, Australia, and the National University of Singapore, Singapore. He has published over 300 papers in refereed international journals and refereed international conferences proceedings. His research interests include distributed systems, network security, bioinformatics, and e-Learning. Dr. Wanlei was the General Chair/Program Committee Chair/Co-Chair of a number of international conferences, including ICA3PP, ICWL, PRDC, NSS, ICPAD, ICEUC, and HPCC.
Dr. Xinghao Yang is currently an Associate Professor at the China University of Petroleum. He has a Ph.D. degree in advanced analytics from the University of Technology Sydney, Sydney, NSW, Australia. His research interests include multiview learning and adversarial machine learning with publications on information fusion and information sciences.
Sommaire:
Adversarial Machine Learning.- Adversarial Deep Learning.- Security and Privacy in Adversarial Learning.- Game-Theoretical Attacks with Adversarial Deep Learning Models.- Physical Attacks in the Real World.- Adversarial Defense Mechanisms.- Adversarial Learning for Privacy Preservation.
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