Dr. Daniel TakabiBased in ATLANTA, GA, United States
Dr. Daniel Takabi received his PhD from University of Pittsburgh and is currently an Associate Professor of Computer Science and Next Generation Scholar at Georgia State University (GSU) with courtesy appointment at the Department of Computer Information Systems, J. Mack Robinson College of Business. He is founding director of the INformation Security and Privacy: Interdisciplinary Research and Education (INSPIRE) Center. He has extensive experience in cybersecurity and privacy research and education; His research is focused on various aspects of cybersecurity and privacy including privacy preserving machine learning, adversarial machine learning, applied cryptography, privacy enhancing technologies (PETs), cyber deception, advanced access control and usable security and privacy.
His work is supported by several grants from National Science Foundation (NSF), National Security Agency (NSA), Department of Defense (DoD), Microsoft, and Nvidia. He has consistently published in top tier conferences and journals with more than 100 publications and 2700+ citations.
He is recipient of several Best Paper and Best Poster awards from top conferences and workshops such as ACM Conference on Data and Application Security and Privacy (CODASPY) and recently received Microsoft Research Investigator Fellowship for his work on privacy preserving machine learning.
He serves on program/ organizing committees of top tier conferences such as IEEE Symposium Security and Privacy, ACM Computer and Communications Security Conference (CCS), Privacy Enhancing Technologies Symposium (PETS). He is also Associate Editor of Cybersecurity & Privacy of the Frontiers in Big Data.
Dr. Takabi has presented training workshops and tutorials on adversarial machine learning and private machine learning at the Annual Computer Security Applications Conference (ACSAC) and the Conference on Computer Vision and Pattern Recognition (CVPR).
ACM Involvement: Dr. Takabi has served/ is serving on organizing/ program committees of several ACM SIGSAC conferences and associated events including but not limited to the ACM Computer and Communications Security Conference (CCS), the annual ACM Conference on Data and Application Security and Privacy (CODASPY), the ACM Symposium on Access Control Models and Technologies (SACMAT), and the Annual Computer Security Applications Conference (ACSAC).
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Secure Privacy preserving Machine Learning
Deep learning with neural networks has become a highly popular machine learning method due to recent breakthroughs in computer vision, speech recognition, and other areas. However, the deep...
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