Intelligence Fusion Laboratory
Electrical and Computer Engineering | George Mason University
– Dr. Chen attended the 56th Design Automation Conference (DAC) 2019 at Las Vegas, NV.
– Dr. Chen presented the paper:
“ReForm: Static and Dynamic Resource-Aware DNN Reconfiguration Framework for Mobile Device”;
“MASKER: Adaptive Mobile Security Enhancement against Automatic Speech Recognition in Eavesdropping”.
– One Papers is accepted by the 28th International Joint Conference on Artificial Intelligence (IJCAI) 2019.
“Interpreting and Evaluating Neural Network Robustness”
– Two Papers are accepted by the 28th ACM SigKDD Conference on Knowledge Discovery and Data Mining
(KDD) 2019.
“Deep Learning Alternating Direction Method of Multipliers” is accepted as a regular paper;
“DoPa: A Fast and Comprehensive CNN Defense Methodology against Physical Adversarial Attacks” is accepted as a workshop paper for the Workshop on Artificial Intelligence of Things hosted by the Microsoft Research.
– Dr. Chen was invited to the Comcast Research Lab D.C., Washington D.C.
– Dr. Chen gave the talk “High-Performance and Robust Computing for Artificial Intelligence on Edge”.
– Dr. Chen attended the 25th IEEE International Symposium on High-Performance Computer Architecture (HPCA)
(HPCA) 2019 at Washington D.C.
– Dr. Chen gave an invited talk at the IEEE Workshop on SecArch: Built-in Security-Architecture, Chip, and System.
– “The Software-Hardware Approaches for Deep Learning Security Enhancement”
– Two Papers are accepted by the 56th Design Automation Conference
(DAC) 2019.
“ReForm: Static and Dynamic Resource- Aware DNN Reconfiguration Framework for Mobile Devices”
“MASKER: Adaptive Mobile Security Enhancement against Automatic Speech Recognition in Eavesdropping”
– Zhuwei Qin attended the 24th Asia and South Pacific Design Automation Conference
(ASP-DAC) 2019 at Seoul, South Korea.
– Zhuwei presented three papers:
“HAMPER: High-Performance Adaptive Mobile Security Enhancement against Malicious Speech and Image Recognition”
“REIN: A Robust Training Method for Enhancing Generalization Ability of Neural Networks in Autonomous Driving Systems”
“CAPTOR: A Class Adaptive Filter Pruning Framework for Convolutional Neural Networks in Mobile Applications”
