DATA SCIENCE & SYSTEM SECURITY
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PEOPLE
PUBLICATIONS
PATENTS
By leveraging big data and deep learning, in recent years, AI technologies have made significant progress. They have been adopted in many applications, including malware detection, image classification, and stock market prediction. As our society becomes more automated, more and more systems will rely on AI techniques. And instead of augmenting human decisions, some AI systems will make their own decisions and execute autonomously. This is extremely risky especially for mission-critical fields (like homeland security, medical diagnosis, and self-driving vehicles). In fact, the advances in AI outpace efforts to curb its potential hazards, because AI systems are vulnerable due to various attacks and biases.
This project aims to develop innovative system testing and data governance engines to identify AI system vulnerabilities, defend against advanced attacks, improve technical robustness, and mitigate technical unfairness/biases. Our engines leverage fine-grained reliability assessment, generalized robustness enhancement, and hardware-based privacy protection techniques to secure both AI model and data throughout the system development life cycle.
Since AI is being applied to more domains for mission-critical tasks, our engines can offer “AI System Testing and Data Governance” as a service to make businesses in these domains more reliable, trustworthy, transparent, and secure. We are developing new solutions to support a variety of AI systems with different types of data, thus can be applied to an enormous variety of businesses, including autonomous driving, biometric authentication, finance, healthcare, IoT devices, smart factories, and more.
Team Members: Wei Cheng, Zhengzhang Chen, Haifeng Chen
The development of next-generation sequencing (NGS) has enabled the discovery of cancer-specific driver gene alternations, making precision medicine possible. However, accurategenetic testing requires a sufficient amount of tumor cells in the specimen. The evaluation of tumor content ratio (TCR) from
Data crowdsourcing is an increasingly pervasive and lifestyle-changing technology due to the flywheel effect that results from the interaction between the Internet of Things and Cloud Computing. This paper presents the Citizen Science for the Sea with Information Technologies (C4Sea-IT) framework. It
By employing distributed fiber optic sensing (DFOS) technologies, field deployed fiber cables can be utilized as not only communication media for data transmissions but also sensing media for continuously monitoring of the physical phenomenon along the entire route. The fiber can be used to monitor ambient
Distributed fiber optic sensing systems (DFOS) allow deployed fiber cables to be sensing media, not only dedicated function of data transmission. The fiber cable can monitor the ambient environment over wide area for many applications. We review recent field trial results, and show how artificial intelligence
A simplified, system-agnostic NLC algorithm based on a neuron network is proposed to pre-distort symbols at transmitter side to demonstrate ~0.6dB Q improvement after 2800km SMF transmission using 32Gbaud DP-16QAM.
We report on the evolution of the longest segment of FASTER cable at 11,017 km, with 8QAM transponders at 4b/s/Hz spectral efficiency (SE) in service. With offline testing, 6 b/s/Hz is further demonstrated using probabilistically shaped 64QAM, and a novel, low complexity nonlinearity compensation technique
