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Researcher
- Hsuan-Hao Lu
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This invention utilizes new techniques in machine learning to accelerate the training of ML-based communication receivers.

Technologies are described for privacy-preserving, reduced bias system that identifies faces. The disclosed technologies can be used for national security purposes, or other civilian purposes.

A quantum communication system enabling two-mode squeezing distribution over standard fiber optic networks for enhanced data security.

While there is abundant need spanning many science domains to make public ML algorithms fit on private data, differentially private(DP) ML is constrained by a Pareto front in which the accuracy-privacy tradeoff is often untenable for use.

Real-time tracking and monitoring of radioactive/nuclear materials during transportation is a critical need to ensure safety and security. Current technologies rely on simple tagging, using sensors attached to transport containers, but they have limitations.

We propose an efficient data memory management for the IRIS heterogeneous computing framework along with data transfer policies to select an efficient data transfer between heterogeneous computing units.

An ultrabroadband, polarization-entangled photon source for C+L-band quantum networks, enabling adaptive, high-fidelity entanglement distribution.

We developed a smartphone app that can provide real-time driving speed advisory to help drivers reduce excessive fuel consumption in urban traffic corridors.

MAPSTER is a lightweight software package that automatically searches deployed laptops for geospatial data and complies metadata (GPS coordinates, file size, etc) at a central checkpoint.