NEC Laboratories America conducts advanced research in AI, networking, optical systems, and security. Its mission is to invent and apply new technologies that shape the future of information and communication systems. NEC Labs America serves as a central hub for innovation across the NEC global research ecosystem, advancing core technologies in AI, networking, sensing, and quantum systems. Please read about our latest news and collaborative publications with NEC Laboratories America.

Posts

NEC Laboratories America 2025: A Year of Disruptive Innovation

/in /by

As 2025 comes to a close, NEC Laboratories America reflects on a year defined by scientific breakthroughs, global collaboration, and real-world impact. Our researchers advanced the state of the art across AI, optical networking and sensing, system security, and multimodal analytics, while expanding our intellectual property portfolio and presence at the world’s leading conferences. This year-in-review highlights the people, ideas, and partnerships that shaped 2025 and set the foundation for continued innovation in the year ahead.

Read more

NEC Laboratories America: Celebrating 23 Years of Research Innovation!

/in /by

NEC Laboratories America celebrates 23 years of pioneering research and innovation. Emerging from the 2002 merger of NEC Research Institute and NEC C&C Research Laboratories, NECLA has become the U.S. hub for NEC’s global R&D network. Under the leadership of Dr. Christopher White, NECLA bridges the gap between scientific discovery and market-ready technology. With groundbreaking work in AI, optical networking, sensing, and system architecture, our teams continue to drive world-class innovation that shapes industries and connects the world.

Read more

Reducing Hallucinations of Medical Multimodal Large Language Models with Visual Retrieval-Augmented Generation

/in /by

Multimodal Large Language Models (MLLMs) have shown impressive performance in vision and text tasks. However, hallucination remains a major challenge, especially in fields like healthcare where details are critical. In this work, we show how MLLMs may be enhanced to support Visual RAG (V-RAG), a retrieval-augmented generation framework that incorporates both text and visual data from retrieved images. On the MIMIC-CXR chest X-ray report generation and Multicare medical image caption generation datasets, we show that Visual RAG improves the accuracy of entity probing, which asks whether a medical entities is grounded by an image. We show that the improvements extend both to frequent and rare entities, the latter of which may have less positive training data. Downstream, we apply V-RAG with entity probing to correct hallucinations and generate more clinically accurate X-ray reports, obtaining a higher RadGraph-F1 score.

Read more

TGNet: Learning to Rank Nodes in Temporal Graphs

/in /by

Node ranking in temporal networks are often impacted by heterogeneous context from node content, temporal, and structural dimensions. This paper introduces TGNet , a deep-learning framework for node ranking in heterogeneous temporal graphs. TGNet utilizes a variant of Recurrent Neural Network to adapt context evolution and extract context features for nodes. It incorporates a novel influence network to dynamically estimate temporal and structural influence among nodes over time. To cope with label sparsity, it integrates graph smoothness constraints as a weak form of supervision. We show that the application of TGNet is feasible for large-scale networks by developing efficient learning and inference algorithms with optimization techniques. Using real-life data, we experimentally verify the effectiveness and efficiency of TGNet techniques. We also show that TGNet yields intuitive explanations for applications such as alert detection and academic impact ranking, as verified by our case study.

Read more