Apply for a Summer 2026 Internship

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Our exciting internship opportunities for Summer 2026 are now available. We are looking for students pursuing advanced degrees in Computer Science and Electrical Engineering. Internships are typically 3 months long in duration. The benefits of working for us include the opportunity to quickly become part of a project team applying cutting-edge technology to industry-leading concepts. We have opportunities in Data Science & System Security, Integrated Systems, Media Analytics, Machine Learning, and Optical Networking & Sensing.

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NeurIPS 2025 in San Diego from November 30th to December 5th, 2025

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NEC Laboratories America is heading to San Diego for NeurIPS 2025, where our researchers will present cutting-edge work spanning optimization, AI systems, language modeling, and trustworthy machine learning. This year’s lineup highlights breakthroughs in areas like multi-agent coordination, scalable training, efficient inference, and techniques for detecting LLM-generated text. Together, these contributions reflect our commitment to advancing fundamental science while building real-world solutions that strengthen industry and society. We’re excited to join the global AI community in San Diego from November 30 to December 5 to share our latest innovations.

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xTime: Extreme Event Prediction with Hierarchical Knowledge Distillation and Expert Fusion

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Extreme events frequently occur in real-world time series and often carry significant practical implications. In domains such as climate and healthcare, these events, such as floods, heatwaves, or acute medical episodes, can lead to serious consequences. Accurate forecasting of such events is therefore of substantial importance. Most existing time series forecasting models are optimized for overall performance within the prediction window, but often struggle to accurately predict extreme events, such as high temperatures or heart rate spikes. The main challenges are data imbalance and the neglect of valuable information contained in intermediate events that precede extreme events. In this paper, we propose xTime, a novel framework for extreme event forecasting in time series. xTime leverages knowledge distillation to transfer information from models trained on lower-rarity events, thereby improving prediction performance on rarer ones. In addition, we introduce a MoE mechanism that dynamically selects and fuses outputs from expert models across different rarity levels, which further improves the forecasting performance for extreme events. Experiments on multiple datasets show that xTime achieves consistent improvements, with forecasting accuracy on extreme events improving from 3% to 78%.

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Correlation-aware Online Change Point Detection

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Change point detection aims to identify abrupt shifts occurring at multiple points within a data sequence. This task becomes particularly challenging in the online setting, where different types of change can occur, including shifts in both the marginal and joint distributions of the data. In this paper, we address these challenges by tracking the Riemannian geometry of correlation matrices, allowing Riemannian metrics to compute the geodesic distance as an accurate measure of correlation dynamics.We introduce Rio-CPD, a correlation-aware online change point detection framework that integrates the Riemannian geometry of the manifold of symmetric positive definite matrices with the cumulative sum (CUSUM) statistic for detecting change points. Rio-CPD employs a novel CUSUM design by computing the geodesic distance between current observations and the Fréchet mean of prior observations. With appropriate choices of Riemannian metrics, Rio-CPD offers a simple yet effective and computationally efficient algorithm. We also provide a theoretical analysis on standard metrics for change point detection within Rio-CPD. Experimental results on both synthetic and real-world datasets demonstrate that Rio-CPD outperforms existing methods on detection accuracy, average detection delay, and efficiency.

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Quantitative Bounds for Length Generalization in Transformers

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We study the problem of length generalization (LG) in transformers: the ability of a model trained on shorter sequences to maintain performance when evaluated on much longer, previously unseen inputs. Prior work by Huang et al. (2025) established that transformers eventually achieve length generalization once the training sequence length exceeds some finite threshold, but left open the question of how large it must be. In this work, we provide the first quantitative bounds on the required training length for length generalization to occur. Motivated by previous empirical and theoretical work, we analyze LG in several distinct problem settings: error control vs. average error control over an input distribution, infinite-precision softmax attention vs. finite-precision attention (which reduces to an argmax) in the transformer, and one- vs. two-layer transformers. In all scenarios, we prove that LG occurs when the internal behavior of the transformer on longer sequences can be “simulated” by its behavior on shorter sequences seen during training. Our bounds give qualitative estimates for the length of training data required for a transformer to generalize, and we verify these insights empirically. These results sharpen our theoretical understanding of the mechanisms underlying extrapolation in transformers, and formalize the intuition that richer training data is required for generalization on more complex tasks.

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Scalable Photonic Neurons for High-speed Automatic Modulation Classification

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Automatic modulation classification (AMC) is becoming increasingly critical in the context of growing demands for ultra-wideband, low-latency signal intelligence in 5G/6G systems, with photonics addressing the bandwidth and real-time adaptability limitations faced by traditional radio-frequency (RF) electronics. This paper presents the first experimental photonicimplementation of AMC, achieved through a fully functional photonic neural network built from scalable microring resonators that co-integrate electro-optic modulation and weighting. Thiswork also represents a system-level deployment of such compact photonic neurons in a real photonic neural network, demonstrating the significant potential of photonic computing forlarge-scale, complex RF intellegence for next-generation wireless communication systems.

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Neuromorphic Photonics-Enabled Near-Field RF Sensing with Residual Signal Recovery and Classification

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We present near-field radio-frequency (RF) sensing using microwave photonic canceler (MPC) for residual signal recovery and neuromorphic photonic recurrent neural network (PRNN)chip and FPGA hardware to implement machine learning for high-bandwidth and low-latency classification.

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Eric Blow Presents at the IEEE Photonics Conference Singapore on November 10th & 13th

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Eric Blow of NEC Labs will address how machine-learning methods applied to distributed acoustic-sensing data can monitor facility perimeters and detect intrusion via walk, dig, or drive events over buried optical fibre—for example achieving ~90% classification accuracy. Later in the week he will explore neuromorphic photonic RF sensing combining silicon photonics with FPGA-based recurrent neural networks, and his intern Yuxin Wang will present a finalist paper on scalable photonic neurons for automatic modulation classification.

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NEC Laboratories America: Celebrating 23 Years of Research Innovation!

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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.

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Sound Event Classification meets Data Assimilation with Distributed Fiber-Optic Sensing

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Distributed Fiber-Optic Sensing (DFOS) is a promising technique for large-scale acoustic monitoring. However, its wide variation in installation environments and sensor characteristics causes spatial heterogeneity. This heterogeneity makes it difficult to collect representative training data. It also degrades the generalization ability of learning-based models, such as fine-tuning methods, under a limited amount of training data. To address this, we formulate Sound Event Classification (SEC) as data assimilation in an embedding space. Instead of training models, we infer sound event classes by combining pretrained audio embeddings with simulated DFOS signals. Simulated DFOS signals are generated by applying various frequency responses and noise patterns to microphone data, which allows for diverse prior modeling of DFOS conditions. Our method achieves out-of-domain (OOD) robust classification without requiring model training. The proposed method achieved accuracy improvements of 6.42, 14.11, and 3.47 percentage points compared with conventional zero-shot and two types of fine-tune methods, respectively. By employing the simulator in the framework of data assimilation, the proposed method also enables precise estimation of physical parameters from observed DFOS signals.

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