NEC Laboratories America, Inc.

Our #internship opportunities for Summer 2026 are now available! We are looking for #intern students pursuing advanced degrees in Computer Science and Electrical Engineering. You will have the opportunity to quickly become part of a project team applying cutting-edge technology to industry-leading concepts. We currently have available #intern positions in: ☑️ Data Science & System Security (Princeton, NJ) ☑️ Integrated Systems (Princeton, NJ) ☑️ Media Analytics (San Jose, CA) ☑️ Machine Learning (Princeton, NJ) ☑️ Optical Networking & Sensing (Princeton, NJ) Apply Today: https://lnkd.in/epqqikVN #internjobs #phdjobs #phdlife 

Wei Cheng, Senior Researcher, Data Science & System Security, will present DISC: Dynamic Decomposition Improves LLM Inference Scaling Poster at #NeurIPS25 in San Diego. Abstract: Inference scaling methods often rely on decomposing problems into steps, followed by sampling and selecting the best next steps. However, these steps and their sizes are typically fixed or depend on domain knowledge. We propose dynamic decomposition, a method that adaptively and automatically breaks down solution and reasoning traces into manageable steps during inference. By allocating compute more effectively, particularly by subdividing challenging steps and sampling them more frequently— dynamic decomposition significantly enhances inference efficiency. Experiments on benchmarks such as APPS, MATH, and LiveCodeBench demonstrate that dynamic decomposition outperforms static approaches, including token-level, sentence-level, and single-step decompositions. These findings highlight the potential of dynamic decomposition to improve a wide range of inference scaling techniques. Authors: Jonathan Light, Rensselaer Polytechnic Institute; Wei Cheng, NEC Laboratories America, Inc.; Wu Yue, Princeton University; Masafumi Oyamada, NEC Corporation; Mengdi Wang, Princeton University; Santiago Paternain, Rensselaer Polytechnic Institute; haifeng Chen, NEC Laboratories America, Inc. Location: Exhibit Hall C,D,E #511 Date and Time: Friday, December 5th from 4:30 p.m. PST — 7:30 p.m. PST Session: NEC TR #: 2025-TR087 Learn more: https://lnkd.in/e2aMxWPp

See Xujiang Zhao, Researcher, Data Science & System Security, present "SolverLLM: Leveraging Test-Time Scaling for Optimization Problem via LLM-Guided Search" Poster at #NeurIPS25. Abstract: Large Language Models (#LLMs) offer promising capabilities for tackling complex reasoning tasks, including optimization problems. However, existing methods either rely on prompt engineering, which leads to poor generalization across problem types, or require costly supervised training. We introduce #SolverLLM, a training-free framework that leverages test-time scaling to solve diverse optimization problems. Rather than solving directly, SolverLLM generates mathematical formulations and translates them into solver-ready code, guided by a novel Monte Carlo Tree Search (MCTS) strategy. To enhance the search process, we modify classical MCTS with (1) dynamic expansion for adaptive formulation generation, (2) prompt backpropagation to guide exploration via outcome-driven feedback, and (3) uncertainty backpropagation to incorporate reward reliability into decision-making. Experiments on six standard benchmark datasets demonstrate that SolverLLM outperforms both prompt-based and learning-based baselines, achieving strong generalization without additional training. Authors: Dong Li, Baylor University; Xujiang Zhao*, NEC Labs America; Linlin Yu, Augusta University; Yanchi Liu, NEC Labs America; Wei Cheng, NEC Labs America; Zhengzhang Chen, NEC Labs America; Carson (Zhong) Chen, Southern Illinois University, Carbondale; Feng Chen, The University of Texas at Dallas; Chen Zhao*, Baylor University; Haifeng Chen, NEC Labs America. *Chen Zhao and Xujiang Zhao are corresponding authors. Location: Exhibit Hall C,D,E #2011 Date and Time: Thursday, December 4th, from 4:30 p.m. to 7:30 p.m. PST Session: NEC TR #: 2025-TR128 Learn more: https://lnkd.in/e2nyFqJj

Our #MediaAnalytics team tackles core challenges in #ComputerVision, pushing the boundaries of 3D scene understanding, human-object interaction, and vision-language modeling. We develop foundational AI systems capable of robust, cross-domain learning—advancing the way machines perceive, interpret, and reason about the world. Our research lays the foundation for more adaptive and intelligent real-world applications. Discover our MA team and explore our #research: https://lnkd.in/eSww_83a

#HappyThanksgiving from NEC Laboratories America! 🍁🦃🍁 We want to share our gratitude for the brilliant researchers, dedicated staff, and inspiring collaborators who make our company a place where innovation thrives. This year brought remarkable breakthroughs across AI, networking, sensing, security, and computing, which were made possible by the curiosity, creativity, and teamwork of our community. To everyone who supports our mission of disruptive innovation, engages with our pioneering research, and helps push the boundaries of what’s possible: thank you. We’re grateful for your partnership and excited for the discoveries ahead. Wishing you and your loved ones a joyful, restful, and meaningful #Thanksgiving.

AI-Aided Transmission Systems with Photonic Signal Processing Project With exponential traffic growth driven by 5G and cloud applications, optical networks powering the Internet backbone will require orders-of-magnitude improvements in capacity and latency while minimizing the carbon footprint of transmission systems. Advanced signal processing approaches aided by artificial intelligence are at the core of our innovation in this area. Some examples include photonic neural network-based nonlinearity compensation, QoT prediction for advanced modulation formats, and novel fiber transmission engineering that optimizes spectrum utilization, maximizes transmission reach, and reduces energy costs. Our recent world-leading achievements include the first silicon photonic neural network chip that compensates fiber nonlinearity for >10,000-km submarine cable, the highest C-band field fiber capacity at 41.5-Tb/s using probabilistic shaping and AI-aided GSNR prediction, and in-service transmission cable health monitoring with > 1,000-km range. Keyword Tags: #opticalnetworks Learn more: https://lnkd.in/eXdNPhYX

Beyond the Permutation Symmetry of Transformers: The Role of Rotation for Model Fusion Publication Date: 7/13/2025 Event: Forty-Second International Conference on Machine Learning (#ICML2025) Reference: pp. 1-17, 2025 Authors: Binchi Zhang, University of Virginia; Zaiyi Zheng, University of Virginia; Zhengzhang Chen, NEC Laboratories America, Inc.; Jundong Li, University of Virginia Abstract: Symmetry in the parameter space of deep neural networks (DNNs) has proven beneficial for various deep learning applications. A well-known example is the permutation symmetry in Multi-Layer Perceptrons (MLPs), where permuting the rows of weight matrices in one layer and applying the inverse permutation to adjacent layers yields a functionally equivalent model. While permutation symmetry fully characterizes the equivalence set for MLPs, its discrete nature limits its utility for transformers. In this paper, we introduce rotation symmetry, a novel form of parameter space symmetry for transformers that generalizes permutation symmetry by rotating parameter matrices in self-attention layers. Unlike permutation symmetry, rotation symmetry operates in a continuous domain, thereby significantly expanding the equivalence set for transformers. Based on this property, we propose a theoretically optimal parameter matching algorithm as a plug-and-play module to enhance model fusion. We evaluate our approach using pre-trained transformers across diverse natural language and vision tasks. Experimental results demonstrate that our rotation symmetry based matching algorithm substantially improves model fusion, highlighting the potential of parameter space symmetry to facilitate model fusion. Learn more: https://lnkd.in/eTzfdEUc Tags: binchi zhang, convolutional neural networks, data science system security, dsss publication, jundong li, model fusion, parameter space symmetry, transformers, university of virginia, #UVA, zaiyi zheng, zhengzhang chen

Abhishek A. (Aich), Researcher, Media Analytics Department, will present iFinder: Structured Zero-Shot Vision-Based LLM Grounding for Dash-Cam Video Reasoning Poster at #NeurIPS25. Abstract: Grounding large language models (#LLMs) in domain-specific tasks like post-hoc dash-cam driving video analysis is challenging due to their general-purpose training and lack of structured inductive biases. As vision is often the sole modality available for such analysis (i.e., no LiDAR, GPS, etc.), existing video-based vision-language models (V-VLMs) struggle with spatial reasoning, causal inference, and explainability of events in the input video. To this end, we introduce iFinder, a structured semantic grounding framework that decouples perception from reasoning by translating dash-cam videos into a hierarchical, interpretable data structure for LLMs. iFinder operates as a modular, training-free pipeline that employs pretrained vision models to extract critical cues—object pose, lane positions, and object trajectories—which are hierarchically organized into frame- and video-level structures. Combined with a three-block prompting strategy, it enables step-wise, grounded reasoning for the LLM to refine a peer V-VLM’s outputs and provide accurate reasoning. Evaluations on four public dash-cam video benchmarks show that iFinder’s proposed grounding with domain-specific cues—especially object orientation and global context—significantly outperforms end-to-end V-VLMs on four zero-shot driving benchmarks, with up to 39% gains in accident reasoning accuracy. By grounding LLMs with driving domain-specific representations, iFinder offers a zero-shot, interpretable, and reliable alternative to end-to-end V-VLMs for post-hoc driving video understanding. Authors: Manyi Yao, University of California, Riverside; Bingbing Zhuang, NEC Laboratories America; Sparsh Garg, NEC Laboratories America; Amit Roy-Chowdhury, University of California, Riverside; Christian Shelton, University of California, Riverside; Manmohan Chandraker, NEC Laboratories America; University of California, San Diego; Abhishek A., NEC Laboratories America. Location: Exhibit Hall C,D,E #4804 Date and Time: Wednesday, December 3rd, from 4:30 p.m. PST — 7:30 p.m. PST Learn more: https://lnkd.in/e2aMxWPp

The Optical Networking & Sensing (ONS) team at NEC Laboratories America stepped away from the lab this week for a well-earned pho lunch together. A perfect opportunity to recharge, reconnect, and celebrate the innovative work the team has been driving across fiber-optic sensing, next-generation communications, and real-world systems research. Here is to more shared meals, big ideas, and continued innovation from the ONS team: Andrea D'Amico, Azita Nouri, Eric C. Blow, Ezra Ip, Fatih Yaman, Giacomo Borraccini, Giovanni Milione, Jamie Lynn, Jian Fang, Junqiang Hu, Ming-Fang Huang, Philip Ji, Sarper Ozharar, Shaobo Han, Shuji Murakami, Stacey M., Tingfeng Li, Wataru Kohno, Yangmin (Benjamin) D. (Ding), Yaowen Li, Yue Tian, Yue-Kai Huang, Yuheng Chen, Zhuocheng Jiang and our leader, Ting Wang.