Haifeng Chen NEC Labs America

Haifeng Chen is the Department Head of the Data Science and System Security Department at NEC Laboratories America. He received his PhD in Computer Engineering from Rutgers University. His research focuses on data mining, system security, and industrial AI. He leads NEC’s work on secure systems, anomaly detection, and AI-driven automation solutions. Based in Princeton, Dr. Chen brings deep expertise in machine learning, anomaly detection, and system health monitoring, with a particular focus on building trustworthy and scalable AI-driven platforms. He has spearheaded numerous high-impact projects, including AI for spacecraft systems, root-cause analysis in cloud environments, and dynamic graph analysis for network security.

His leadership has helped shape the department’s role as a key contributor to NEC’s innovations in fields such as enterprise systems, national defense, and space technology. Dr. Chen holds more than 80 patents and has published over 100 peer-reviewed papers in top-tier venues, earning multiple best paper awards. His contributions extend beyond technical leadership; he serves on program committees for major AI and data science conferences such as SIGKDD and AAAI and has been a panelist for NSF grant reviews. Recognized with NEC’s highest corporate honor, the Contributor of the Year award, Haifeng Chen continues to drive the lab’s efforts in developing real-world, high-impact solutions that merge cutting-edge research with scalable applications across industries.

Posts

Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

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Unsupervised anomaly detection on multi- or high-dimensional data is of great importance in both fundamental machine learning research and industrial applications, for which density estimation lies at the core. Although previous approaches based on dimensionality reduction followed by density estimation have made fruitful progress, they mainly suffer from decoupled model learning with inconsistent optimization goals and incapability of preserving essential information in the low-dimensional space. In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection. Our model utilizes a deep autoencoder to generate a low-dimensional representation and reconstruction error for each input data point, which is further fed into a Gaussian Mixture Model (GMM). Instead of using decoupled two-stage training and the standard Expectation-Maximization (EM) algorithm, DAGMM jointly optimizes the parameters of the deep autoencoder and the mixture model simultaneously in an end-to-end fashion, leveraging a separate estimation network to facilitate the parameter learning of the mixture model. The joint optimization, which well balances autoencoding reconstruction, density estimation of latent representation, and regularization, helps the autoencoder escape from less attractive local optima and further reduce reconstruction errors, avoiding the need of pre-training. Experimental results on several public benchmark datasets show that, DAGMM significantly outperforms state-of-the-art anomaly detection techniques, and achieves up to 14% improvement based on the standard F1 score.

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Co-Regularized Deep Multi-Network Embedding

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Network embedding aims to learn a low-dimensional vector representation for each node in the social and information networks, with the constraint to preserve network structures. Most existing methods focus on single network embedding, ignoring the relationship between multiple networks. In many real-world applications, however, multiple networks may contain complementary information, which can lead to further refined node embeddings. Thus, in this paper, we propose a novel multi-network embedding method, DMNE. DMNE is flexible. It allows different networks to have different sizes, to be (un)weighted and (un)directed. It leverages multiple networks via cross-network relationships between nodes in different networks, which may form many-to-many node mappings, and be associated with weights. To model the non-linearity of the network data, we develop DMNE to have a new deep learning architecture, which coordinates multiple neural networks (one for each input network data) with a co-regularized loss function. With multiple layers of non-linear mappings, DMNE progressively transforms each input network to a highly non-linear latent space, and in the meantime, adapts different spaces to each other through a co-regularized learning schema. Extensive experimental results on real-life datasets demonstrate the effectiveness of our method.

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