Princeton Junction, NJ, United States of America

Cristian Lumezanu

This inventor holds 36 USPTO granted patents and 7 published patent applications and 11 EPO patents, primarily in Anomaly Detection. Top assignees: Nec Corporation, Nec Laboratories America, Inc., Alcatel Lucent. Active years: 2013-2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Cristian Lumezanu. Retrieved from https://idiyas.com/inventor/cristian-lumezanu

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile

Calculating rank
USPTO Granted Patents = 36 

% Patents Active = 75.0

 

Average Co-Inventor Count = 4.4

ph-index = 7

Forward Citations = 274(Granted Patents)


Location History:

  • Princeton, NJ (US) (2014)
  • Arlington, VA (US) (2013 - 2015)
  • East Windsor, NJ (US) (2014 - 2017)
  • Princeton Junction, NJ (US) (2017 - 2023)

Company Filing History:


Years Active: 2013-2026

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Areas of Expertise:
Federated Learning
Anomaly Detection
Time Series Analysis
Network Management
Causal Attention
Multi-modal Data
Sensor Attribution
Audio Scene Recognition
Unsupervised Learning
Data Processing
Virtual Tenant Networks
Network Optimization
36 patents (USPTO):Explore Patents

Title: Cristian Lumezanu: Innovator in System Metric Prediction and Event Identification

Introduction

Cristian Lumezanu, based in Princeton Junction, NJ, has made significant contributions to the fields of computer science and artificial intelligence through his innovative inventions. Holding a total of 32 patents, Lumezanu's work primarily focuses on advanced methodologies in system metric prediction and event identification.

Latest Patents

Among his latest patents, Cristian has developed "Causal attention-based multi-stream RNN for computer system metric prediction and influential events identification based on metric and event logs." This pioneering method enhances the prediction of future metrics by concurrently employing metric logs and event logs. It utilizes a multi-stream recurrent neural network (RNN), which consists of individual RNNs for each metric and each event sequence. The incorporation of an attention mechanism allows for the identification of target events that are most responsible for fluctuations in various metrics.

Another noteworthy patent is his work on "Embedding multi-modal time series and text data." This innovation involves training time series embedding models alongside text embedding models using unsupervised clustering techniques. The result is a method that translates both time series and text into a shared latent space, enhancing the connectivity between different types of data and improving their analysis.

Career Highlights

Cristian Lumezanu has worked with prominent organizations such as NEC Corporation and NEC Laboratories America, Inc., where he applied his deep knowledge in machine learning and data analysis to drive significant advancements in technology. His contributions have had a profound impact on how systems process and predict metrics in dynamic environments.

Collaborations

During his career, Lumezanu has collaborated with notable peers, including Guofei Jiang and Haifeng Chen. These collaborations have helped to strengthen the research and development efforts within their respective fields, enabling innovative solutions to emerge from their collective expertise.

Conclusion

Cristian Lumezanu continues to push the boundaries of innovation in computer science, as evidenced by his numerous patents and collaborations. His work demonstrates a profound understanding of complex data systems and positions him as a leading inventor in the realm of system metric prediction and event identification. The impact of his inventions will likely resonate within the tech community for years to come.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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