Princeton, NJ, United States of America

Yanjun Qi

USPTO Granted Patents = 7 

Average Co-Inventor Count = 2.7

ph-index = 2

Forward Citations = 73(Granted Patents)


Company Filing History:


Years Active: 2013-2015

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7 patents (USPTO):Explore Patents

Title: Yanjun Qi: Innovator in Statistical Modeling and Machine Learning

Introduction

Yanjun Qi is a prominent inventor based in Princeton, NJ (US), known for his contributions to statistical modeling and machine learning. He holds a total of 7 patents, showcasing his innovative approach to complex data analysis and feature interactions.

Latest Patents

Among his latest patents is the "Sparse higher-order Markov random field," which provides systems and methods for identifying combinatorial feature interactions. This invention captures statistical dependencies between categorical variables, storing them in a computer-readable medium. The model selection is based on these statistical dependencies using a neighborhood estimation strategy, which generates high-order feature interactions and optimizes likelihood functions. Additionally, a damped mean-field approach is applied to obtain parameters of a Markov random field (MRF), resulting in a sparse high-order semi-restricted MRF that models indirect long-range dependencies between feature groups.

Another significant patent is the "Latent factor dependency structure determination," which introduces a general learning framework for computer implementation. This framework induces sparsity on the undirected graphical model imposed on latent factors. The latent factor model SLFA is presented as a matrix factorization problem with a special regularization term that encourages collaborative reconstruction. This model allows for simultaneous learning of lower-dimensional data representations while explicitly modeling pairwise relationships between latent factors. An online learning algorithm is also disclosed, making the model suitable for large-scale learning problems.

Career Highlights

Yanjun Qi has worked with notable organizations, including NEC Laboratories America, Inc. His experience in these institutions has contributed to his expertise in statistical modeling and machine learning.

Collaborations

Throughout his career, Yanjun has collaborated with talented individuals such as Bing Bai and Xi Chen, enhancing his research and development efforts.

Conclusion

Yanjun Qi's innovative work in statistical modeling and machine learning has led to significant advancements in understanding complex data interactions. His contributions continue to influence the field and inspire future innovations.

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