Mehdi Rezagholizadeh

This inventor holds 13 USPTO granted patents and 2 published patent applications, plus 2 CIPO patents and 1 EPO patent. Top assignees: Huawei Technologies Co., Limited, Huawei Technologies Co., Ltd.. Active years: 2021-2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Mehdi Rezagholizadeh. Retrieved from https://idiyas.com/inventor/mehdi-rezagholizadeh

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

USPTO Granted Patents = 13 (Patent Applications = 2)

% Patents Active = 76.9

CIPO Patents = 2

EPO Patents = 1 (Patent Applications = 4)

Average Co-Inventor Count = 2.9

ph-index = 2

Forward Citations = 17(Granted Patents)

Company Filing History:

9 out of 27,557

Huawei Technologies Co., Limited

patents

3 out of 2,332

Huawei Technologies Co., Ltd.

patents

where one patent can have more than one assignee

Years Active: 2021-2026

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13 patents (USPTO):Sort by Forward CitationsExplore Patents

1. 12670404 - Method and system for training a neural network model using knowledge distillation (2026-06-30),

2. 12619881 - Method and system for training a neural network model using adversarial learning and knowledge distillation (2026-05-05),

3. 12619880 - Methods, devices and media for re-weighting to improve knowledge distillation (2026-05-05),

4. 12541689 - Knowledge distillation by utilizing backward pass knowledge in neural networks (2026-02-03),

5. 12505351 - Methods, devices and media for improving knowledge distillation using intermediate representations (2025-12-23),

6. 12236337 - Methods and systems for compressing a trained neural network and for improving efficiently performing computations of a compressed neural network (2025-02-25),

7. 11914670 - Methods and systems for product quantization-based compression of a matrix (2024-02-27),

8. 11663483 - Latent space and text-based generative adversarial networks (LATEXT-GANs) for text generation (2023-05-30),

9. 11586833 - System and method for bi-directional translation using sum-product networks (2023-02-21),

10. 11423282 - Autoencoder-based generative adversarial networks for text generation (2022-08-23),

11. 11151334 - Systems and methods for multilingual text generation field (2021-10-19),

12. 11120337 - Self-training method and system for semi-supervised learning with generative adversarial networks (2021-09-14),

13. 11003995 - Semi-supervised regression with generative adversarial networks (2021-05-11),

Title: Innovations of Mehdi Rezagholizadeh

Introduction

Mehdi Rezagholizadeh is a prominent inventor based in Montreal, Canada. He has made significant contributions to the field of neural networks and matrix compression. With a total of eight patents to his name, his work focuses on enhancing the efficiency of computational processes in artificial intelligence.

Latest Patents

Among his latest patents are methods and systems for compressing a trained neural network. These innovations aim to improve the efficiency of computations performed by a compressed neural network. The process involves obtaining data samples from a training dataset and inputting them into a trained neural network to generate predictions. Additionally, he has developed methods for product quantization-based compression of a matrix. This involves partitioning a matrix into submatrices, clustering sub-vectors, and storing centroids and variances for reconstruction.

Career Highlights

Mehdi Rezagholizadeh currently works at Huawei Technologies Co., Limited. His role at the company allows him to apply his expertise in neural networks and matrix compression to real-world applications. His innovative approaches have the potential to significantly impact the efficiency of AI systems.

Collaborations

He collaborates with talented individuals such as Md Akmal Haidar and Alan Do-Omri. These partnerships enhance the scope and depth of his research and development efforts.

Conclusion

Mehdi Rezagholizadeh is a key figure in the field of artificial intelligence, with a focus on neural network and matrix compression technologies. His contributions are paving the way for more efficient computational methods in the industry.

Profile summary based on public USPTO records.