Mayank Sharma

Title: Innovations and Contributions of Inventor Mayank Sharma

Introduction

Mayank Sharma is a prominent inventor based in Bengaluru, India. He has made significant contributions to the field of technology, particularly in software development and machine learning. With a total of seven patents to his name, Sharma's work focuses on enhancing application performance and training machine learning models.

Latest Patents

One of Mayank Sharma's latest patents is titled "Determining non-functional requirements (NFR) from a run time environment and incorporating into a development cycle." This computer-implemented method analyzes application details associated with various timestamp intervals. It responds to application failure events and sub-par runtime performance by identifying Non-Functional Requirement (NFR) issues. The method collects pattern information and applies it to a weightage algorithm to create test cases that address coverage gaps.

Another notable patent is "Systems and methods for training machine learning models using unlabeled electrocardiogram data." This system trains machine learning models with unlabeled electrocardiogram signals. It involves creating overlapping temporal patches from ECG data, masking certain patches, and pretraining an ECG machine learning model to predict the masked patches. This innovative approach adjusts model parameters based on predictions and labeled training data.

Career Highlights

Mayank Sharma has worked with several esteemed companies, including Wipro Limited and IBM. His experience in these organizations has allowed him to develop and refine his skills in software engineering and machine learning. His contributions have been instrumental in advancing the technologies used in these companies.

Collaborations

Throughout his career, Mayank has collaborated with talented individuals such as Matheen Shaik and Chiranjeevi Bhagavan. These collaborations have fostered innovation and creativity in his projects.

Conclusion

Mayank Sharma's innovative patents and career achievements highlight his significant impact on technology and machine learning. His work continues to inspire future advancements in the field.