Pratap Kishore Varma Vemulamanda

Title: Pratap Kishore Varma Vemulamanda: Innovator in Machine Learning

Introduction

Pratap Kishore Varma Vemulamanda is a notable inventor based in Hyderabad, Telangana, India. He has made significant contributions to the field of machine learning, particularly in the area of time series models. His innovative approach has led to the development of a unique patent that enhances the understanding and application of machine learning techniques.

Latest Patents

Pratap Kishore Varma Vemulamanda holds a patent for a method, computer system, and computer program product aimed at explaining time series machine learning models. The patent outlines a process that includes determining a first order difference in time series input data and historical training data. It also involves performing perturbation of time series input data based on the determined first order difference and historical training data. Furthermore, the method computes the closeness of the determined first order difference in the historical training data to that in the time series input data. The embodiment generates a uniform random sample of the first value input to a time series machine learning model and determines values of other inputs based on this random sample and a random sample from the historical training data first order differences. This innovative approach has the potential to significantly improve the interpretability of machine learning models.

Career Highlights

Pratap is currently employed at International Business Machines Corporation (IBM), where he continues to work on advancing machine learning technologies. His expertise in this domain has positioned him as a valuable asset to his team and the organization as a whole.

Collaborations

He collaborates with talented individuals such as Manish Anand Bhide and Venkata R Madugundu, contributing to a dynamic work environment that fosters innovation and creativity.

Conclusion

Pratap Kishore Varma Vemulamanda is a distinguished inventor whose work in machine learning is paving the way for advancements in the field. His patent on explaining time series models exemplifies his commitment to enhancing the understanding of complex machine learning systems.