Deepanjan Chakraborti

Title: Deepanjan Chakraborti: Innovator in Latent Dirichlet Allocation Model Accuracy

Introduction

Deepanjan Chakraborti is a notable inventor based in Kolkata, India. He has made significant contributions to the field of data modeling, particularly in maintaining the accuracy of Latent Dirichlet Allocation (LDA) models. His innovative approach addresses the challenges of model drift, ensuring that LDA models remain effective and reliable.

Latest Patents

Chakraborti holds a patent for a method and system designed to maintain the accuracy of LDA models. The patent, titled "Method and system for maintaining latent Dirichlet allocation model accuracy," outlines a technique for counteracting model drift. This method leverages LDA classification and vector algebra to detect potential degradations in model performance. It involves measuring cosine similarities of respective topics between two LDA models and determining topic match entropy to assess whether retraining is necessary.

Career Highlights

Deepanjan Chakraborti is currently employed at J.P. Morgan Chase Bank, N.A. His work at the bank allows him to apply his expertise in data modeling and analytics, contributing to the financial sector's understanding of complex data structures.

Collaborations

Chakraborti has collaborated with talented individuals such as Luke Meier and Brandon Chihkai Yang. These collaborations have likely enriched his work and fostered innovative solutions in the field of data modeling.

Conclusion

Deepanjan Chakraborti's contributions to the field of Latent Dirichlet Allocation model accuracy exemplify the importance of innovation in data science. His patent and work at J.P. Morgan Chase Bank highlight his commitment to advancing technology in meaningful ways.