Tathagata Sengupta

Title: Tathagata Sengupta: Innovator in Keyword Bid Optimization

Introduction

Tathagata Sengupta is a prominent inventor based in Bengaluru, India. He has made significant contributions to the field of machine learning and keyword bid optimization, holding a total of four patents. His innovative work focuses on enhancing the efficiency of keyword bidding processes in digital marketing.

Latest Patents

One of Tathagata Sengupta's latest patents is titled "Keyword bids determined from sparse data." This invention describes a portfolio optimization platform that identifies low-impression keywords within a keyword portfolio. The platform trains a machine learning model to generate bids for these low-impression keywords using historical data from a search engine. Specifically, it employs an algorithm suited for training with sparse data, such as a temporal difference learning algorithm. In contrast, different models are used to generate bids for keywords that are not classified as low-impression. Once the low-impression keyword model is trained offline, it is deployed online to generate actual bids, which are then submitted to the search engine. The platform continuously updates the low-impression keyword model while it is in use, ensuring optimal performance.

Career Highlights

Tathagata Sengupta has worked with notable companies such as Adobe, Inc. and Visa International Service Association. His experience in these organizations has allowed him to refine his skills in technology and innovation, contributing to his success as an inventor.

Collaborations

Some of Tathagata Sengupta's coworkers include Anirban Basu and Kunal Kumar Jain. Their collaboration has likely fostered a creative environment that encourages innovation and the development of new ideas.

Conclusion

Tathagata Sengupta is a distinguished inventor whose work in keyword bid optimization has the potential to transform digital marketing strategies. His patents reflect a deep understanding of machine learning and its applications in real-world scenarios.