Soumen Pachal

Title: Innovations of Soumen Pachal in Public Transit and Time-Series Prediction

Introduction

Soumen Pachal is an accomplished inventor based in Chennai, India. He has made significant contributions to the fields of public transit and time-series prediction, holding a total of 3 patents. His work addresses critical challenges faced in these areas, particularly in the context of developing countries.

Latest Patents

One of Soumen Pachal's latest patents is titled "Systems and methods for public transit arrival time prediction." This invention tackles the variability in public transit arrival times caused by various factors such as seasonality, dwell times at bus stops, and traffic conditions. The system employs recurrent neural networks (RNNs) to predict bus arrival times in real-time, incorporating spatial and temporal correlations. A Gated Recurrent Unit (GRU) based Encoder-Decoder model is utilized, which enhances the accuracy of predictions by considering additional synchronized inputs from previous trips.

Another notable patent is "Methods and systems for time-series prediction under missing data using joint impute and learn technique." This invention addresses the limitations of existing time-series prediction techniques that often overlook immediate observed values when imputing missing data. The system identifies missing data values and calculates their importance using a feed-forward neural network, ultimately providing a more accurate prediction of missing values in time-series data.

Career Highlights

Soumen Pachal is currently employed at Tata Consultancy Services Limited, where he continues to innovate and develop solutions that enhance public transit systems and data prediction methodologies. His work is instrumental in improving the efficiency and reliability of transit services, particularly in urban environments.

Collaborations

Soumen collaborates with talented professionals such as Avinash Achar and Nancy Bhutani, contributing to a dynamic work environment that fosters innovation and creativity.

Conclusion

Soumen Pachal's contributions to public transit and time-series prediction exemplify the impact of innovative thinking in addressing real-world challenges. His patents not only enhance the accuracy of predictions but also pave the way for improved transit systems in developing regions.