Rishab Goel

Title: Rishab Goel and Time-Dependent Machine Learning

Introduction

Rishab Goel is an inventor based in Toronto, Canada, whose patent work addresses the representation of time in machine-learning systems. His invention focuses on helping machine-learning architectures account for periodic patterns in time-dependent data.

Latest Patents

Goel is associated with 1 patent, titled “System and Method for Time-Dependent Machine Learning Architecture.” The patent describes techniques for decomposing time as an input to machine learning, including a vector representation of continuous time that uses a periodic function with learnable frequency and phase-shift parameters. This representation can help track periodic behavior in features and modify time inputs within machine-learning architectures. The described methods aim to improve model accuracy and convergence speed while reducing training epochs and computational resource requirements.

Career Highlights

Goel works at Royal Bank of Canada. His patent reflects an application of machine-learning research to the technical challenge of modeling time-dependent behavior.

Collaborations

Goel’s listed coworkers include Sepehr Eghbali and Janahan Mathuran Ramanan. Their shared professional context connects Goel’s work to a broader team environment.

Conclusion

Rishab Goel’s patent presents a time-aware approach to machine learning that seeks to improve model performance and computational efficiency.