Company Filing History:
Years Active: 2023
Title: Nariman Farsad: Innovator in Machine Learning for Touch Sensors
Introduction
Nariman Farsad is an accomplished inventor based in Redwood City, CA. He has made significant contributions to the field of machine learning, particularly in the area of multitouch sensors. His innovative approach focuses on separating noise from signal, enhancing the accuracy and reliability of touch data.
Latest Patents
Nariman Farsad holds a patent for a "System and machine learning method for separating noise and signal in multitouch sensors." This invention addresses the challenge of noise in touch data by employing advanced machine learning techniques. Specifically, it utilizes gated recurrent units and convolutional neural networks to effectively mitigate noise. The design allows for a series arrangement where the output of the gated recurrent unit serves as input to the convolutional neural network. This dual-stage approach significantly improves the quality of touch data by removing noise caused by various components of electronic devices.
Career Highlights
Nariman Farsad is currently associated with Apple Inc., where he continues to push the boundaries of technology through his innovative work. His expertise in machine learning and sensor technology has positioned him as a valuable asset in the tech industry. With a focus on practical applications, he aims to enhance user experiences through improved touch sensitivity and accuracy.
Collaborations
Throughout his career, Nariman has collaborated with talented individuals such as Baboo V Gowreesunker and Behrooz Shahsavari. These partnerships have fostered a creative environment that encourages the exchange of ideas and the development of cutting-edge technologies.
Conclusion
Nariman Farsad's contributions to machine learning and multitouch sensor technology exemplify the spirit of innovation. His patent and work at Apple Inc. highlight his commitment to advancing technology for better user experiences. His efforts in separating noise from signal in touch data are paving the way for more reliable electronic devices.