Company Filing History:
Years Active: 2020
Title: Mojtaba Solgi: Innovator in Machine Learning and Map Labeling Systems.
Introduction
Mojtaba Solgi is a prominent inventor based in Sammamish, WA (US). He has made significant contributions to the field of machine learning, particularly in the area of map labeling systems. His innovative approach has led to the development of a unique patent that enhances the accuracy and reliability of machine-learned predictions.
Latest Patents
Mojtaba Solgi holds a patent titled "Confidence threshold determination for machine-learned labeling." This patent describes a map labeling system that trains a machine-learned model using a set of training data. The system generates a set of test predictions for various test properties by applying the machine-learned model to a set of testing data. Each prediction includes a confidence score that reflects the model's confidence in its prediction. The system assesses the correctness of each prediction and establishes a relationship between the confidence scores and the correctness of the test predictions. Ultimately, it sets a confidence threshold for the machine-learned model and labels production properties by applying the model to production data. Mojtaba has 1 patent to his name.
Career Highlights
Mojtaba Solgi is currently employed at Uber Technologies, Inc., where he continues to innovate and contribute to advancements in technology. His work focuses on improving machine learning applications, which are crucial for various operational aspects of the company.
Collaborations
Mojtaba collaborates with talented individuals such as Ankit Tandon and Vasudev Parameswaran. Their combined expertise fosters a creative environment that drives innovation and enhances the development of cutting-edge technologies.
Conclusion
Mojtaba Solgi is a key figure in the realm of machine learning and map labeling systems. His contributions through his patent and work at Uber Technologies, Inc. highlight his commitment to advancing technology. His innovative spirit and collaborative efforts continue to shape the future of machine learning applications.