This inventor holds 2 USPTO granted patents. Top assignee: Massachusetts Institute of Technology. Active years: 2022-2023.
Company Filing History:
Years Active: 2022-2023
Title: Innovations of Muhammad Amjad
Introduction
Muhammad Amjad is an accomplished inventor based in Cambridge, MA (US). He has made significant contributions to the field of time series analysis, holding 2 patents that showcase his innovative approach to modeling and forecasting.
Latest Patents
One of his latest patents is titled "Model Agnostic Time Series Analysis via Matrix Estimation." This system and method model a time series from missing data by imputing missing values, denoising measured but noisy values, and forecasting future values of a single time series. The invention represents a time series of potentially noisy, partially-measured values of a physical process as a non-overlapping matrix. It has been proven that for several classes of common model functions, the resulting matrix has a low rank or approximately low rank. This allows for the efficient application of matrix estimation techniques, such as singular value thresholding. By applying this technique, a mean matrix is produced that estimates latent values of the physical process at times or intervals corresponding to measurements, with less error than previously known methods. The latent values are denoised if noisy and imputed if missing. Linear regression of the estimated latent values permits forecasting with an error that decreases as more measurements are made.
Career Highlights
Muhammad Amjad is affiliated with the Massachusetts Institute of Technology, where he continues to advance his research and innovations. His work has garnered attention for its practical applications in various fields.
Collaborations
Some of his notable coworkers include Devavrat D Shah and Anish Agarwal, who contribute to the collaborative environment that fosters innovation at the Massachusetts Institute of Technology.
Conclusion
Muhammad Amjad's contributions to time series analysis through his innovative patents demonstrate his expertise and commitment to advancing technology. His work not only enhances forecasting accuracy but also provides valuable tools for handling missing and noisy data.