This inventor holds 3 USPTO granted patents and 1 published patent application. Top assignee: International Business Machines Corporation. Active years: 2015-2021.
Company Filing History:
Years Active: 2015-2021
Title: Claudia Perlich: Innovator in Predictive Analytics
Introduction
Claudia Perlich is a prominent inventor based in Mount Kisco, NY (US). She has made significant contributions to the field of predictive analytics, particularly in understanding social networks. With a total of 3 patents, her work has had a substantial impact on how influence is measured and predicted in digital environments.
Latest Patents
One of Claudia's latest patents focuses on "Predicting influence in social networks." This method, system, and computer program product aim to identify influential users within a social network based on specific criteria. The process involves recognizing a set of users and determining which among them are influential. Various measures are identified as predictors of influence, which are then aggregated to form a composite predictor model. This model is utilized to forecast which users will exert a specified influence in the future, particularly based on the messages they send and how often those messages are re-sent by others.
Career Highlights
Claudia Perlich has built a distinguished career at the International Business Machines Corporation (IBM). Her expertise in data science and machine learning has positioned her as a leader in her field. She has been instrumental in developing innovative solutions that leverage data to drive insights and decision-making.
Collaborations
Throughout her career, Claudia has collaborated with notable colleagues, including Estepan Meliksetian and Prem Melville. These partnerships have fostered a rich environment for innovation and have contributed to the advancement of predictive analytics.
Conclusion
Claudia Perlich's work exemplifies the intersection of technology and social science, making her a key figure in the realm of predictive analytics. Her contributions continue to shape the understanding of influence in social networks, showcasing the power of data-driven insights.