Manoj Awasthi

Title: Innovations by Manoj Awasthi

Introduction

Manoj Awasthi is a notable inventor based in Ghaziabad, India. He has made significant contributions to the field of technology, particularly in data analysis and predictive modeling. With a total of 2 patents, Awasthi's work has garnered attention for its innovative approaches to complex problems.

Latest Patents

One of Awasthi's latest patents is titled "Efficient computation of variable predictiveness." This invention relates to the efficient computation of variable predictiveness, allowing users, such as digital marketers, to receive real-time indications of variable predictiveness based on their queries. The method involves computing mutual information offline and generating conditional mutual information in real time, effectively reducing the number of data access attempts and calculations performed by processors.

Another significant patent is "Identifying significant anomalous segments of a metrics dataset." In this invention, Awasthi's approach involves accessing a metrics dataset that indicates data network activity. The processor identifies anomalous segments within the dataset, generating a data graph that represents these segments and their relationships. This innovation allows for the ranking of anomalous segments based on their trends, providing valuable insights into data network behavior.

Career Highlights

Manoj Awasthi is currently employed at Adobe, Inc., where he continues to develop innovative solutions in data analysis and predictive modeling. His work at Adobe has positioned him as a key contributor to advancements in technology.

Collaborations

Awasthi collaborates with talented professionals in his field, including Nitin Goel and Kapil Malik. These collaborations enhance the innovative potential of his projects and contribute to the overall success of their initiatives.

Conclusion

Manoj Awasthi's contributions to technology through his patents reflect his commitment to innovation and problem-solving. His work continues to influence the field of data analysis, making significant strides in how predictive modeling is approached.