Company Filing History:
Years Active: 2015
Title: Suman Sundaresh: Innovator in Scientific Data Processing
Introduction
Suman Sundaresh is a notable inventor based in Cupertino, California, recognized for his innovative contributions to the field of scientific data processing. With a focus on creating efficient systems for handling complex biological and chemical data, his work has significant implications for research in various scientific disciplines.
Latest Patents
Suman Sundaresh holds a patent for the invention titled "Categorization and filtering of scientific data." This patent involves methods, systems, and apparatus designed to capture and integrate large-scale data from high-throughput biological and chemical assay platforms. His invention provides a robust meta-analysis infrastructure allowing researchers to perform queries across numerous studies and experiments. This is particularly useful for navigating and filtering vast amounts of experimental data efficiently, ensuring that scientists can easily retrieve the most relevant information for their specific research queries.
Career Highlights
Suman has made substantial contributions during his tenure at NextBio, a company dedicated to optimizing data use in life sciences. His expertise in data organization and query systems has made a significant impact, improving how researchers interact with large datasets to derive meaningful insights.
Collaborations
Throughout his career, Suman has collaborated with esteemed colleagues, including Ilya Kupershmidt and Qiaojuan Jane Su. Together, they have worked on advancing the systems and methodologies that facilitate more effective data analysis in the scientific community, showcasing the power of teamwork in innovation.
Conclusion
Suman Sundaresh stands out as a pioneering inventor in the realm of scientific data processing. His patent on categorizing and filtering scientific data exemplifies the essential role that innovative technologies play in enhancing research capabilities. As a member of NextBio, his contributions continue to shape the future of data application in biological and chemical research.