This inventor holds 1 USPTO granted patent. Top assignee: Rutgers, The State University of New Jersey. Active years: 2023.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile
Company Filing History:
Years Active: 2023
Title: Karan Ahuja: Innovator in Biological Particle Classification
Introduction
Karan Ahuja is a notable inventor based in Bluffdale, UT (US). He has made significant contributions to the field of biological particle classification through his innovative patent. His work focuses on enhancing the accuracy of classifying biological particles, which has important implications in medical and research settings.
Latest Patents
Karan Ahuja holds a patent titled "Use of multi-frequency impedance cytometry in conjunction with machine learning for classification of biological particles." This patent provides methods and systems for classifying biological particles, such as blood cells, microbes, and circulating tumor cells (CTCs). By utilizing impedance flow cytometry, particularly multi-frequency impedance cytometry, along with supervised machine learning, his methods demonstrate improved accuracy in the classification of these biological entities.
Career Highlights
Karan Ahuja is affiliated with Rutgers, The State University of New Jersey, where he continues to advance his research and innovations. His work is pivotal in bridging the gap between technology and biological sciences, showcasing the potential of machine learning in medical diagnostics.
Collaborations
Karan collaborates with esteemed colleagues, including Mehdi Javanmard and Jianye Sui. Their combined expertise enhances the research and development of innovative solutions in the field of biological particle classification.
Conclusion
Karan Ahuja's contributions to the field of biological particle classification through his patent and collaborative efforts highlight the importance of innovation in advancing medical technology. His work exemplifies the intersection of machine learning and biological sciences, paving the way for future advancements.
Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile
