Chih-Chieh Yang

Title: Chih-Chieh Yang: Innovator in Molecular Dynamics and Machine Learning

Introduction

Chih-Chieh Yang is a prominent inventor based in White Plains, NY (US). He has made significant contributions to the fields of molecular dynamics and machine learning, holding a total of 3 patents. His innovative work focuses on enhancing computational methods and data processing techniques.

Latest Patents

One of his latest patents is titled "Optical flow based forecasting of binding states in molecular dynamic simulations." This invention involves a computer-implemented method for executing computation tasks in molecular dynamic simulations. It includes identifying a bonding target on a ligand, constructing a protein structure, and rendering images of the ligand. The method also involves subsampling data, computing optical flows, and predicting the probability of binding outcomes based on various viewpoints.

Another notable patent is "Similarity-based hierarchical data loading for machine learning training." This invention addresses the efficient loading of original data for machine learning by dividing it into baseline and difference data. The baseline data is stored in a faster memory device, while the difference data is kept in a slower device. This innovative approach allows for the reconstruction of original data, which can then be used to train machine learning models effectively.

Career Highlights

Chih-Chieh Yang is currently employed at International Business Machines Corporation (IBM), where he continues to develop cutting-edge technologies. His work at IBM has positioned him as a key player in advancing computational methods in various scientific fields.

Collaborations

Chih-Chieh has collaborated with notable coworkers, including Guojing Cong and Eun Kyung Lee. These partnerships have contributed to the success of his projects and the advancement of innovative solutions in their respective fields.

Conclusion

Chih-Chieh Yang is a distinguished inventor whose work in molecular dynamics and machine learning has led to significant advancements in computational methods. His contributions continue to impact the scientific community and drive innovation forward.