Our Mission
We strive to develop high-quality (efficient and robust) and high-impact (fundamental and general) software and solutions (usually with a high concentration of mathematical and algorithmic insights).
More specifically, our current focus is to develop highly scalable software and solutions for analyzing massive graphs and matrices arising from data analytics and scientific computing.
We believe that the only way to deliver on our promise is to seek innovations in the interplay among theory, algorithms and implementation.
More specifically, our current focus is to develop highly scalable software and solutions for analyzing massive graphs and matrices arising from data analytics and scientific computing.
We believe that the only way to deliver on our promise is to seek innovations in the interplay among theory, algorithms and implementation.
Our Team
Hui Zheng, Ph. D.
Before co-founding LeapLinear in 2016, Hui Zheng had 20 years of R&D experience in the EDA (electronic design automation) industry, amassing deep knowledge of many aspects of building innovative and successful commercial software. A desire to develop high-impact software with more mathematical rigor -- that also appeals to a broader range of industries -- led him to team up with Dr. Zhuo Feng to tackle the problems of analyzing massive graphs and matrices with scalable algorithms and implementation. He received his Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University in 2003. He is an expert in numerical methods, high-performance computing and software development.
|
Zhuo Feng, Ph. D.
Zhuo Feng received the Ph.D. degree in Electrical and Computer Engineering from Texas A&M University, College Station, TX in 2009. He is currently an associate professor at the Department of Electrical and Computer Engineering, Michigan Technological University. In 2016, he co-founded LeapLinear Solutions to commercialize his research breakthroughs in graph analytics and scientific computing. He received a Faculty Early Career Development (CAREER) Award from the National Science Foundation (NSF) in 2014 and a Best Paper Award from ACM/IEEE Design Automation Conference (DAC) in 2013.
|
