Kwan-Liu Ma Receives Prestigious Honor from Association for Computing Machinery
For leadership and contributions to large-scale data visualization, the Association for Computing Machinery, or ACM, has named Distinguished Professor Kwan-Liu Ma to its 2023 cohort of fellows.
The ACM Fellow is the international association's most prestigious member grade. It is reserved for the top one percent of society members, recognizing outstanding contributions in computing and information technology or service to ACM and the larger computing community.
"This year's inductees include the inventor of the World Wide Web, the 'godfathers' of AI, and other colleagues whose contributions have all been important building blocks in forming the digital society that shapes our modern world," said Yannis Ioannidis, president of ACM.
Ma's research has transformed the visualization of big data with high-performance computing, advanced computer graphics, human-computer interaction and machine learning. His work has allowed scientists to see their data with a higher level of clarity and has left an indelible mark on diverse fields of study, from cybersecurity, supercomputing and manufacturing to healthcare and social science.
"It's a great honor to receive this prestigious recognition from the global computer science community," Ma said. "I would like to express my sincere appreciation to the students and collaborators I have the privilege to work with and the unique opportunities I have received to delve into the forefront of data-driven science and engineering challenges. Moving forward, I will continue pushing the boundaries of research innovation in data visualization."
In addition to Ma, Sein Peisert received the ACM Distinguished Member honor, celebrating longstanding members who have significantly advanced computing, fostered innovation across various fields, and improvised computer science education. Peisert is an adjunct professor of computer science at the University of California, Davis, and a senior scientist at Lawrence Berkeley National Laboratory who focuses on developing techniques that allow for secure and privacy-preserving analysis of scientific data.
