AI researcher works to bridge big data, machine learning and computer systems

Artificial intelligence — the systems that information social media feeds, smartphone apps and different facets of our lives — is getting smarter, and Computer Science & Engineering Professor Feng Yan is taking part in a task in that scholarship. Yan’s analysis bridges three key elements of AI: big information, machine learning and computing systems.
That work has garnered Yan a 2022 Nevada Regents’ Rising Researcher Award, certainly one of a number of Regents’ Awards introduced by the Nevada System of Higher Education March 4. In addition to Yan, 4 different teachers acquired the Rising Researcher honor, which carries a $2,000 prize. 
“I’m very honored and grateful to obtain this prestigious award because it is a crucial and encouraging reflection of the high-quality analysis work everybody in my lab has been doing,” Yan stated. “We are inspired by this award and will carry on attempting our greatest to conduct excessive impression and productive analysis within the areas of big information, machine learning and computing systems in addition to interdisciplinary matters that transcend computer science and engineering.”

Specific computer science and engineering analysis Yan and his workforce have been engaged on embody large-scale distributed deep learning, federated learning (a privateness preserving machine-learning approach), serverless computing (a brand new cloud computing paradigm), and broad matters in cloud computing and excessive efficiency computing. Additionally, Yan’s analysis into Machine-Learning-as-a-Service (an rising computing paradigm that gives optimized execution of machine learning duties) was acknowledged by the National Science Foundation with a CAREER Award in 2021.
Yan can also be excited about interdisciplinary analysis and has established collaborations with specialists in areas akin to well being, physics, geography, materials science, mechanical engineering, civil engineering. He has innovated big information and AI-driven approaches for these fields.
He says convergence analysis — a way of fixing advanced issues by way of deep integration throughout disciplines — is vital for reaching success in in the present day’s AI revolution.
“I see a lot of missed alternatives in addition to unaddressed challenges in in the present day’s AI revolution that might be solved by seamlessly integrating big information, machine learning, computing systems and utility area information,” Yan stated.
Yan provides that his analysis has the potential to considerably scale back useful resource and vitality consumption in addition to the carbon footprint related to the fast-growing societal calls for in big information and machine learning. His work additionally gives alternatives for undergraduate and graduate college students by coaching them within the artwork of system optimization mixed with the most recent big information and machine learning information.
Yan credit his college students, colleagues, and many collaborators for his success, and additionally appreciates the constant sturdy helps from Computer Science & Engineering Department, College of Engineering, and the University management.

https://www.unr.edu/x279891.xml

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