Using groundbreaking artificial intelligence (AI) technology, a University of Kentucky researcher is developing a machine learning pipeline with the goal of improving our quality of life.
Hana Khamfroush, Ph.D., associate professor in the Department of Computer Science in the UK Stanley and Karen Pigman College of Engineering, recently received the prestigious National Science Foundation (NSF) Faculty Early Career Development (CAREER) Award. The NSF will support Khamfroush with $624,716 over five years for her research involving pre-processing data, while maintaining privacy, so that it can be trained for use in machine learning models for smart cities applications.
With eco-friendly practices as a priority, smart cities use data and technology to create more livable and sustainable urban environments.
“I think we are all used to the internet on computers and smartphones. But when we talk about the ‘internet of things,’ we are looking at every possible device becoming connected devices to the internet,” said Khamfroush. “For example, we can have a smart thermometer that can just sense when we are out of the home to reduce the lights. This can help with energy consumption.”
The NSF-funded work will serve as a foundation for various emerging AI-based applications including smart traffic light systems. Many of these applications will require a huge amount of data to be automatically processed and some will need to be processed in real time.
“There is a lot of noisy data and missing data points,” said Khamfroush. “A big part of this project is about federated learning and federated data preparation. This means we are preparing data and training machine learning models without losing privacy because we are not sharing the data to a cloud. All the training is done collaboratively and locally on the devices.”
Khamfroush’s research was previously focused on distributed and edge computing systems. As machine learning becomes more and more developed, she says her research becomes more applicable in the domain of machine learning and distributed machine learning.
“I was looking for something that is more of interdisciplinary research. I thought about how I can bring in my previous research and add a flavor of machine learning to it?”
“Dealing with the unknown is something that I really like. I think doing research, especially in this very exciting field of machine learning and computer science, is something that I really like and appreciate because I can get creative. I can envision things that may be very ambitious. You may fail. But I just like dealing with the unknown and being able to deal with the challenges.”
The CAREER Award is one of the “most prestigious awards in support of the early career-development activities of those teacher-scholars who most effectively integrate research and education within the context of the mission of their organization,” according to NSF.
This material is based upon work supported by the National Science Foundation under Award Number 2340075. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.