Kimathi Kaai, a current master’s student in the Department of Systems Design Engineering, has been awarded the prestigious Indigenous and Black Engineering and Technology Momentum (IBET) Fellowship, which will enable him to pursue his PhD studies with greater prospects.
The fellowship, provided by the Faculty of Engineering, provides recipients with $30,000 each year throughout their graduate degrees and aims to increase the representation of Black and Indigenous communities in academia while fostering regional connections.
Kaai starts his PhD in the fall of 2024 under the supervision of Dr. Alex Wong, a systems design engineering professor, and Dr. Sirisha Rambhatla, an assistant professor in the Department of Management Sciences. Kaai expressed his excitement about the fellowship, "IBET allows me the freedom to explore various areas of research and find what topics peak my interests.” He is particularly eager for the opportunity to be part of research conversations through attending conferences and networking with researchers.
Kimathi’s research interests are motivated by the question: How can we make innovations in AI-based imaging easier to adopt? His master’s thesis emphasizes the impact of Artificial Intelligence (AI) and Machine Learning (ML) research on high-stakes fields such as public health and manufacturing. For his PhD. focus, Kaai plans to address the accessibility of machine learning (ML) innovations.
"Machine learning requires large volumes of data so the process of gathering imaging can create quite a bottleneck,” said Kaai. "Providing per-pixel annotations for imaging tasks is very expensive and labour-intensive. And satisfying small margins of error also needs to be very accurate and highly specialized, making AI imaging labelling difficult to translate to different industries."
Leveraging his existing industry partnerships with companies like Nissan and Apple, Kaai will also continue to analyze factors hindering the adoption of emerging AI-based imaging solutions. His research will contribute to AI accessibility by developing ML strategies that can adapt to new environments while reducing the need for annotated training data.
"As an immigrant, I perceive my research aspirations through the lens of influencing communities that have limited AI infrastructure, like my homeland, Kenya," Kaai explained. He believes that addressing these challenges is essential to empowering researchers to adopt ML solutions for communities and initiatives with limited data collection and computational resources.