Adetayo Okunoye
Phd Software Engineer @Meta
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WORK HISTORY
Phd Software Engineer @Meta
CA, US
Develop highly scalable classifiers and tools leveraging machine learning, regression, and rules-based modelsSuggest, collect and synthesize requirements and create effective feature roadmap.Code deliverables in tandem with the engineering team.Adapt standard machine learning methods to best exploit modern parallel environments (e.g. distributed clusters, multicore SMP, and GPU)
EDUCATION
Southern University and A&M College- Baton Rouge
Master's degree
Ladoke Akintola University of Technology
Bachelor of Technology - BTech
The University of Georgia
Doctor of Science
ABOUT ADETAYO OKUNOYE
I’m currently pursuing a Ph.D. in Computer Science at UGA where I serve as a Graduate Student Researcher within the Novel Engineering Solutions (NES) Lab. At NES Lab, I am privileged to be part of a groundbreaking initiative, the Scalation Project, Timeseries projects, Symbolic Regression for scientific discovery SRSD, which is at the forefront of integrating intelligent systems with advanced machine learning systems. This work is revolutionizing the way we approach machine learning by enhancing model interpretability, uncovering hidden patterns, and addressing critical challenges such as algorithmic bias and fairness. Our research transcends traditional academic boundaries by tackling some of the most pressing issues in artificial intelligence today. We are committed to reshaping the landscape of deep learning through innovations that make these systems not only more transparent but also ethically sound. The potential impact of this work extends beyond the lab, offering transformative solutions for real-world challenges across industries like healthcare, finance, and public policy. For me, this endeavor is deeply personal. It embodies my passion for using data science and AI to address complex societal problems while ensuring that the technologies we build are equitable and trustworthy. Being part of a team that dares to challenge the status quo and reimagine the future of machine learning is both inspiring and humbling, a testament to the power of collaboration and the pursuit of excellence.
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