About me?

I am currently pursuing a doctorate degree in Computer Science at Carnegie Mellon. My research goal is to develop tools for building learning systems that generalise well out of distribution, do not fail spuriously and adapt to changing environments gracefully. I attempt to achieve this through the lens of representation learning using tools from generative modelling and optimisation.

Prior to PhD, I had a wonderful time at IBM-Research in Nairobi, Kenya where I worked on algorithms to ensure robustness of deep learning models for example, being able to detect when such models are processing adversarial inputs, techniques for explainability of machine learning models, and causal inference for global health.

I am big believer in entrepreneurship, and I love listening to Afrobeat. Calibrated intuition is a space to develop frameworks of how to think about innovation and how technology will shape development in Africa. I will also write technical articles related to my research in AI and deep learning.

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