PhD, University of Cambridge
I am a trained experimental physicist with a passion for statistical problem-solving across a range of research disciplines. A sample include:
As a teacher, I wish to inculcate in students the intuitions and skills that I bring to my research. On the technical side, I bring together topics in programming, probability theory, statistics, linear algebra, and machine learning to present a rigorous, yet accessible theoretical minimum for the aspiring data innovator. On the conceptual side, I enjoy exploring ideas in epistemology to situate the modern practice of data science within a larger discourse on the history and philosophy of knowledge and science – from al-Ghazali to Karl Popper.
Most importantly, my work at Equitech Futures brings together my passion for research and teaching with a deeper calling – to share my joy for the awe-inspiring expressiveness of mathematics. Maths education across the world largely fails at cultivating mathematical intuition, instead focusing on tedious calculations and rote methods. As a result, many students fail to see any underlying coherence or beauty in the structure of mathematics, and instead begin to simply associate it with either torture or anxiety. In short, if we think of learning math like learning a second language, most students are stuck in the awkward phase of knowing a lot of vocabulary and some grammar, but not being able to hold a conversation. In my role as a maths educator, I strive to provide my students with the conceptual and socioemotional scaffolding to become native speakers of mathematics.
I have a masters and doctorate from the University of Cambridge where I was a Gates Scholar at Trinity College and won the Cavendish Laboratory prize. I earned my bachelor’s degree from the University of Texas at Dallas where I was a Eugene McDermott Scholar.