Curriculum Vitae

It is a massive global problem that we cannot inexpensively determine the quality, character, and contents of the materials we consume day-to-day and of the world around us in general. It is likely these problems impact you directly. My recent work tries to address some of these challenges using scalable approaches developed in the machine learning community. Here are a few examples of such problems:

• The EU Commission found evidence in 2023 that 147 of 320 honey samples collected from EU member states were illicitly diluted with sugar syrups. Recent tests in the UK suggest that number may be over 90% [scholarly source, media source].
• There is a history of cadmium and lead contamination in common food products such as chocolate [scholarly source, media source].
• When CO2 levels are elevated in the atmosphere, major food crops such as rice begin to uptake toxic levels of arsenic from their paddies [scholarly source, media source].
• We cannot reliably determine the carbon, nitrogen, nutrient, or contaminant contents of agricultural soil without sending samples to a lab.
• Some of the largest deposits of rare minerals ever recorded are only now being discovered, and they're in places where thorough surveying has already occurred [scholarly source, media source].

I believe that scalable material characterization is at the core of the climate, sustainability, and human health challenges that we face in the modern day. We should be able to track pollutants, greenhouse gas emissions, and nutrient fluxes at scale around the globe, to understand how people and industries can (A) mitigate environmental and human harms, and (B) adapt to a changing climate. You should be able to easily and personally confirm that what you think you are eating is actually what you are eating, and that the products you buy are not exposing you and your loved ones to trace contaminants. This means:

• Building flexible, non-destructive analysis techniques and hardware which can analyze the elemental contents of everyday things safely and inexpensively [what I'm doing on this].
• Interoperating data collection platforms with flexible coverage, such as ground- and satellite-based imaging systems [what I'm doing on this].
• Tying Earth system data to a source of clear economic value which justifies collecting it, such as grid management or geological surveying [work on this coming soon].

In my doctoral studies, I worked on fundamental problems in physics regarding the behavior of quantum theories of gravity, and how we might construct them in a manner consistent with the observed accelerated expansion of the universe. How can we describe inflating universes (metastable de Sitter vacua) in string theory without relying on, for example, the KKLT prescription? How do various mechanisms in string theory reinforce the causal nature of spacetime, or excise infinitely-curved regions of spacetime? How do we use lessons from non-perturbative frameworks like finite-volume AdS holography to gain insight into holographic descriptions of our universe?

Away from my desk, I enjoy music of all kinds, and play classical guitar. I partake in endurance cycling, rock climbing, weightlifting, and judo.