Research interests
Overview
During my undergraduate studies at McGill, I was deeply interested in theoretical physics. My program, a joint honours program for mathematics and physics, focused heavily on theoretical physics and abstract mathematics, and it was during this time that I became involved in astrophysics/cosmology research, where I took part in a project that tried to understand how very massive objects in the universe (such as galaxies and clusters of stars) came to be. The research focused on analyzing cosmic strings, which are thought to have arisen in the very early stages of the universe, and examining whether they can act as "seeds" for the said massive objects, since these strings are thought to be very massive, and thus have large gravitational effects.
As intriguing as this research was, as I prepared to commence my PhD, I began to realize that I wanted my research to be more tangible. For this reason, I joined an atmospheric physics research group at University of Toronto. My research revolves around making measurements of the atmosphere using a Fourier transform infrared (FTIR) spectrometer, as well as using computer models to interpret the measurements. This spectrometer is part of an international network of similar instruments, and the measurements (from 2002~) of various species of gases, including acetylene, ethane, methane, methanol, carbon monoxide, formaldehyde, hydrogen chloride, hydrogen cyanide, formic acid, hydrogen fluoride, nitric acid, nitrous oxide, ammonia and ozone are all publicly available.
My research projects include analyzing emissions from wildfires, which get transported great distances and can be seen passing over Toronto, examining pollutants like ammonia using various observations including the FTIR, surface (in-situ) and satellite-based observations, as well as performing time series analysis on the 17+ years worth of data, looking for trends, enhancement events and outliers.
Current projects
My current project is centered around examining trends and identifying unusual enhancements in the time series data. This involves fitting the time series data with a function that includes a linear trend, as well as a cyclical component to account for seasonality (referred to as Fourier series). The analysis relies on statistical tricks like bootstrapping for error analysis. The fit can also be used to identify enhancements, by examining the residuals of the fit. For some species of gases, such as methane (important greenhouse gas) and carbon monoxide (important pollutant), models can be run in a "tagged" mode to attempt to attribute the driving factors of trends. The model that I use for my research is called GEOS-Chem, a chemical transport model (CTM) developed at Harvard.
Another is to analyze the effects of the Lockdown (caused by the COVID-19 pandemic) on air quality in Toronto (and elsewhere). You can read about some of the preliminary findings here (based on discussions to be presented in my PhD thesis, currently under review by my doctoral committee).