During my undergraduate studies, I pursued a number of different research opportunities, while obtaining a double major in Chemical Physics and Biochemistry, with a minor in Mathematics from the University of Toronto. I split my research work between both experimental and theoretical groups. I worked with Professor Katherine Siminovitch characterizing the role of an actin organizing protein, N-WASp, in mouse brain-specific conditional knockouts. Concurrently, I also worked with Professor Paul Brumer exploring theoretical problems in quantum mechanics, focusing on the control of system dynamics.

In my third year, I worked at Janelia Farm in Dr. Karel Svoboda’s lab, training mice in a learned behavior in order to gain insight into the neural coding of somatosensation in the barrel cortex. I then focused on computational learning networks, for my fourth year thesis; I worked on restricted Boltzmann Machine neural networks, under Professor Geoffrey Hinton, studying the results of modifying these models to make them more biologically realistic.

Currently, as a student in the joint Janelia Farm-Cambridge University graduate program, my supervisors are Professor Simon Laughlin (Cambridge), and Mitya Chklovskii (JFRC). Our aim is to apply theoretical techniques to gain an understanding of the function of different subsets of neurons within the fly visual system.

By understanding the theoretical structure of the computations performed in the visual system, we should be able to constrain the potential circuit structures that can be used to implement these computations. Then, by looking for these topologies, it should be possible to associate functions with particular neurons within the fly visual system.

The two computations that I have focused on are mean adaptation, and motion detection. In the case of mean adaptation, we have been able to show that only a limited number of small neuronal circuits can implement mean adaptation. In the case of motion detection, our theoretical analysis has allowed us to provide alternative models to the existing (well-accepted) Elementary Motion Detection (EMD) model. These alternatives have more natural implementations than the EMD within the circuitry of the fly visual system.

In addition to these theoretical results, I have been developing a novel measure of error probability in semi-automated electron microscopy (EM) reconstruction, giving us the probability of a single proofreader’s work resulting in an error within the final connectivity diagram. We have used these probabilities to drive a new method of proofreading EM reconstructions, which allows us to provide probability bounds on the resulting connectivity matrix, while also allowing us to speed up the proofreading process. This method of synapse-driven reconstruction is currently being implemented, at Janelia, in semi-automated reconstruction of a column from the fly medulla.

Finally, I have also been working on a computationally efficient biophysical model of synaptic transmission. This should allow Monte Carlo simulations of cellular connections within the fly visual system, which often contain many tens of synapses. These simulations should provide a better understanding of the way activity is transmitted in the system, allowing us to better constrain the function of individual neurons within the fly visual system.

Outside the lab, I enjoy spending time in the great outdoors: hiking, bird-watching, and photographing nature. I also like to fit in a game of ultimate Frisbee, tennis, or soccer. In addition, I try to find some time to go scuba diving, as well as flying (having gotten my private pilot’s license, just before starting my graduate work).