I recently received a Ph.D. in Bioengineering with a concentration in Neurotechnology and Computational Neuroscience from George Mason University with Dr. Giorgio Ascoli as my advisor. I also have a M.Sc in Biomedical Informatics from Arizona State University and a B.Sc. in Biology from Quinnipiac University. I am currently continuing to work as a research assistant at George Mason University as I pursue post-doctoral positions.

I research computational neuroscience with a focus on network- and circuit-level simulations. I am interested in simulating low physiological levels such as individual neurons’ spikes and synapses. For instance, I like to investigate how fundamental building blocks of neural processing lead to more complex cognitive functions. In particular, it is interesting to study how individual spatially-modulated cells, e.g., grid and place cells, contribute to spatial awareness. I am similarly interested in how sensory-specific cells such as visual system cells, e.g., simple and complex light gradient or color responsive cells, lead to advanced operations like object identification or object location recognition. I like to work closely with animal data and have collaborations with animal experimentalists. I am very interested in working on research questions that create simulations that show promise of practical ways to validate them with recordings, and personally working with experimentalists to conduct this work. I am also particularly interested in the neural activity involved in a wide range of learning and memory mechanisms.

During my Ph.D. some key questions I investigated were what are some of the most popular methods for modeling cognitive functions associated with the hippocampal formations of rodents? How would modeling neural properties with additionally biologically realistic details relative to past models affect simulations of spatial navigation associated cells? I created a literature review and knowledge base to report on the modeling methods (Sutton & Ascoli, 2021). Knowledge from that resource helped inform a simulation I created to study the spatial navigation question. The modeling of the spatial navigation associated cells is connected to spatial memory and helps contribute to that knowledge through the connections (Sutton et al., 2024).