Postdoctoral Research Scientist
During medical school, the dream of helping as many people as possible led me to obesity and metabolic research and eventually into single-cell genomics. During my Ph.D. I was initially focused on the arcuate nucleus of the hypothalamus, an incredibly diverse brain region directly implicated in obesity, perhaps the most cellular diverse brain region of mammals. After realising standard single-cell methods led to results that were known to be wrong by decades of experimental data, my interest shifted toward learning accurate representations of single-cell data. I’ve found critical biases in current single-cell data analysis and addressed them by developing TopOMetry, a new modular and generalised framework for single-cell analysis that is guaranteed to find optimal representations. After developing this tool and validating its power in immune cells, my focus returned to the hypothalamus, in which we described the population dynamics involved in its development and maintenance in detail by integrating multiple datasets.
My post-doctoral research now involves applying state-of-the-art algorithms to high-throughput datasets implicated in obesity and its comorbidities, with a special focus on the sympathetic nervous system (SNS) and adipose tissue. We're interested in generating updated and highly detailed neuroanatomical and molecular descriptions of the SNS, which will serve as the foundation for new investigations testing cellular-specific therapeutic targets.
I am always happy to chat about neuroscience, metabolism, machine learning, and bioinformatics. If you have an idea or feel like chatting about these topics, drop me an email at email@example.com. Having years of experience with single-cell genomics in R and Python, I am always happy to help analyse complex datasets and design high-throughput experiments.