Jennifer Stiso

Jennifer

Neuroscience Ph.D. student at the University of Pennsylvania, working with Prof. Danielle Bassett in the Complex Systems Group and Prof. Timothy Lucas in the Center for Neural Engineering and Therapeutics. I graduated in 2016 from the University of California Berkeley with a B.A. in Molecular and Cellular Biology, and Cognitive Science. At Berkeley, I worked in the lab of Prof. Robert Knight.

I am interested in the electrophysiological mechanisms that support cognition, and how we can use our understanding of these mechanisms to inform effective therapies for neurological disease. I find combining computational modeling with time-resolved neural recordings (usually ECoG) to be an especially useful, interesting, and fun way to investigate these topics. My main side hustle is to support diversity and inclusion in science.

Tools Enabling Citation Transparency: Some recent work from Jordan Dworkin showed that neuroscience tends to cite men more than would be expect given thier prevalence in the field and that this trend in increasing with time. While there are many courses of action to help fight these biased citation practices, I have recently helped contribute to some tools that can help individuals quantify and offset these biases.

The I have developed a Google Chrome extension with Matthew Schaff the will display the probabilistic gender of first and last authors of all papers on Google Scholar or PubMed search page. The idea is that is people are consciously aware of this information as they are searching for references, they can more easily choose to cite a diverse group of scientists.

I have also contributed to a project led by Dale Zhou that developed a jupyter notebook that will quantify the gender breakdown of first and last authors in a .bib file. The hope is that authors can see the gender breakdown of their citations, and then retroactively add more work from women led teams.

There are a lot of limitations to these tools, the first that I notice is the lack of intersectionality. We hope to help extend these tools to race in the near future. Additionally, the classification of authors in man/woman categories imposes a false gender binary. The intent here is to capture the perceived gender of the authors, rather than reflect their true gender identity.

News: Check out Penn's press release about my first project modeling direct electrical brian stimulation.

In the summer of 2019, I developed content for and taught a Python Bootcamp at Penn. You can find an article about the bootcamp here.