I am a Scientist I (Shanahan Research Fellow) at the Allen Institute in Seattle, WA. In this independent fellowship position, I develop computational neuroscience methods for the institute’s ever-increasing quantities of data in order to generate new scientific insights. I am particularly interested in methods for translational neuroscience and disease treatment and am currently working on projects to understand the temporal dynamics of neurological disorders, both from a genetic basis (Alzheimer’s disease) and in the context of neuromodulatory signaling (autism spectrum disorder).Previously, I earned my Ph.D. in Neuroscience from Princeton University, where I had a particular focus on quantitative and computational approaches to neuroscience. While there, I also earned a supplemental graduate Certificate in Statistics and Machine Learning (CSML) and was a member of the Quantitative Neuroscience Training Program (QNTP). In my research, I used statistical modeling and machine learning to quantify animal behaviors and their associated neural dynamics. Specifically, I developed latent variable models for identifying the dynamic structure underlying complex cognitive processes in mice, such as decision-making and exploration.I also have a strong interest in biotechnology and biomedicine and currently volunteer with Nucleate Seattle, a 501(c)(3) non-profit organization dedicated to empowering the next generation of biotech leaders through free educational programming and training. While in grad school, I also completed an internship at a biotech startup focused on using cerebral organoids to develop treatments for neurological diseases. Prior to that, I earned my B.S. in Physics from George Mason University, where I researched the use of organic and nanomaterials in biomedical applications. In undergrad I also ventured into entrepreneurship, founding a research/writing/editing company that I operated for five years. For more: www.iris-stone.comInterests: - Biotechnology & drug discovery- Systems & translational neuroscience- Statistical modeling- Machine learning- Dynamical systems