Iris Stone

I am currently a Scientist I (Shanahan Research Fellow) at the Allen Institute in Seattle, WA. This is an independent fellowship position where I work with Allen Institute investigators as well as faculty at the University of Washington to develop computational neuroscience methods for the institute’s ever-increasing quantities of data and generate new scientific insights. I am enthusiastic about working on open, team-oriented science and supporting the Allen Institute’s mission of unlocking treatments and cures for human health. 

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 worked with Dr. Jonathan Pillow and Dr. Ilana Witten, using statistical modeling and machine learning to quantify animal behaviors and their associated neural dynamics. My thesis work involved using latent variable models to identify 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. In 2022, I completed an internship at a biotech startup focused on using cerebral organoids to develop treatments for neurological diseases. Prior to grad school, I earned my B.S. in Physics from George Mason University, where I researched the use of organic and nanomaterials in biomedical applications with Dr. Patrick Vora. 

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News:

October 2023: I have started a new job as a Scientist I (Shanahan Research Fellow) at the Allen Institute in Seattle, WA.
September 2023: I’m thrilled to announce that I have defended my Ph.D. on my thesis work entitled, “Latent variable models for characterizing the dynamic structure underlying complex behaviors”, making me Dr. Stone!
August 2023: I had a great time attending the Summer Workshop on the Dynamic Brain in the San Juan Islands this month, sponsored by the Allen Institute and the University of Washington
July 2023: I’m happy to announce that my paper (along with co-first author Yotam Sagiv) “Spectral learning of Bernoulli linear dynamical systems models” is now published in Transactions on Machine Learning Research
March 2023: I will be presenting a poster on my work “Spectral learning of Bernoulli linear dynamical systems models for decision-making” at COSYNE and giving a talk in the workshop “What I cannot create I do not understand: analyzing neural and behavioral data with generative models“
March 2023: My second (co) first author paper, “Spectral learning of Bernoulli linear dynamical systems models for decision-making” is now up as a pre-print on arXiv.
July 2022: I’ve received the National Research Service Award (NRSA) for predoctoral students (F31) from the National Institutes of Health (Impact Score: 16, Percentile: 4.0)
May 2022: I’m excited to head out to San Francisco this summer for an internship at Herophilus, a startup focused on curing complex brain disease using human brain models, scaled biology, and machine learning
March 2022: My first (co) first author paper, “Opponent control of behavior by dorsomedial striatal pathways depends on task demands and internal state” is now out in Nature Neuroscience
February 2022: A paper I co-authored, “Mice alternate between discrete strategies during perceptual decision-making” is now out in Nature Neuroscience
March 2020: I will be presenting a poster on my work, “Latent-state models reveal a state-dependent contribution of the striatum to decision-making” at COSYNE and have also received a student travel grant to support the trip
April 2019: I’ve been accepted into the Marine Biological Laboratory’s Brains, Minds, and Machines (BMM) summer course, which I will attend for three weeks this August in Woods Hole, Massachusetts