Dana Mastrovito
Scientist II at Allen Institute for Brain Science
- Role
- Scientist Ii at Allen Institute
- Location
- Seattle, WA, US
- LinkedIn followers
- 500 followers
About Dana Mastrovito
I am currently working at the Allen Institute building models to understand how information is transferred between regions of the brain during visually guided behavior. My interests include brain computer interfaces, computational neuroscience and artificial intelligence. My thesis research characterized the dynamics of activity in the default mode network and the structural connections between the DMN and subcortical structures in cortical striatal thalamic circuits. I have also published work showing that the dynamics of brain activity associated with autism and schizophrenia can be used distinguished the two disorders. I have a background in physics and computing, with > 20 years of experience developing software for data analysis and hardware control in scientific research environments. Through my academic and professional experiences, I have developed expertise in machine learning, real-time computing, feature extraction, classifiers, clustering algorithms and developing analysis pipelines for large-scale data analysis. I have collaborated with neuroscientists, engineers, and physicists from around the world and have presented my work at scientific conferences world-wide. In my free time I enjoy many artistic endeavors and am driven to create. A lifelong dancer and singer, I also enjoy photography, drawing/painting, digital art, various AI-assisted creative endeavors, fashion design and cooking. http://danamastrovito.science/
Experience
Scientist Ii
Jul 2020 — Present · Seattle, WA, US
Built trainable RNN models of complex biological systems, implementing custom neural architec-tures for multi-scale data processing, for LFP, neuronal spike and animal behavior prediction• Performed analysis of system dynamics in biologically realistic networks, delivering published findings that inform hierarchical system dynamics.• Directed interns in developing gradient-descent-compatible GLIF (generalized leaky integrateand-fire) models, resulting in peer-reviewed publications.• Developed analysis for subspace alignment in neural networks • Implemented theoretical frameworks for consciousness modeling, bridging neuroscience and AI applications
Education
Rutgers University–Newark
PhD, Behavioral Neural Science
2010 — 2017
Ursinus College
BS, Physics
1994 — 1998
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