Julia Vendemiatti
Harvey Mudd College
8 Papers
15 Citations
Julia Vendemiatti is an academic researcher from Harvey Mudd College. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 3, co-authored 4 publications.
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Papers
Adeno-associated viral vectors for functional intravenous gene transfer throughout the non-human primate brain.
Miguel R. Chuapoco,Nicholas C. Flytzanis,Nick Goeden,K. M. Roxas,Ken Y. Chan,Jon Scherrer,Janet Winchester,Roy J. Blackburn,Lillian Campos,Kwun Nok Mimi Man,Junqing Sun,Xinhong Chen,Vikram Singh,Cynthia Mary Arokiaraj,Timothy F. Shay,Julia Vendemiatti,Min Jee Jang,John K. Mich,Yemeserach Bishaw,Bryan B. Gore,Victoria Omstead,Naz Taskin,Natalie Weed,Boaz P. Levi,Jonathan T. Ting,Cory T. Miller,Benjamin E. Deverman,James Pickel,Lin Tian,Andrew S. Fox,Viviana Gradinaru +30 more
TL;DR: CAP-Mac is shown to have potential for non-invasive systemic gene transfer in the brains of non-human primates and applications of a single, intravenous dose of CAP-Mac to deliver functional GCaMP for ex vivo calcium imaging across multiple brain areas, or a cocktail of fluorescent reporters for Brainbow-like labelling throughout the macaque brain, circumventing the need for germline manipulations.
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The Bias-Expressivity Trade-off
TL;DR: This work derives bounds relating bias to expressivity by using an information-theoretic notion of entropy on algorithm outcome distributions, demonstrating a trade-off between bias and expressivity.
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The Bias-Expressivity Trade-off
Julius Lauw,Dominique Macias,Akshay Trikha,Julia Vendemiatti,George D. Montanez +4 more
- 01 Jan 2020
TL;DR: In this paper, the authors examine the flexibility (expressivity) of biased algorithms and derive bounds relating bias to expressivity, proving the necessary trade-offs inherent in trying to create strongly performing yet flexible algorithms.
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The Futility of Bias-Free Learning and Search
George D. Montanez,Jonathan Hayase,Julius Lauw,Dominique Macias,Akshay Trikha,Julia Vendemiatti +5 more
TL;DR: This work demonstrates the necessity of bias in learning, quantifying the role of bias (measured relative to a collection of possible datasets, or more generally, information resources) in increasing the probability of success, and demonstrates that bias is a conserved quantity.
Intravenous gene transfer throughout the brain of infant Old World primates using AAV
Miguel R. Chuapoco,Nicholas C. Flytzanis,Nick Goeden,J. Christopher Octeau,K. M. Roxas,Ken Y. Chan,Jon Scherrer,Janet Winchester,Roy J. Blackburn,Lillian Campos,Cynthia Mary Arokiaraj,Timothy F. Miles,Min Jee Jang,Julia Vendemiatti,Benjamin E. Deverman,James Pickel,Andrew S. Fox,Viviana Gradinaru +17 more
TL;DR: CAP-Mac is an engineered AAV variant that enables systemic, brain-wide gene delivery in infants of two Old World primate species and shows promise for researchers and clinicians alike to unlock novel, noninvasive access to the brain for efficient gene transfer.