Stefano Martiniani, PhD

Affiliations: 
2019-2021 Chemical Engineerng and Materials Science University of Minnesota, Twin Cities, Minneapolis, MN 
 2022- Physics New York University, New York, NY, United States 
Area:
Statistical and Computational Physics
Website:
https://as.nyu.edu/content/nyu-as/as/faculty/stefano-martiniani.html
Google:
"Stefano Martiniani"
Bio:

Stefano Martiniani is an Assistant Professor of Physics, Chemistry and Mathematics at New York University.
https://orcid.org/0000-0003-2028-2175
https://www.researchgate.net/profile/Stefano-Martiniani
https://scholar.google.com/citations?user=pxSj9JkAAAAJ&hl=en

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Cross-listing: Physics Tree

Parents

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Brian C O'Regan research assistant 2010-2011 Imperial College London
Alexei Kornyshev research assistant 2011-2012 Imperial College London (Physics Tree)
Daan Frenkel grad student 2012-2017 Cambridge
Paul Michael Chaikin post-doc 2017-2019 NYU (Physics Tree)
Dov Levine post-doc 2017-2019 Technion (Physics Tree)

Collaborators

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David J. Heeger collaborator 2019- NYU (Neurotree)
Brian C O'Regan collaborator 2010-2011 Imperial College London
David J. Wales collaborator 2012-2017 Cambridge
Bulbul Chakraborty collaborator 2016-2017 Brandeis (Physics Tree)
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Publications

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Rawat S, Martiniani S. (2023) Element-wise and Recursive Solutions for the Power Spectral Density of Biological Stochastic Dynamical Systems at Fixed Points. Arxiv
Vita JA, Fuemmeler EG, Gupta A, et al. (2023) ColabFit exchange: Open-access datasets for data-driven interatomic potentials. The Journal of Chemical Physics. 159
Golinski AW, Schmitz ZD, Nielsen GH, et al. (2023) Predicting and Interpreting Protein Developability Via Transfer of Convolutional Sequence Representation. Acs Synthetic Biology. 12: 2600-2615
Anzivino C, Casiulis M, Zhang T, et al. (2023) Estimating random close packing in polydisperse and bidisperse hard spheres via an equilibrium model of crowding. The Journal of Chemical Physics. 158: 044901
Ro S, Guo B, Shih A, et al. (2022) Model-Free Measurement of Local Entropy Production and Extractable Work in Active Matter. Physical Review Letters. 129: 220601
Golinski AW, Mischler KM, Laxminarayan S, et al. (2021) High-throughput developability assays enable library-scale identification of producible protein scaffold variants. Proceedings of the National Academy of Sciences of the United States of America. 118
Martiniani S, Lemberg Y, Chaikin PM, et al. (2020) Correlation Lengths in the Language of Computable Information. Physical Review Letters. 125: 170601
Frenkel D, Schrenk KJ, Martiniani S. (2017) Monte Carlo sampling for stochastic weight functions. Proceedings of the National Academy of Sciences of the United States of America
Ballard AJ, Das R, Martiniani S, et al. (2017) Energy landscapes for machine learning. Physical Chemistry Chemical Physics : Pccp
Martiniani S, Schrenk KJ, Stevenson JD, et al. (2016) Structural analysis of high-dimensional basins of attraction. Physical Review. E. 94: 031301
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