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 PhysicsWebsite:
https://as.nyu.edu/content/nyu-as/as/faculty/stefano-martiniani.htmlGoogle:
"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
Mean distance: (not calculated yet)
Cross-listing: Chemistry Tree
Parents
Sign in to add mentor| Brian C O'Regan | research assistant | 2010-2011 | Imperial College London (Chemistry Tree) |
| Alexei Kornyshev | research assistant | 2011-2012 | Imperial College London |
| Daan Frenkel | grad student | 2012-2017 | Cambridge |
| Paul Michael Chaikin | post-doc | 2017-2019 | NYU |
| Dov Levine | post-doc | 2017-2019 | Technion |
Collaborators
Sign in to add collaborator| David J. Heeger | collaborator | 2019- | NYU (Neurotree) |
| Brian C O'Regan | collaborator | 2010-2011 | Imperial College London (Chemistry Tree) |
| David J. Wales | collaborator | 2012-2017 | Cambridge (Chemistry Tree) |
| Bulbul Chakraborty | collaborator | 2016-2017 | Brandeis |
BETA: Related publications
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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 |