Stefano Martiniani
New York University, University of Minnesota, University of Cambridge, Imperial College London
0000-0003-2028-2175
About
Stefano Martiniani is an Assistant Professor of Physics, Chemistry, Mathematics, and Neural Science at New York University. He holds a Ph.D. in Chemistry and an M.Phil in Scientific Computing from the University of Cambridge, and a B.Sc. in Chemistry from Imperial College London.
Prior to joining NYU, Martiniani was an Assistant Professor of Chemical Engineering and Materials Science at the University of Minnesota - Twin Cities. Previously, he held a postdoctoral position in Physics at NYU. He received awards including the AFOSR Young Investigator Award, NSF CAREER, 2023 Interdisciplinary Early Career Scientist Prize from IUPAP, Simons Foundation Faculty Fellowship, Gates Cambridge Scholarship, and Outstanding Ph.D. Thesis Award from the University of Cambridge.
Employment
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New York University Assistant Professor2022 - Present
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University of Minnesota Assistant Professor2019 - 2021
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New York University Postdoc2017 - 2019
Education
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University of Cambridge Theoretical Chemistry PhD2013 - Present
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University of Cambridge Scientific Computing MPhil2012 - 2013
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Imperial College London Chemistry BSc2009 - 2012
Projects & Funding
Projects & funding information is unavailable.
Publications (52)
- Emergent universal long-range structure in random-organizing systems Save
- Open Materials Generation with Inference-Time Reinforcement Learning Save
- PropMolFlow: property-guided molecule generation with geometry-complete flow matching Save
- Persistently Increased Expression of PKMzeta and Unbiased Gene Expression Profiles Identify Hippocampal Molecular Traces of a Long‐Term Active Place Avoidance Memory and “Shadow” Proteins Save
- MolGuidance: Advanced Guidance Strategies for Conditional Molecular Generation with Flow Matching Save
- Stabilization of recurrent neural networks through divisive normalization Save
- Spatial and Temporal Cluster Tomography of Active Matter Save
- Perspective on artificial intelligence for accelerated materials design (AI4Mat) workshops in 2024 Save
- Persistently increased expression of PKMzeta and unbiased gene expression profiles identify hippocampal molecular traces of a long-term active place avoidance memory and ‘shadow’proteins Save
- Open Materials Generation with Stochastic Interpolants Save