MV
Michele Vallisneri
ETH Zurich, Jet Propulsion Laboratory, California Institute of Technology
Research areas: Physics and Astronomy · Astronomy and Astrophysics · Pulsars and Gravitational Waves Research · Astrophysical Phenomena and Observations · Gamma-ray bursts and supernovae
0000-0002-4162-0033
Employment
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ETH Zürich Professor of Gravitational Physics2025 - Present
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Jet Propulsion Laboratory Senior research scientist2021 - 2025
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Jet Propulsion Laboratory Research scientist2005 - 2021
Education
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California Institute of Technology PhD, physics1998 - 2002
Projects & Funding
Projects & funding information is unavailable.
Publications (39)
- Generalized Nonlinear Bayesian Pulsar Timing with ENTERPRISE Save
- GNSS-based automated detection of the 2025 Kamchatka tsunami 30 minutes before landfall using GUARDIAN Save
- Rapid Parameter Estimation for Pulsar-Timing-Array Datasets with Variational Inference and Normalizing Flows Save
- From the LISA global fit to a catalog of Galactic binaries Save
- Robust Bayesian inference with gapped LISA data using all-in-one TDI- ∞ Save
- NANOGrav 15-year gravitational-wave background methods Save
- Posterior predictive checking for gravitational-wave detection with pulsar timing arrays. I. The optimal statistic Save
- Posterior predictive checking for gravitational-wave detection with pulsar timing arrays. II. Posterior predictive distributions and pseudo-Bayes factors Save
- The NANOGrav 15 yr Data Set: Constraints on Supermassive Black Hole Binaries from the Gravitational-wave Background Save
- The NANOGrav 15 yr Data Set: Bayesian Limits on Gravitational Waves from Individual Supermassive Black Hole Binaries Save
- The NANOGrav 15 yr Data Set: Detector Characterization and Noise Budget Save
- The NANOGrav 15 yr Data Set: Evidence for a Gravitational-wave Background Save
- The NANOGrav 15 yr Data Set: Observations and Timing of 68 Millisecond Pulsars Save
- The NANOGrav 15 yr Data Set: Search for Signals from New Physics Save
- Time-delay interferometry without delays Save
- Modeling the Uncertainties of Solar System Ephemerides for Robust Gravitational-wave Searches with Pulsar-timing Arrays Save
- Learning Bayesian Posteriors with Neural Networks for Gravitational-Wave Inference Save
- Bayesian cross validation for gravitational-wave searches in pulsar-timing array data Save
- Reduced-Order Modeling with Artificial Neurons for Gravitational-Wave Inference Save
- A Gaussian Mixture Model for Nulling Pulsars Save