Michael Wilson
This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.
Researcher
Also affiliated: Australian National University (1997–2002); Manchester Metropolitan University (1995–2002); University of London (1970–1998); University of Wisconsin–Eau Claire (2011); Argonne National Laboratory (1967–1971); Rutherford Appleton Laboratory (1988–2025); Jet Propulsion Laboratory (2017); Los Alamos National Laboratory (1988); Johns Hopkins University (1968); Science and Technology Facilities Council (1988–2025); Royal Holloway University of London (1991–1993); Kansas State University (1990); Kingston University (2006); University of Massachusetts Amherst (2024); SLAC National Accelerator Laboratory (2011–2013); Center for Theoretical Physics (2021); UK Carbon Capture and Research Centre (2006); Devon History Society (1996–1997); MRC Biostatistics Unit (1986–1990); Xerox (United Kingdom) (1990); Eastman Dental Hospital (1996–1998); Universidad de Londres (1973); Atotech (United States) (2026); Université Savoie Mont Blanc (2013); University of Reading (1969); Temple University (1958); Newcastle University (1971); Science and Engineering Research Council (1988–1991); Stanford University (2011–2012); University of Edinburgh (2019–2022)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Michael Wilson's research has explored diverse areas within physics and materials science, with recent publications focusing on theoretical physics and experimental semiconductor characterization. His work on the "Power-Law Approach of the Stress-Energy Tensor to the Unruh State after Gravitational Collapse" delves into fundamental aspects of physics, examining the behavior of physical systems under extreme conditions.
In parallel, Wilson has contributed to advancements in semiconductor materials for advanced applications. Publications such as "Single-Step Synthesis of Cs <sub>3</sub> Bi <sub>2</sub> I <sub>9</sub> Nanocrystals for Scalable Direct X-ray Detectors" and "Characterisation and quantification of the `Excess-Leakage-Current' effect in Redlen HF-CdZnTe at > 10<sup>6</sup> ph s<sup>-1</sup> mm<sup>-2</sup>" highlight his engagement with materials relevant to radiation detection and imaging technologies. These works indicate a focus on material properties, synthesis methods, and performance characterization for specific technological applications.
Wilson's research interests also extend to computational physics and machine learning applications within scientific contexts, as evidenced by publications like "An open-source machine learning framework for global analyses of parton distributions" and "Bayesian Approach to Inverse Problems: an Application to NNPDF Closure Testing." These contributions suggest an interest in developing and applying computational tools for complex data analysis and modeling in physics.
Metrics
- h-index: 26
- Publications: 164
- Citations: 5,987
Selected Publications
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A face-isolation proof of the two-variable Gaussian Moments Conjecture (2026)arXiv (Cornell University) OpenAlex
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Photocatalytic water splitting for sustainable hydrogen: materials, mechanisms, and future directions (2026)
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Power-Law Approach of the Stress-Energy Tensor to the Unruh State after Gravitational Collapse (2026)arXiv (Cornell University) OpenAlex
Collaboration Network
Top Collaborators
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
- Bayesian Approach to Inverse Problems: an Application to NNPDF Closure Testing
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
- Bayesian Approach to Inverse Problems: an Application to NNPDF Closure Testing
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
- The path to proton structure at 1% accuracy
- An open-source machine learning framework for global analyses of parton distributions
- An open-source machine learning framework for global analyses of parton distributions
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