Dan C. Mann
Assistant Staff Scientist
Also affiliated: The Graduate Center, CUNY (2019–2021); University of Vienna (2019–2023); Austrian Academy of Sciences (2022–2024); University of Veterinary Medicine Vienna (2022–2025); City University of New York (2019–2021); Acoustics Research Institute (2022–2024)
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Dan C. Mann's research interests span interdisciplinary areas, including the acoustics of animal vocalizations and human speech, as well as public perception of climate change. His work on vocalizations has investigated universal principles underlying segmental structures in parrot song and human speech, and has explored octave equivalence perception in budgerigars. Mann also studies how house finches learn vocalizations from other species. In the realm of climate change, his research examines public opinion and consensus, specifically testing theories of pluralistic ignorance and the impact of public-consensus messaging across multiple countries. Earlier in his career, Mann published research on the thermal properties and hydrogenation of carbon nanotubes and the determination of specific heat and thermal conductivity in glass.
Metrics
- h-index: 6
- Publications: 22
- Citations: 132
Positions
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Assistant Staff Scientist 2023–presentUniversity of Arkansas for Medical Sciences Biomedical Informatics ORCID
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Marie Skłodowska-Curie Fellow 2021–2029Austrian Academy of Sciences Acoustics Research Institute ORCID
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Computational Behavioral Science Researcher 2020–2023University of Veterinary Medicine Vienna Interdisciplinary Life Sciences ORCID
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Staff Researcher 2019–2020University of Vienna Cognitive Biology ORCID
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Research Fellow 2016–2017University of Vienna Cognitive Biology ORCID
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Lecturer 2013–2016Brooklyn College English ORCID
Selected Publications
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Healing of lytic lesions and restoration of bone health in multiple myeloma through sclerostin inhibition (2025)
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Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images (2025)
Collaboration Network
Top Collaborators
- Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images
- Healing of lytic lesions and restoration of bone health in multiple myeloma through sclerostin inhibition
- Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images
- Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images
- Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images
- Evaluating Skellytour for Automated Skeleton Segmentation from Whole-Body CT Images
- Healing of lytic lesions and restoration of bone health in multiple myeloma through sclerostin inhibition
- Healing of lytic lesions and restoration of bone health in multiple myeloma through sclerostin inhibition
- Healing of lytic lesions and restoration of bone health in multiple myeloma through sclerostin inhibition
- Healing of lytic lesions and restoration of bone health in multiple myeloma through sclerostin inhibition
- Healing of lytic lesions and restoration of bone health in multiple myeloma through sclerostin inhibition
- Healing of lytic lesions and restoration of bone health in multiple myeloma through sclerostin inhibition
- Healing of lytic lesions and restoration of bone health in multiple myeloma through sclerostin inhibition
- Healing of lytic lesions and restoration of bone health in multiple myeloma through sclerostin inhibition
- Healing of lytic lesions and restoration of bone health in multiple myeloma through sclerostin inhibition
- Healing of lytic lesions and restoration of bone health in multiple myeloma through sclerostin inhibition
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