Ting Liu
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.
Research Associate
Also affiliated: Tianjin University of Commerce (2006–2010); Shanghai University (2010); Beijing Language and Culture University (2013–2017); Google (United States) (2021–2023); State University of New York (2012); Texas State University (2011); Harbin Engineering University (2014); Nanjing Normal University (2018–2020); Open University of China (2025); Tianjin University (2009–2019); National University of Defense Technology (2024); Nanyang Technological University (2015); Shenzhen University (2024); Shanghai Jiao Tong University (2014–2025); Chinese Academy of Sciences (2017–2020); Nantong University (2018); Harbin Institute of Technology (2008–2025); Beijing Jiaotong University (2017–2019); University of Victoria (2023); Charles University (2017); Renji Hospital (2014); Shanghai Mental Health Center (2018–2020); Yunnan Arts University (2022); University at Albany, State University of New York (2008–2012); Zhengzhou Normal University (2012); Shanghai Children's Medical Center (2023–2024); Shanghai Ninth People's Hospital (2021–2023); Institute of Disaster Prevention (2008); Affiliated Hospital of Guizhou Medical University (2025); Xianyang Normal University (2021); Shanghai First People's Hospital (2017); Peng Cheng Laboratory (2020); Science and Technology Commission of Shanghai Municipality (2021); Academy of Opto-Electronics (2017–2020); Weihai Science and Technology Bureau (2020); Mindray (China) (2023); Dalian Maritime University (2023); State Administration for Market Regulation (2022); Meituan (China) (2022–2025); Shanghai Key Laboratory of Psychotic Disorders (2018–2020); Shanghai Key Laboratory of Gynecologic Oncology (2014); Yichun Vocational Technical College (2021); Alibaba Group (China) (2021–2022); Arizona State University (2022); Ocean University of China (2012); Carnegie Mellon University (2006); Xi'an Jiaotong University (2012–2023)
Upstream record may be merged OpenAlex, the source of these figures, lists 48 institutions in 5 countries for this author record — a pattern that usually means it combines several researchers with similar names. The totals above may include work by other people.
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Ting Liu's research has explored diverse areas, including environmental monitoring, machine learning applications, and disease mechanisms. Liu has contributed to studies investigating the spatial distribution and environmental impact of substances like black carbon and rare earth elements in soils of China. This work has involved analyzing environmental samples and assessing potential health risks associated with their presence.
In the realm of medical research, Liu's work includes investigating cellular processes and potential therapeutic strategies. Publications demonstrate a focus on signal transduction pathways, such as the role of STAT3 in hepatic epithelial-to-mesenchymal transition, and the development of targeted drug delivery systems, like cisplatin-alginate conjugate liposomes for ovarian cancer treatment. Furthermore, Liu has been involved in developing and applying machine learning techniques for tasks such as skin lesion segmentation and human parsing in images, indicating an interest in the computational aspects of medical and biological data analysis.
Liu's scholarly output is substantial, with a reported h-index of 27, over 204 total publications, and more than 3,101 citations. This productivity and impact have led to designations as a highly cited researcher. Collaborations include work with Rebecca Logsdon Muenich, Arghajeet Saha, and Barira Rashid at the University of Arkansas at Fayetteville.
Metrics
- h-index: 24
- Publications: 187
- Citations: 2,854
Positions
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Shanghai Jiao Tong University publications 2014–2025School of Electronic, Information and Electrical Engineering ORCID
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Research Associate publications 2025University of Arkansas at Fayetteville Listing
Selected Publications
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Creation of a Landslide Susceptibility Map Using Short‐Term Data From the July 2018 Heavy Rainfall in Southern Hiroshima Prefecture (2026)
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Gaps in U.S. livestock data are a barrier to effective environmental and disease management (2025)
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Machine learning-based identification of animal feeding operations in the United States on a parcel-scale (2025)
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Modeling green infrastructure as a flood mitigation strategy in an urban coastal area (2024)
Collaboration Network
Top Collaborators
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Creation of a Landslide Susceptibility Map Using Short‐Term Data From the July 2018 Heavy Rainfall in Southern Hiroshima Prefecture
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Modeling green infrastructure as a flood mitigation strategy in an urban coastal area
- Modeling green infrastructure as a flood mitigation strategy in an urban coastal area
- Modeling green infrastructure as a flood mitigation strategy in an urban coastal area
- Modeling green infrastructure as a flood mitigation strategy in an urban coastal area
- Modeling green infrastructure as a flood mitigation strategy in an urban coastal area
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Machine learning-based identification of animal feeding operations in the United States on a parcel-scale
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
- Gaps in U.S. livestock data are a barrier to effective environmental and disease management
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