Chunling Bao | Data Science | Best Researcher Award

Ms. Chunling Bao | Data Science | Best Researcher Award

PhD Candidates, Shanghai Normal University, China

Chunling Bao is a dedicated Ph.D. candidate at Shanghai Normal University, specializing in environmental and geographical sciences 🌍. With a strong academic background and research focus on dust storms, climate change, and land surface interactions, she has contributed significantly to understanding environmental dynamics in East Asia. Her scholarly work is widely recognized, with multiple publications in high-impact journals 📚.

Publication Profile

ORCID

🎓 Education

Chunling Bao embarked on her academic journey at Inner Mongolia Normal University, earning her undergraduate degree (2014-2018) and later obtaining her master’s degree (2018-2021) 🎓. She expanded her expertise through an exchange program at the Center for Agricultural Resources Research, Chinese Academy of Sciences (2023), before pursuing her doctoral studies at Shanghai Normal University (2023-present) 🏫.

💼 Experience

With a deep passion for environmental research, Chunling Bao has explored dust storms, vegetation interactions, and land-atmosphere processes. Her experience includes field studies, satellite data analysis, and interdisciplinary research collaborations 🌪️. Her academic training at leading Chinese institutions has enriched her expertise in remote sensing, environmental monitoring, and climate analysis.

🏆 Awards and Honors

Chunling Bao has been recognized for her outstanding research contributions in environmental science 🏅. Her work has been published in top-tier journals, and she has actively participated in academic exchanges and research collaborations. Her efforts in studying dust storm dynamics have positioned her as an emerging scholar in the field 🌿.

🔬 Research Focus

Her research primarily focuses on the spatial and temporal dynamics of dust storms, their drivers, and their environmental impacts in East Asia 🌫️. Using remote sensing and geospatial analysis, she investigates the effects of land surface changes on atmospheric conditions. Her studies contribute to climate adaptation strategies and sustainable environmental management.

📌 Conclusion

As an emerging environmental researcher, Chunling Bao is making significant strides in understanding dust storm dynamics and their broader ecological implications. With her growing academic contributions and research excellence, she continues to shape the field of environmental science and atmospheric studies 🌏.

📚 Publications

Dust Intensity Across Vegetation Types in Mongolia: Drivers and Trends. Remote Sensing, 17(3), 410. 🔗 DOI

Analyses of the Dust Storm Sources, Affected Areas, and Moving Paths in Mongolia and China in Early Spring. Remote Sensing, 14, 3661. 🔗 DOI

Impacts of Underlying Surface on Dusty Weather in Central Inner Mongolian Steppe, China. Earth and Space Science, 8, e2021EA001672. 🔗 DOI

Regional Spatial and Temporal Variation Characteristics of Dust in East Asia. Geographical Research, 40(11), 3002-3015. 🔗 DOI (in Chinese)

Analysis of the Movement Path of Dust Storms Affecting Alxa. Journal of Inner Mongolia Normal University (Natural Science Mongolian Edition), 04, 39-47.

Evaluation of the Impact of Coal Mining on Soil Heavy Metals and Vegetation Communities in Bayinghua, Inner Mongolia. Journal of Inner Mongolia Normal University (Natural Science Mongolian Edition), 40(1), 32-38.

 

 

Fatemeh Shah-Mohammadi | Biomedical Informatics | Best Researcher Award

Dr. Fatemeh Shah-Mohammadi | Biomedical Informatics | Best Researcher Award

Research Assistant Professor, University of Utah, United States

🎓 Fatemeh Shah Mohammadi is a dedicated Research Assistant Professor at the University of Utah’s School of Medicine, specializing in Biomedical Informatics and Clinical Data Science. With a strong foundation in machine learning, she excels in developing innovative solutions for healthcare, particularly in clinical natural language processing (NLP) and predictive analytics. Fatemeh is an active member of professional societies such as the American Medical Informatics Association and has contributed significantly to advancing health informatics through research and teaching.

Publication Profile

Google Scholar

Education

📘 Fatemeh Shah Mohammadi earned her Ph.D. in Engineering with a specialization in Machine Learning from Rochester Institute of Technology (2014–2020), where her dissertation focused on resource allocation in cognitive radio networks under the guidance of Prof. Andres Kwasinski. Prior to this, she completed her Master of Science in Electrical and Communication Engineering from Birjand University of Technology (2008–2011), demonstrating her foundational expertise in communications engineering.

Experience

💼 Fatemeh brings a wealth of experience from her roles as a Research Assistant Professor at the University of Utah, Biostatistician II/NLP Engineer at Mount Sinai Health System, and Postdoctoral Fellow at Icahn School of Medicine. Her earlier work as a Research Assistant at Rochester Institute of Technology laid the groundwork for her career in data science and health informatics.

Research Interests

🔬 Fatemeh’s research focuses on applying machine learning and natural language processing (NLP) in healthcare, with interests spanning clinical decision support, resource optimization, and cross-domain data integration. She is passionate about exploring generative AI’s potential in improving patient outcomes and healthcare delivery.

Awards

🏆 Fatemeh has received numerous accolades, including the Best Conference Paper Award at IEEE ECBIOS 2024 and recognition as the best graduate student presenter at the Wireless Telecommunications Symposium in 2020. She is a proud member of the Honor Society of Phi Kappa Phi and an active contributor to health informatics and machine learning.

Publications

Shah-Mohammadi F, Enaami H. H., Kwasinski A. (2021). Neural network cognitive engine for autonomous and distributed underlay dynamic spectrum access. IEEE Open Journal of the Communications Society, 2, 719-737. Read more
Cited by: 3 articles

Shah-Mohammadi F, Cui W, Finkelstein J. (2021). Entity Extraction for Clinical Notes, a Comparison Between MetaMap and Amazon Comprehend Medical. Stud Health Technol Inform, 281:258-262. Read more
Cited by: 5 articles

Shah-Mohammadi F, Parvanova I, Finkelstein J. (2022). NLP-Assisted Pipeline for COVID-19 Core Outcome Set Identification Using ClinicalTrials.gov. Stud Health Technol Inform, 290:622-626. Read more
Cited by: 4 articles

Shah-Mohammadi F, Finkelstein J. (2024). NLP-Assisted Differential Diagnosis of Chronic Obstructive Pulmonary Disease Exacerbation. Stud Health Technol Inform, 310:589-593. Read more
Cited by: 2 articles

Cui W, Shah-Mohammadi F, Finkelstein J. (2023). Using Electronic Medical Records and Clinical Notes to Predict the Outcome of Opioid Treatment Program. Stud Health Technol Inform, 305:568-571. Read more
Cited by: 1 article