Apoorva Safai | Neuroscience | Best Researcher Award

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Dr. Apoorva Safai | Neuroscience | Best Researcher Award 

Postdoctoral Research Associate, at University of Wisconsin-Madison, United States.

Dr. Apoorva Safai is a distinguished researcher specializing in neuroimaging, with a focus on deep learning applications in medical imaging and multimodal MRI analysis. She is currently a Postdoctoral Research Associate at the Integrating Diagnostics and Analytics (IDiA) Lab at the University of Wisconsin–Madison. Throughout her career, Dr. Safai has contributed significantly to understanding neurological disorders, particularly Parkinson’s disease and Alzheimer’s disease. Her research integrates advanced imaging techniques with machine learning to uncover intricate patterns in brain connectivity and structure. Dr. Safai’s work has been recognized through various awards and grants, underscoring her commitment to advancing medical imaging and neurodegenerative disease research. ​Idia Labs

Professional Profile

Scopus

ORCID

Google scholar

Education 🎓

Dr. Safai’s academic journey began with a Bachelor of Engineering in Electronics Engineering from P.V.P.I.T College, University of Pune, where she graduated with 65.4% marks in 2012. She pursued a Master of Technology in Biomedical Engineering at VIT University, Vellore, achieving a CGPA of 8.49 in 2015. Her passion for research led her to earn a PhD in Engineering from Symbiosis International University, Pune, between 2018 and 2023. Her doctoral research focused on developing a multimodal brain connectomic framework employing graph attention networks on structural and functional brain data, aiming to enhance the prediction and understanding of neurological disorders. ​Idia Labs

Experience 🧠

Dr. Safai’s professional experience is rich and diverse. Since April 2023, she has been serving as a Postdoctoral Research Associate at the IDiA Lab, University of Wisconsin–Madison, focusing on deep learning applications in optical coherence tomography and neuroimaging. Prior to this, she was a Senior Research Fellow and PhD Scholar at the Symbiosis Centre for Medical Image Analysis, Pune, from September 2021 to May 2022, where she worked on multimodal MRI analysis and deep learning models for neurological disorders. She also held the position of Technical Assistant in Imaging at the Department of Neuroimaging, NIMHANS, Bangalore, from May 2017 to August 2018, contributing to the Indo-UK project cVEDA, focusing on fMRI data acquisition and analysis. ​Idia Labs+1Google Scholar+1

Research Interests 🔬

Dr. Safai’s research interests lie at the intersection of neuroimaging and artificial intelligence. She specializes in multimodal MRI analysis, aiming to integrate various imaging modalities to provide a comprehensive understanding of brain structure and function. Her work in deep learning in medical imaging seeks to develop algorithms that can assist in the early detection and monitoring of neurological disorders such as Parkinson’s and Alzheimer’s diseases. By leveraging advanced computational techniques, Dr. Safai aims to uncover biomarkers and patterns that can lead to better diagnosis and treatment strategies for these conditions. ​

Awards 🏆

Dr. Safai’s contributions to neuroimaging and medical imaging have been recognized through several prestigious awards. She received the Alzheimer’s Association Research Fellowship to Promote Diversity (AARF-D) grant for 2025–2027, supporting her project titled “Multimodal AI-based Predictor of Alzheimer’s Disease (MAP-AD).” In 2020, she was awarded the ISMRM student research exchange grant for her proposal on high temporal resolution fMRI acquisition and advanced analysis for identifying reliable imaging markers for Parkinson’s disease. Additionally, she received a travel grant from the Movement Disorder Society for the MDS Conference in 2019 and an educational stipend from ISMRM for the same year’s conference, highlighting her active engagement and recognition in the scientific community. ​

Top Noted Publications 📚

  • Microstructural abnormalities of substantia nigra in Parkinson’s disease: A neuromelanin sensitive MRI atlas-based study

    • Year: 2020

    • Journal: Human Brain Mapping

    • Citations: 35 (PubMed)

    • Summary: This study investigates microstructural changes in the substantia nigra of Parkinson’s disease patients using neuromelanin-sensitive MRI, providing an atlas-based approach for assessing disease-related abnormalities.

  • Multimodal brain connectomics-based prediction of Parkinson’s disease using graph attention networks

    • Year: 2022

    • Journal: Frontiers in Neuroscience

    • Citations: 16 (Google Scholar)

    • Summary: The research utilizes graph attention networks (GATs) to analyze multimodal brain connectomics data for predicting Parkinson’s disease, demonstrating the effectiveness of deep learning in neurological disorder classification.

  • Disrupted structural connectome and neurocognitive functions in Duchenne muscular dystrophy: classifying and subtyping based on Dp140 dystrophin isoform

    • Year: 2022

    • Journal: Journal of Neurology

    • Citations: 11 (Loop)

    • Summary: This study explores the relationship between structural brain connectivity disruptions and neurocognitive deficits in Duchenne muscular dystrophy, with a focus on the Dp140 dystrophin isoform for patient subtyping.

  • Developing a radiomics signature for supratentorial extra-ventricular ependymoma using multimodal MR imaging

    • Year: 2021

    • Journal: Frontiers in Neurology

    • Citations: 5 (Google Scholar)

    • Summary: The research develops a radiomics-based approach using multimodal MRI to characterize supratentorial extra-ventricular ependymoma, enhancing tumor classification and diagnosis.

  • Quantifying Geographic Atrophy in Age-Related Macular Degeneration: A Comparative Analysis Across 12 Deep Learning Models

    • Year: 2024

    • Journal: Investigative Ophthalmology & Visual Science

    • Summary: This study compares the performance of 12 deep learning models in quantifying geographic atrophy in age-related macular degeneration, assessing their accuracy and reliability for clinical applications.

Conclusion

Apoorva Safai is a highly qualified candidate for the Best Researcher Award based on her strong academic background, impactful research, prestigious grants, and leadership in medical imaging and deep learning. Addressing minor improvements in authorship and funding scale would further elevate her profile. Overall, she is an excellent contender for the award.

jinghua wu | Behavior science | Women Researcher Award

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Assoc Prof Dr. jinghua wu | Behavior science | Women Researcher Award

Assoc Prof Dr. jinghua wu, sichuan Agricultural University, China

Dr. Wu Jinghua is an associate professor at the College of Information Engineering, Sichuan Agricultural University, a key university under China’s “Project 211.” She holds a Ph.D. in Information Management System from South-West Financial University. With research interests in information management systems, e-commerce, and e-finance, she has authored numerous papers and publications. Previously, she worked at Leshan Gas Company and has undertaken significant roles in research projects focusing on digital agriculture and financial technologies. Dr. Wu is known for her analytical skills and dedication to advancing information technology applications. 📊

 

Publication profile

Scopus

Education Experience

Dr. Wu Jinghua holds a Ph.D. in Information Management System from South-West Financial University (2012), an M.S. in Computer Science from Southwest University (2005), and a B.S. in Computing Science from China West Normal University (1999).

 

Professional Experience

She has been an associate professor at Sichuan Agricultural University since 2012, teaching courses on E-commerce, Java Programming, and more.

Dr. Wu Jinghua’s research focuses on several key areas within information engineering and agricultural economics. Her work primarily centers around data mining, machine learning applications such as natural language processing and sentiment analysis, and the digital transformation of agriculture. She has contributed significantly to fields like e-commerce, e-finance, and digital agriculture, emphasizing innovations in information management systems and software engineering. Dr. Wu’s research also extends to agricultural price prediction models and the spatial-temporal factors influencing agricultural economics. Her interdisciplinary approach integrates technology with agricultural practices, aiming to enhance efficiency and decision-making processes in farming communities. 🌾💻

Research Focus

Dr. Wu Jinghua’s research focuses on several key areas within information engineering and agricultural economics. Her work primarily centers around data mining, machine learning applications such as natural language processing and sentiment analysis, and the digital transformation of agriculture. She has contributed significantly to fields like e-commerce, e-finance, and digital agriculture, emphasizing innovations in information management systems and software engineering. Dr. Wu’s research also extends to agricultural price prediction models and the spatial-temporal factors influencing agricultural economics. Her interdisciplinary approach integrates technology with agricultural practices, aiming to enhance efficiency and decision-making processes in farming communities. 🌾💻

 

Publication Top Notes

📄 Agricultural price prediction based on data mining and attention-based gated recurrent unit: a case study on China’s hog, Journal of Intelligent and Fuzzy Systems, 2024

📄 Exploring the Chinese public’s affective attitudes towards digital transformation in agriculture: A social media-based analysis, Applied Psychology: Health and Well-Being, 2024

📄 Agricultural price forecasting based on the spatial and temporal influences factors under spillover effects, Kybernetes, 2023 (cited by 1)

📄 Student classroom behavior recognition and evaluation system based on YOLOX, Proceedings of SPIE – The International Society for Optical Engineering, 2022 (cited by 1)

📄 A Comparative Study of Image Dehazing Based on Attention-Net and U-Net Atmospheric Light Estimation, 2020 IEEE 6th International Conference on Computer and Communications, 2020

📄 Image restoration of agricultural history based on neural patch synthesis, 2019 IEEE 11th International Conference on Communication Software and Networks, 2019

📄 Automatic classification and detection of oranges based on computer vision, 2018 IEEE 4th International Conference on Computer and Communications, 2018

 

 

Hai-Long Zhang | Neuroscience | Best Researcher Award

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Hai-Long Zhang | Neuroscience | Best Researcher Award

Dr Hai-Long Zhang, The Fourth Affiliated Hospital of Soochow University ,China

🎓 With a keen focus on electrical engineering, Hai-Long Zhang pursued their academic journey with zeal and dedication. Graduating with a Master’s degree from Tianjin Chengjian University in Building Electrical and Intelligent systems in 2021, they laid a strong foundation for their career. Prior to that, they completed their Bachelor’s degree at Shanghai University of Electric Power in Power System and Automation. This diverse educational background equipped them with a comprehensive understanding of both theoretical concepts and practical applications in the field. Now, armed with knowledge and innovation, they are poised to make significant contributions to the realm of electrical engineering.

Publication Profile

orcid

google scholar

Education

🎓 Embarking on a journey of academic excellence, Hai-Long Zhang began their pursuit of knowledge at Tsinghua University in Beijing, China. Graduating with a Bachelor of Science in Chemical Engineering in 2013, they laid the groundwork for their future endeavors. Undeterred by challenges, they continued their scholarly pursuits, delving deeper into the intricacies of their field. In 2018, [Person’s Name] achieved the pinnacle of academic success, earning a Ph.D. in Chemical Engineering from Tsinghua University. Their years of dedication and hard work have equipped them with a profound understanding of chemical processes and a passion for innovation in their field.

 

Professional Experience

👩‍🏫 Building upon their academic achievements, [Person’s Name] transitioned seamlessly into the realm of academia, serving as an Associate Professor at the School of Food Science and Engineering, South China University of Technology, Guangzhou, China since 2021. Prior to this, they honed their expertise as a Postdoctoral Fellow at the same institution from 2018 to 2021. In both roles, [Person’s Name] has demonstrated a commitment to advancing knowledge in the field of food science and engineering. Their contributions have not only enriched the academic community but also paved the way for innovative solutions to challenges in the food industry.

Research Focus

Dr. Hai-Long Zhang focuses on neuroscience research, particularly in understanding the molecular and cellular mechanisms underlying synaptic plasticity, memory, and pain. 🧠 His work includes studying the roles of astrocytes, glutamatergic circuits, and various proteins such as calmodulin and steroid receptor coactivators in brain function and behavior. 🔬 Dr. Zhang’s research spans from exploring pain processing pathways to investigating the genetic factors contributing to neurological disorders like Alzheimer’s disease and schizophrenia. 🌟 His significant contributions aim to unravel the complexities of brain function and develop therapeutic strategies for neurological conditions. 💡

 

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Publication top Notes