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

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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.