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About Me
About Me
I'm a PhD student in Medical and Molecular Genetics at Indiana University School of Medicine, working at the intersection of neuroimaging, genomics, and computational biology. My work centers on early-onset Alzheimer's disease (EOAD), where I use genetic and imaging data from large consortia like ADNI and LEADS to understand disease subtypes and progression. What draws me to this field is the clinical side of the work — the chance to help move the needle on preventative care for early-onset AD, catching risk before symptoms take hold rather than only responding after the fact.
Outside the lab, I'm currently working on building up my running and swimming, and I'm looking forward to leveling up both. Next up on the climbing calendar: Red River Gorge, Kentucky.
Research Interests
Research Interests
Genetics of Early- and Late-Onset Alzheimer's Disease I study how genetic architecture differs between early-onset (EOAD) and late-onset (LOAD) Alzheimer's disease, using comparative genomics approaches across the ADNI and LEADS cohorts to identify what makes early-onset cases genetically and clinically distinct.
Multi-Omic Integration & Gene Regulation I'm interested in combining multiple layers of omics data (transcriptomic, proteomic, genomic) to build unified measures of gene regulatory activity — work that's informed by RNA-binding protein and transcription factor interaction studies I've contributed to.
Neuroimaging-Genetics Integration A core thread of my work is combining amyloid PET imaging with genetic risk data to stratify patients earlier and more precisely than either data type alone allows, particularly for identifying subtypes of Alzheimer's progression in early disease stages.
Machine Learning for Biomedical Prediction I apply machine learning and deep learning methods to classification problems in neurodegenerative disease and molecular biology — from predicting RNA-binding protein identities to disease progression trajectories.
Publications/Presentations
Publications/Presentations
Krohannon, A., Srivastava, M., Sangani, N., Janga, S.C. "Regulation Ratio: A Singular Multi-Omic Measurement of Gene Regulatory Mechanisms." Computational and Structural Biotechnology Journal, Mar 2026.
Luo, Q., Sangani, N., Abhyankar, S., et al., Bhatwadekar, A.D. "Global Mapping of BMAL1 Protein-DNA Interactions in Human Retinal Müller Cells." Molecular Vision, Nov 2024.
He, B., Wu, R., Sangani, N., et al., Yan, J. "Integrating Amyloid Imaging and Genetics for Early Risk Stratification of Alzheimer's Disease." Alzheimer's & Dementia, Sep 2024.
He, B., Sangani, N., Wu, R., et al., Yan, J. "Integrative Analysis of Amyloid Imaging and Genetics Reveals Subtypes of Alzheimer Progression in Early Stage." Book chapter, Jul 2024.
Salem, D.H., Sangani, N., Janga, S.C. "Predicting RNA-Binding Protein Identities on Pop-seq Dataset: A Robust Multi-Class Machine Learning Framework." Poster, Nov 2023.
Du, J., Wang, Q., Yang, S., et al., Zhou, B. "FOXP3 Exon 2 Controls T Reg Stability and Autoimmunity." Science Immunology, Jun 2022.
+ AAIC 2026 Poster: EOAD/FTD genetics using LEADS cohort data across 20+ global sites
CV/Resume link
CV/Resume link
Skills/Techniques
Skills/Techniques
Bioinformatics/Genomics: NGS data analysis, comparative genomics, multi-omic data integration
Programming: Python (pandas, data pipelines), Git/GitHub for version control and collaborative workflows
Environments: Conda/HPC cluster workflows, Docker, GitHub Actions (CI/CD)
Neuroimaging & Statistics: PET/MRI-derived measures, statistical genetics, machine learning & deep learning for classification/prediction tasks
Other: Scientific writing, data visualization, poster/presentation design
Hobbies/Outside interests
Hobbies/Outside interests
Rock climbing — next trip on the calendar is Red River Gorge, Kentucky.
Running & swimming — currently building up both, working toward improving pace/endurance over time
Time with friends & family
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