Ambika Singh

During my Master’s thesis at Helmholtz Munich, I developed a computational imaging workflow to quantitatively analyze 3D lung airway architecture in healthy and diseased mouse models. The project combined light-sheet fluorescence microscopy (LSFM), AI-based segmentation (nnU-Net), and graph-based morphometric analysis to extract reproducible structural metrics from large-scale volumetric imaging datasets. My work focused on translating complex biological imaging data into structured, traceable, and reprodu

Part of the marketing team at One-AIM, a student led initiative to drive advancements in MedTech.

Built machine learning models to predict Alzheimer’s disease severity from metabolomics data using Elastic Net, Random Forest, and feature selection methods. Processed large ADNI datasets, derived severity scores via PCA, and evaluated model performance using cross-validation and statistical metrics. Strengthened expertise in reproducible R workflows and translational biomedical data analysis.

Worked on modeling neurodegeneration in iPSC-derived dopaminergic neurons to study iron-induced oxidative stress and pathway modulation. Designed and executed controlled experiments, performed quantitative protein analysis (western blot, nIRF assays), and translated experimental results into structured, statistically validated insights. Gained experience in experimental workflow optimization, data integrity, and cross-functional collaboration in a translational research environment.

Understanding the role of molecular mechanisms (insulin signalling, dietary restriction) in the ageing of an organism using C. elegans as a model.

Online summer internship (SRI-VIPRA) Title- Study of regulatory mechanisms in glioma stem cells” (an integrated Omics approach)