Medical Imaging
Neuro-Oncology & Radiomics
Manuscript in Preparation · Second Author
Differentiating True Progression from Pseudoprogression in Glioblastomas using DCE-MRI
A critical challenge in glioblastoma management is distinguishing true tumour progression from pseudoprogression — a treatment-induced imaging change that mimics progression on standard MRI. This work employs dynamic contrast-enhanced MRI to extract voxel-wise pharmacokinetic parameters for temporal biomarker modelling.
Contributions
- Developed pharmacokinetic models for temporal biomarker extraction from DCE-MRI sequences.
- Built complete MRI preprocessing, tumour segmentation (nnU-Net), and radiomic feature workflows using PyRadiomics and SimpleITK.
- Multiparametric habitat-imaging framework combining PK-parameter maps with ensemble-based classification.
Significant Contribution
Glioblastoma Radiomics Classification using Machine Learning
High-dimensional radiomic feature extraction across multimodal MRI sequences for non-invasive glioblastoma characterization. Ensemble learning framework with SHAP-based feature attribution for clinical interpretability.
Contributions
- Engineered 150+ radiomic features across multimodal MRI sequences (T1, T1ce, T2, FLAIR).
- Achieved AUC 0.89 using LightGBM and Random Forest with rigorous cross-validation.
- SHAP-based interpretability framework mapping feature contributions to clinical outcomes.
Climate Science
Spatiotemporal Deep Learning
Manuscript in Preparation · First & Corresponding Author
Forecasting Dry and Humid Heatwaves over India Using Physics-Guided Spatiotemporal Deep Learning
Heatwaves are among India's most lethal meteorological hazards, yet distinguishing dry from humid variants remains computationally challenging. This work designs a ConvLSTM–Transformer hybrid architecture for categorical heatwave forecasting using ERA5 reanalysis data, incorporating atmospheric physics as inductive bias for model regularization.
Contributions
- Designing ConvLSTM–Transformer hybrid to forecast dry vs. humid heatwaves using ERA5 reanalysis data.
- Physics-guided feature selection and categorical heatwave labelling based on thermodynamic and hydrological thresholds.
- XAI tools (SHAP, Grad-CAM) to evaluate learned atmospheric mechanisms and model interpretability.
Computational Cognition
Visual Representation & Perturbation
Manuscript in Preparation · First & Corresponding Author
Fragility of Aesthetic Judgment vs. Stability of Emotional Representation under Visual Perturbation
A fundamental question in computational aesthetics: are neural representations of beauty as robust as representations of emotion? This work conducts systematic perturbation tests on CNN embeddings trained under different supervisory signals to probe the fragility of learned aesthetic versus emotional representations.
Contributions
- Investigating representational stability of CNN embeddings trained on aesthetic vs. emotional supervision.
- Gaussian noise–based perturbation tests to evaluate robustness under progressive visual degradation.
- Demonstrating that emotional representations remain stable while aesthetic representations degrade sharply — with implications for visual AI robustness.