Machine Intelligence & Optimization Lab
Advancing machine learning to uncover structure in complex biological and environmental systems
Focus
Methodological
Diffusion Models and Generative Processes
Graph and Manifold Optimization
Spectral Methods
Multi-View Learning
Heathcare Applications
Cancer Epidemiology and Genomics
Real-world Evidence Studies
Medical Image Analysis
Survival Modelling
Lung Cancer Screening & Surveillance
Environmental Applications
Remote Sensing
Flood Inundation Mapping
Weather Forecasting
Monsoon and Precipitation Prediction
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Diffusion-Guided Multimodal Flood Mapping
Developed a novel framework combining SAR and optical satellite data using diffusion-based denoising and segmentation. The model improves flood mapping accuracy, robustness to noise, and generalization across diverse regions.
Principal Investigator
Aparajita Khan
I am an Assistant Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology (BHU) Varanasi, India. I joined IIT (BHU) Varanasi in July 2025 after serving as an Assistant Professor at IIT Roorkee. Earlier, I worked as a Postdoctoral Scholar with the Departments of Neurosurgery and Medicine (Quantitative Sciences Unit) at Stanford University, where I continue to collaborate.
My research focuses on development of machine learning methods to identify and analyze patterns embedded in complex large-scale datasets from different areas of biomedical sciences spanning single-cell and spatial transcriptomics, multi-omics integration, and medical natural language processing. I particularly emphasize on methodological paradigms of multi-view learning, subspace clustering, multi-graph fusion, and manifold learning.
Dr.Aparajita Khan
Assistant Professor
Indian Institute of Technology (BHU) Varanasi
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