Research
Selected publications and research areas across computer vision, signal processing, large language models, geoscience, and healthcare.
My research focuses on efficient and structure-aware learning methods for scientific and real-world sensing problems, with recurring themes in efficiency, limited supervision, and geometry-aware modeling for deployment in ecology and healthcare.
Full publication list on Google Scholar. † indicates first authorship, and * indicates corresponding authorship.
Media
Research Topics
Geospatial AI for Ecological Monitoring and Environmental Change
Geospatial AI for Ecological Monitoring and Environmental Change
I develop efficient pipelines for large-scale ecological and environmental questions, including palm detection and spatial distribution modeling, illegal mining detection, and remote sensing understanding under limited labels and deployment constraints.
Label-Efficient and Geometry-Aware Learning for Hyperspectral Imaging
Label-Efficient and Geometry-Aware Learning for Hyperspectral Imaging
I study how spatial structure, diffusion geometry, superpixels, and multi-view representations can improve learning from high-dimensional hyperspectral data when supervision is limited or unavailable.
Medical Imaging and Biomedical Signal Analysis
Medical Imaging and Biomedical Signal Analysis
I work on annotation-efficient and clinically meaningful methods for ultrasound, echocardiography, ECG, and cell imaging, with an emphasis on reconstruction, motion understanding, and practical downstream analysis.
LLM Compression, Efficiency, and Interpretability
LLM Compression, Efficiency, and Interpretability
I study efficient and interpretable LLMs, focusing on low-rank/SVD compression, fair evaluation, recovery in compressed subspaces, and decode-time attribution.