Research
I have worked in multiple labs and collaborated with researchers from diverse discipline. My research addresses the design and analysis of data-driven systems for health monitoring and biomedical discovery, with a focus on wearable sensing, spectroscopy-based diagnostics, and clinically grounded machine learning.
I also conduct computational research using high-performance computing (HPC) to analyze large-scale datasets across diverse biomedical data modalities, including clinical EHR data and multi-omic datasets.
My Ph.D. thesis involves an integrated framework that addresses two critical challenges in biomedical AI: data scarcity and poor generalization. My hypothesis is that by combining domain knowledge (physics-constraints), hardware–AI co-design, and generative models, we can create synthetic biomedical data to enhance sensing + modeling. My main application is a continuation of my Master’s work : A non-invasive wearable Hydration Tracking device.
You can explore these projects in more detail on my Projects Page.
Research focus
- AI for Wearables & Digital Biomarkers
- Multimodal Biosignal Representation Learning
- Computational Genomics & Transcriptomics
- Computational Spectroscopy
- Clinical AI Systems & End-to-End ML Pipelines