I build diagnostic systems for settings where they don't exist. My research focuses on cross-modal medical inference - extracting clinical insight from low-cost inputs (smartphone images, ECG waveforms) to replace expensive diagnostics in resource-constrained environments. Current work includes LLM-based ECG-to-echocardiography inference (r=0.394, n=311) and a smartphone hemoglobin estimation pipeline validated in 600 pregnant women. I am a third-year medical student (MBBS) at Gadag Institute of Medical Sciences and a committed listener at MIT's How To Grow (Almost) Anything (HTGAA 2026), working on engineered biological systems for hospital-acquired infection control. My long-term direction is building translation layers between biological systems and computational interfaces.
Core: Python, PyTorch, Scikit-learn, Computer Vision, Transformers, LLMs.
Clinical: Biosignal Processing, Clinical Data Analysis, Trial Design.
Strategy: Grant Writing (ICMR), Technical Manuscripts, Frugal Innovation.