Deploying AI from research to real-world healthcare — at Samsung scale.
I hold a PhD in Signal Processing and AI from IIT Roorkee and currently lead AI initiatives as Senior Manager of GenAI & Digital Health AI at Samsung Research Institute, Noida. My work sits at the intersection of on-device LLM deployment, physiological signal processing, and production-grade AI systems.
I've contributed to FDA-ready cardiovascular monitoring solutions, built intelligent RAG systems for clinical analytics, and shipped LLM features on constrained hardware. My research explores quantization, model compression, and efficient inference across CPUs, NPUs, and Qualcomm QNN.
Previously at Mercedes-Benz R&D India (in-cabin AI, 2022–2025) and Elastic Care, Canada (FDA-cleared AI modules, 2019–2022). Invited speaker at IIM Sambalpur, NIT Jalandhar, and other institutions.
From biomedical signal processing to production GenAI — a decade of building at the edge of AI and healthcare.
Leading on-device AI initiatives across LLM deployment, GenAI systems, and physiological signal processing (ECG, PPG, audio) — targeting deployment across Samsung's 500M+ active device ecosystem. Built intelligent RAG systems for clinical data analytics, enabling clinicians to query physiological datasets in natural language. Driving NPU acceleration via Qualcomm QNN SDK and custom AI agent frameworks at production scale.
Developed in-cabin AI features for smart vehicles, applying multimodal sensing and real-time signal processing for driver monitoring systems. Two pending patents in automotive health sensing.
Led development of FDA-cleared AI modules for wearable vital sign monitoring (HR, RR, arrhythmia classification). Supported FDA 510(k) submissions and SaMD compliance frameworks for cardiovascular and metabolic monitoring pipelines.
Consulting for global health-tech startups including Vigo Health Care, Pareto Tree, and others on FDA 510(k), SaMD compliance, and clinical trial design. Invited speaker at IIM Sambalpur, NIT Jalandhar, and invited reviewer for IEEE TIM, IEEE IoT Journal, and Scientific Reports (Nature).
A decade of sharpening tools across signal processing, deep learning, and production AI systems.
My technical depth spans the full pipeline — from raw physiological signal acquisition to quantized model deployment on edge hardware. I work comfortably across research prototypes and production systems.
End-to-end AI systems — from wearable signal processing to LLM deployment on edge devices.
Peer-reviewed contributions in deep learning, biomedical signal processing, and explainable AI — across Q1 journals and international conferences.
Technical deep-dives into LLMs, quantization, inference runtimes, and digital health AI.
Open to AI leadership roles, research collaborations, consulting, and speaking invitations.