About

Jahnavi Kachhia is an AI and machine learning professional whose work bridges research, product thinking, and real-world deployment. Her profile combines scholarly output, community leadership, conference service, public speaking, and applied AI development across healthcare and intelligent systems.

She has authored and contributed to work in machine learning, deep learning, biomedical signal analysis, radar signal analysis, antenna design, cardiovascular risk screening, diabetes screening, cloud security, edge-cloud orchestration, renewable energy systems, and electric vehicle infrastructure optimization. Her publication record includes peer-reviewed papers indexed through Google Scholar, where her profile currently reflects 174 citations across 11 articles.

Her open-source and public data footprint includes 10 GitHub repositories, 152 GitHub forks, 61 GitHub followers, 3 Docker images with 154K downloads, and a Kaggle Dataset Master ranking of 66/9,606 (top 1%).

In addition to research, she serves the technical community through program committee appointments, conference reviewing, and hackathon judging. Her service spans venues and workshops connected to artificial intelligence, machine learning, multimodal systems, healthcare AI, human-robot interaction, and knowledge discovery.

Her public-facing work also includes invited talks and panel participation on responsible AI, generative AI, LLMs in healthcare, and AI-enabled decision support. This combination of research, service, and speaking positions her as a multidisciplinary contributor at the intersection of technical depth and practical impact.

Public web listings also reflect additional visibility through invited conference speaker pages, technical program committee acknowledgements, and community recognitions including a Women in Tech Global Awards 2025 nomination and a Women in Tech Network global ambassador listing.

Core strengths

• Translating research into applied AI systems and practical outcomes
• Responsible AI, explainability, and decision support in high-impact domains
• Cross-functional collaboration across research, engineering, product, and community settings

Profiles

• Google Scholar
• GitHub
• Docker Hub Profile
• Kaggle
• Academia.edu
• LinkedIn
• ResearchGate
• ORCID

These public profiles strengthen visibility across scholarship, open-source work, professional networking, and applied AI project delivery.

Certifications

• Stanford Machine Learning
• Getting Started with AWS Machine Learning
• NVIDIA Fundamentals of Deep Learning for Computer Vision

Memberships

• IEEE Senior Membership
• MIET
• AI Frontier Network (AIFN)
• WRU

Contact

• Email: jahnavik186@gmail.com