Pallavi Gupta, PhD, MS
Overview
My research focuses on applying machine learning (ML) and artificial intelligence (AI) to improve healthcare outcomes, particularly for aging populations. With a strong academic background and experience in informatics and data science, I have developed specialized expertise in building predictive models and AI-driven solutions for personalized healthcare. My Ph.D. work at the University of Missouri, where I designed machine learning solutions for in-home health monitoring using sensor data, has been pivotal in advancing technology that can help reduce Medicare costs and improve care for older adults.
Currently, as a Postdoctoral Research Scientist at Columbia University, I am leading research initiatives that leverage Multimodal Large Language Models (MLLMs) and Large Language Models to automate patient monitoring and reduce the strain on home healthcare providers. My research work aims to enhance patient care through AI-driven innovations that address real-world challenges, such as reducing nurse burnout through automated video and audio analysis.
Throughout my career, I have consistently sought interdisciplinary collaboration, working closely with cross-functional teams to implement AI solutions that meet practical healthcare needs. My experience spans academic, clinical, and industry environments. I have also been actively involved in teaching, organizing workshops, and mentoring the next generation of data scientists and engineers during my PhD. With my passion for using AI to solve healthcare challenges and my proven ability to lead research from concept to implementation, I am well-positioned to contribute to the next wave of innovations in personalized healthcare. My goal is to continue advancing technologies that can provide early, accurate, and actionable health insights, ultimately improving patient care and reducing healthcare burdens.
Currently, as a Postdoctoral Research Scientist at Columbia University, I am leading research initiatives that leverage Multimodal Large Language Models (MLLMs) and Large Language Models to automate patient monitoring and reduce the strain on home healthcare providers. My research work aims to enhance patient care through AI-driven innovations that address real-world challenges, such as reducing nurse burnout through automated video and audio analysis.
Throughout my career, I have consistently sought interdisciplinary collaboration, working closely with cross-functional teams to implement AI solutions that meet practical healthcare needs. My experience spans academic, clinical, and industry environments. I have also been actively involved in teaching, organizing workshops, and mentoring the next generation of data scientists and engineers during my PhD. With my passion for using AI to solve healthcare challenges and my proven ability to lead research from concept to implementation, I am well-positioned to contribute to the next wave of innovations in personalized healthcare. My goal is to continue advancing technologies that can provide early, accurate, and actionable health insights, ultimately improving patient care and reducing healthcare burdens.