AWS for Industries

Olivia Choudhury

Author: Olivia Choudhury

Olivia Choudhury, PhD is a Principal Partner SA for Worldwide HCLS at AWS. She helps HCLS partners and customers design, develop, and scale state-of-the-art solutions leveraging AWS. She has a background in genomics, healthcare analytics, federated learning, and privacy-preserving AI. Outside of work, she plays board games, collects manga, and enjoys yoga.

Collaborative AI Model Training with Rhino Federated Computing on AWS

Introduction Across the healthcare and life sciences (HCLS) industry, organizations are generating large volumes of data with potential to improve patient care. However, transforming this data into meaningful insights can be challenging for many institutions. Privacy regulations like Health Insurance Portability and Accountability Act (HIPAA), EU General Data Protection Regulation (GDPR), and requirements for data […]

Federated learning-based protein language models with Apheris on AWS

Federated learning-based protein language models with Apheris on AWS

Healthcare and Life Sciences (HCLS) organizations face significant challenges in leveraging their valuable proprietary datasets due to privacy regulations and IP protection concerns. Apheris, an AWS technology partner, offers Apheris Gateway, which enables federated learning-based AI model training across multiple sites without sharing raw data. This allows data custodians to share insights while maintaining privacy […]

Democratize Omics Data Analysis with Basepair on AWS HealthOmics

Introduction Advancements in next-generation sequencing (NGS) technology have created new opportunities for omics data analysis, unlocking valuable insights for precision medicine, clinical diagnostics, and drug discovery. To keep pace with this high-throughput technology and handle the fluctuations in data volume, healthcare and life sciences (HCLS) customers seek a secure, reliable, and scalable environment for data […]

Build an end-to-end framework to store, integrate, and analyze multimodal data using AWS purpose-built Health and Machine Learning services.

Multimodal Data Analysis with AWS Health and Machine Learning Services

In this blog, we show how you can leverage AWS purpose-built health care and life sciences (HCLS), machine learning (ML), and analytics services to simplify storage and analysis across genomic, health records, and medical imaging data for precision health use cases. The included reference architecture is built on AWS HealthOmics, AWS HealthImaging, and AWS HealthLake services which enable you […]

Training Machine Learning Models on Multimodal Health Data with Amazon SageMaker

Training Machine Learning Models on Multimodal Health Data with Amazon SageMaker

This post was co-authored by Olivia Choudhury, PhD, Partner Solutions Architect; Michael Hsieh, Sr. AI/ML Specialist Solutions Architect; and Andy Schuetz, PhD, Sr. Startup Solutions Architect at AWS. This is the second blog post in a two-part series on Multimodal Machine Learning (Multimodal ML). In part one, we deployed pipelines for processing RNA sequence data, clinical […]

Example visualization of a CT scan, with lung tumor mask overlaid in yellow

Building Scalable Machine Learning Pipelines for Multimodal Health Data on AWS

This post was co-authored by Olivia Choudhury, PhD, Partner Solutions Architect; Michael Hsieh, Senior AI/ML Specialist Solutions Architect; and Andy Schuetz, PhD, Sr. Partner Solutions Architect. Healthcare and life sciences organizations use machine learning (ML) to enable precision medicine, anticipate patient preferences, detect disease, improve care quality, and understand inequities. Rapid growth in health information […]