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AI Infrastructure for Healthcare Deployments

Artificial Intelligence is reshaping life sciences, medicine, and healthcare as an industry. GPU-accelerated deep learning solutions are at the heart of this revolution. They’re enabling a new era of biomedical advancements—from real-time pathology assessment to point-of-care interventions to predictive analytics for clinical decision-making. Learn more about how AI-driven healthcare is taking center stage.

Our guest speaker, CEO of Parabricks Mehrzad Samadi, discusses the use of GPU-acceleration to speed the analysis of DNA sequencing data. Parabricks has accelerated secondary analysis of sequencing data to analyze a 30x whole genome from days to less than one hour. Using GPUs, Parabrick’s accelerated workflow can finish alignment, pre-processing and variant calling—a process that can easily take up to 30 hours on 32 vCPU servers—at trailblazing speeds.

By watching this webinar replay, attendees will:
  1. Learn how deep learning and AI are driving advances in healthcare, medical research, pharmacology, precision medicine and other areas
  2. Gain an in-depth understanding of how GPUs can be used for accelerating industry standard algorithms and deep learning technologies used in genomics and Medical Imaging
  3. Get access to the NVIDIA DGX Pod blueprint for large-scale development and deployment of both AI software and Healthcare software applications
ONDEMAND WEBINAR REGISTRATION
Webinar access will be emailed to you.
Presented By
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Dr. Ettikan Kandasamy Karuppiah
Director of Developers Ecosystem, NVIDIA, South East Asia

Dr. Ettikan Kandasamy Karuppiah, Director of Developers Ecosystem at NVIDIA, South East Asia assists innovators, researchers and techno-entrepreneurs to accelerate GPU adaptation for their R& D and software solutioning needs. He has direct experience and passionate in accelerated computing/software research/deep learning, design and development covering end-to-end needs. He also has published numerous publications, patents and software libraries from his past work.
 
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Dr. Gabriel Noaje
Senior Solutions Architect, NVIDIA

Dr. Gabriel Noaje has more than 10 years of experience in accelerator technologies and parallel computing. Prior to joining NVIDIA, he was a Senior Solutions Architect with SGI and HPE where he was developing solutions for HPC and Deep Learning customers in APAC. Previously, he was a Senior Computational Scientist at A*STAR Computational Resource Centre in Singapore (A*CRC) supporting users with deploying their applications on GPUs and large HPC systems. Gabriel was also involved in the commissioning of the first petaflop supercomputer in Singapore for the National Supercomputing Centre (NSCC) providing his expertise in all stages from specifications drafting to the production phase. Gabriel holds a PhD in Computer Sciences from the University of Reims Champagne-Ardenne, France and a BSc and MSc in Computer Sciences from the Polytechnic University of Bucharest, Romania.
 
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Qingyi Tao
Solutions Architect, NVIDIA AI Technology Center (NVAITC)

Qingyi Tao is a solutions architect from NVIDIA AI Technology Center (NVAITC). She is a deep learning researcher focusing on computer vision and medical imaging. She has publications in top AI conference and journal, specializing in domain adaptation and transfer learning. Prior to NVIDIA, Qingyi worked in Rapid-Object-SEarch (ROSE) Lab at Nanyang Technological University (NTU) and also, received a bachelor degree from NTU Singapore.
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Dr. Mehrzad Samadi
CEO, Parabricks Mehrzad Samadi

Dr. Mehrzad Samadi has over 10 years’ experience in high performance computing. An accomplished author, he has published over 20 papers in the area of compiler optimizations for GPUs, heterogeneous computing and machine learning. He co-founded Parabricks in 2015 to provide high performance bioinformatics tools to enable researchers to accelerate their research efforts. Prior to Parabricks, Samadi worked as a researcher at Microsoft research. He received his Ph.D. in Computer Science and Engineering from the University of Michigan.


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