Sponsored Content by AdvantechReviewed by Olivia FrostAug 10 2026
In this interview, News Medical speaks with Dustin Tseng, Product Manager for the Edge Server Group at Advantech, and Ben Busby, Global Alliances Manager for Omics at NVIDIA, about how advances in computing infrastructure are enabling AI-driven life science research. They discuss the growing demand for GPU-accelerated computing, the challenges of managing increasingly complex biological data, and how close collaboration between hardware and software providers is helping accelerate genomics, diagnostics, and scientific discovery. The interview is based on the recent Advantech webinar on computing for life sciences.
Can you please introduce yourselves and your roles?
Dustin Tseng: I focus on developing high-performance server platforms for AI, edge computing, and life science applications. Our team works closely with equipment manufacturers to design infrastructure that supports demanding workloads such as genomics, diagnostics, and real-time AI inference. We also collaborate with ecosystem partners like NVIDIA to ensure our platforms deliver the performance and scalability customers require.
Ben Busby: I work across the life sciences ecosystem with sequencing companies, research organizations, cloud providers, and hardware partners to help accelerate genomics and AI workflows. Our goal is to make advanced computing accessible so researchers can analyze biological data more quickly and efficiently.
Why has computing become such a critical focus for life science research?
Dustin Tseng: Modern laboratory instruments generate enormous volumes of data, whether that comes from genome sequencing, medical imaging, or diagnostic systems. At the same time, organizations are rapidly adopting AI to extract meaningful insights from that information. The challenge is that much of this data is unstructured, meaning it cannot immediately be used by AI models. Computing infrastructure, therefore, becomes essential because it enables data to be collected, processed, and analyzed efficiently. We are seeing AI move beyond pilot projects into everyday laboratory workflows, making high-performance computing a fundamental part of scientific research rather than simply an IT consideration.
Image Credit: Collagery/Shutterstock.com
What are the biggest infrastructure challenges laboratories are facing today?
Dustin Tseng: One of the biggest challenges is what we call the data-to-discovery gap. Instruments produce vast datasets that often need to be analyzed immediately to support clinical decisions or ongoing experiments. Moving terabytes of information to the cloud introduces latency, increases costs, and raises data privacy concerns. Increasingly, laboratories need computing resources close to where the data is generated. Success is no longer measured simply by the amount of computing power available, but by how efficiently that infrastructure converts data into meaningful scientific outcomes.
Why is the partnership between Advantech and NVIDIA so important?
Dustin Tseng: Advantech provides the hardware infrastructure that supports the entire scientific data pipeline, from acquisition through to AI analysis. NVIDIA provides the GPU acceleration and AI software that allow those platforms to perform complex workloads efficiently. We are creating AI-ready infrastructure that laboratories and equipment manufacturers can deploy with confidence.
Ben Busby: Collaboration is essential because life science computing spans many organizations. We work closely with hardware manufacturers, sequencing companies, software developers, research institutes, and cloud providers. By combining specialized hardware with accelerated software, we can help researchers move much more quickly from raw data to scientific insight.
How have your AI server platforms been designed specifically for genomics and diagnostics?
Dustin Tseng: Our SKY server architecture was developed to meet the unique demands of life science applications. We focus on high-density PCIe Gen5 connectivity to eliminate data bottlenecks between CPUs and GPUs, advanced zonal cooling to maintain performance during intensive AI workloads, and modular designs that allow future GPU generations to be integrated without redesigning the entire platform. These systems also support the compact footprints and low acoustic levels required in laboratory environments while providing the computing power needed for real-time genomic analysis and AI inference.
Image Credit: Alcato/Shutterstock.com
Ben, how is NVIDIA accelerating genomics research through software?
Ben Busby: Our philosophy is to accelerate the software researchers already use rather than requiring them to adopt entirely new workflows. We have accelerated numerous open-source genomics applications covering short-read sequencing, long-read sequencing, RNA analysis, protein modeling, and pan-genome analysis. In many cases, we have demonstrated around forty-fold improvements over CPU-based workflows. These applications can run on accessible GPU platforms, enabling researchers to process more data in less time while reducing computational costs.
Beyond genomics, what other AI tools are helping researchers?
Ben Busby: We are advancing technologies such as federated learning, accelerated Python libraries, and knowledge graph frameworks. These allow researchers to accelerate familiar workflows with minimal code changes while also supporting secure collaboration between institutions. Knowledge graphs combined with large language models also have enormous potential because they allow scientists to query complex biological information in natural language while improving traceability and validation. These developments are helping AI become much more practical for everyday biological research.
Where do you see computing in life sciences heading over the next five years?
Dustin Tseng: Computing will increasingly move closer to where scientific data is generated. Edge AI, GPU acceleration, and modular infrastructure will allow laboratories to perform sophisticated analysis in real time while maintaining data privacy and reducing latency. As AI becomes embedded throughout research workflows, scalable infrastructure will become even more important.
We also expect to see growing demand for computing solutions that can be tailored to different research environments. Moving analysis closer to the edge requires more compact, flexible hardware, whether that's benchtop systems with short-depth servers or rackmount infrastructure for larger research facilities. Laboratories increasingly need solutions that fit their available space, performance requirements, and workflows, rather than relying on standard off-the-shelf configurations. The ability to customize system design, from hardware form factor to overall system architecture, will become an important differentiator as life sciences computing continues to evolve.
Ben Busby: I also expect much stronger collaboration across the life science ecosystem. Open-source software, accelerated computing, and AI will continue to lower barriers to advanced analysis, allowing researchers to spend less time waiting for computation and more time making scientific discoveries.
About Dustin Tseng
Dustin Tseng is the Product Manager for the Edge Server Group at Advantech, where he focuses on developing AI-ready server platforms for edge computing, high-performance computing, and life sciences applications. He works closely with technology partners to deliver scalable infrastructure that supports genomics, diagnostics, and AI-driven research, helping equipment manufacturers bring advanced computing capabilities directly into laboratory environments.
About Ben Busby
Ben Busby is Global Alliances Manager for Omics at NVIDIA, where he works with sequencing companies, research institutions, cloud providers, and hardware partners to accelerate genomics and AI workloads. His work focuses on expanding access to GPU-accelerated computing, open-source bioinformatics tools, and AI technologies that help researchers analyze biological data more efficiently and advance precision medicine.
About Advantech
With decades of proven experience and trusted by 23 of the top 30 medical device manufacturers, Advantech is a leading player in the global healthcare market. Advantech partners with leading medical equipment manufacturers and system integrators to transform healthcare and elevate patient-centered care. Advantech’s medical device safety certifications and FDA registration, paired with high-performance and customizable products, meets the healthcare industry’s demands for both turnkey solutions and comprehensive design and manufacturing services.
Global headquarters located in Taipei, Taiwan, North American headquarters in Irvine, California and design center in Cincinnati, Ohio, Advantech is a global market-leader in industrial PCs and medical grade PCs. Offering a high degree of customization to meet unique customer requirements with in-house design and manufacturing, along with the deliberate selection of component, processor, and chipset for product designs, Advantech products are designed and manufactured with the primary intent of longevity and availability. Advantech has been ISO 13485 certified for medical devices since 2003; its North American Service Center located in Milpitas, CA is also a FDA Registered facility.
Advantech’s medical product portfolio includes medical-grade PCs, medical displays, medical tablets, mobile workstation and telehealth carts and healthcare information terminals. Medically certified for patient safety (60601), fanless and sealed for infection control, Advantech’s purpose built products specific for healthcare use cases are configurable and customizable, built for both acute care and non-acute care healthcare facilities.
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