FLARE @ Duke

Evelyn Chen
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At FLARE — the Frontier Life Sciences AI Research Ecosystem — we bridge talent, capital, and knowledge at the intersection of biomedical science and AI. As this frontier takes form, we're the flare that illuminates what's next and ignites the breakthroughs that follow.

Episodes

  1. Sep 1

    Need, Love, Grow: The Product Philosophy Behind Juno | Marshall Gould

    How do you build a health product people genuinely need and love? Marshall, co-founder of Juno, joins FLARE Perspectives to unpack how the AI-powered chronic-care platform grew to around 80,000 users in six months, why more than 1,000 conversations with users have shaped its product strategy, and what Juno has learned about turning deeply personal healthcare problems into products with real utility and remarkable organic growth. We also explore the challenges of chronic-disease diagnosis, Juno’s long-term vision for patient and clinician support, and the optimism, resilience, and relentless learning required to build from scratch.

  2. Aug 27

    From AI Pilots to Better Patient Outcomes | Will Ratliff, Duke Institute for Health Innovation

    Will Ratliff, Innovation Program Manager at the Duke Institute for Health Innovation (DIHI), joins FLARE Perspectives to discuss what it takes to move healthcare AI beyond research and into real clinical practice. Will walks us through DIHI’s innovation pipeline—from identifying high-impact problems and evaluating proposals to piloting solutions across Duke Health. Through Sepsis Watch, he illustrates how thoughtfully implemented AI can support earlier intervention, improve clinical outcomes, and serve as a second pair of eyes for care teams. We also explore AI’s potential to reduce administrative burden, return clinicians’ attention to patients, and create new opportunities for students interested in healthcare innovation. HighlightsTurning ideas into clinical impact: How DIHI selects, pilots, and scales innovations inside a working health system.AI as a clinical support system: Sepsis Watch helps care teams identify at-risk patients and intervene earlier while keeping clinicians at the center of decision-making.Measuring what matters: Successful healthcare innovation must demonstrate tangible improvements in clinical workflows, compliance, and patient outcomes.Returning attention to the patient: AI can reduce documentation and administrative burdens that contribute to clinician burnout.Breaking into healthcare innovation: Curiosity, adaptability, and firsthand exposure to real clinical problems matter more than arriving with every technical skill already mastered.

  3. Aug 27

    Building the Learning Loop for AI-Driven Biosensing | Natalie Saude, DiagnoBac

    Natalie Saude, Co-Founder of DiagnoBac, joins FLARE Perspectives to discuss why the next frontier in AI-driven molecular design lies beyond predicting structure: designing biomolecules for specific functions in the environments where they must actually perform. Natalie explains how DiagnoBac is rethinking biosensor development by treating the molecule, sensing hardware, biological environment, and computational model as one integrated system. We explore why experimental feedback loops and proprietary performance data—not foundation models alone—may become the real competitive advantage in AI-enabled biotechnology, as well as the challenge of understanding not only whether a molecule works, but why. She also shares her vision for greater biological data standardization and collaboration, and reflects on the resilience, adaptability, and willingness to keep learning required to build a deep-tech venture. Episode highlights Biosensing is a system-level problem: Binding affinity alone is insufficient. Molecules must be designed around the materials, biological matrix, hardware, and operating environment in which they will function.Prediction is not mechanistic understanding: Models may identify patterns that predict success or failure without revealing the biological mechanisms responsible—limiting our ability to explain and systematically improve performance.Data fragmentation slows the field: Greater standardization and responsible data sharing could create richer datasets and accelerate progress across AI-driven biology.Criticism is information: Natalie views rejection and critical feedback as signals that help founders refine, reposition, or pivot their ventures.Founders do not need to know everything: What matters is the confidence and openness to learn quickly, adapt continually, and solve unfamiliar problems as they arise.

  4. Aug 26

    The Materials Innovation Behind the AI Chip Boom | Chris Taylor, VioNano

    Chris Taylor, Co-Founder and CEO of VioNano, joins FLARE Perspectives to discuss how precision-engineered polymer materials could help semiconductor manufacturers reduce patterning defects, improve yield, and extend existing fabrication processes. Drawing on more than two decades across semiconductor R&D, manufacturing, product management, and business development, Chris explains the difficult transition from invention to commercially viable innovation. We explore why VioNano is pursuing a customer-driven product model, how the AI boom is reshaping semiconductor manufacturing, and why even nanometer-scale improvements can create enormous economic value. Chris also shares lessons for aspiring deep-tech founders: understand the customer’s pain point, gain industry exposure, build a trusted network, and be ready when the market finally catches up with the technology.

  5. Aug 20

    The Hidden Data Problem in AI-Driven R&D | Ian Naccarella, 83 Sciences

    Ian Naccarella saw a problem hiding in plain sight: scientific teams were producing vast amounts of experimental knowledge, yet much of it was still difficult for AI systems to use. In this episode of FLARE Perspectives, the CEO & Co-Founder of 83 Sciences shared how that realization shaped the company he is building and ultimately pulled him from industry into entrepreneurship. The conversation went beyond the familiar claim that “AI needs better data.” We explored: What “better data” actually means in R&D when critical context is scattered across workflows, records, and individual teamsWhy so much experimental knowledge remains difficult for AI systems to useWhere lasting advantage comes from as foundation models become increasingly accessibleHow proprietary experimental knowledge can become a compounding advantageWhat Ian learned moving from industry insider to founder and building 83 Sciences around a problem he had experienced firsthand A founder conversation about AI-driven R&D, scientific data, company building, and the infrastructure behind the next generation of research.

  • 5 Episodes

About

At FLARE — the Frontier Life Sciences AI Research Ecosystem — we bridge talent, capital, and knowledge at the intersection of biomedical science and AI. As this frontier takes form, we're the flare that illuminates what's next and ignites the breakthroughs that follow.

Information

  • Creator
    Evelyn Chen
  • Years Active
    2026
  • Episodes
    5
  • Rating
    Clean
  • Copyright
    © 2026 Evelyn Chen