Ari recently completed her internship at Ourobionics, where she conducted research on AI-driven optimization of electrospinning parameters for biomedical materials discovery. As a continuation of this work, she had the opportunity to present her findings at the Nature Conference: Materials for AI, AI for Materials in South Korea.
Her research, titled “Optimizing Electrospinning Parameters with AI: A Pathway to Faster Biomedical Materials Discovery,” was a collaboration with Ourobionics, a company pioneering biofabrication technologies for next-generation skin models and human-derived biomaterials. During her internship, Ari explored how AI and Explainable AI (XAI) can enhance electrospinning processes, contributing to smarter and more efficient biomaterials discovery.
Her work aligns with the goals of CHIMERA, a groundbreaking platform developed by Ourobionics. CHIMERA integrates five bio-electrofabrication technologies into a single machine, enabling high-speed, high-viability biomanufacturing with applications in regenerative medicine, tissue engineering, and beyond.
Research motivations
Traditional biomaterials discovery is often time-consuming, costly, and experimentally intensive. The complexity of interactions between material properties, fabrication parameters, and biological outcomes makes optimization a computationally overwhelming challenge. Her research focuses on Explainable AI (XAI) methods to: ✅ Improve model interpretability – ensuring that AI-driven predictions in material design are transparent and trustworthy ✅ Enhance decision-making – enabling researchers to understand how fabrication parameters influence biomaterial properties ✅ Accelerate discovery – reducing trial-and-error by providing actionable insights into optimal configurations
Potential outcomes for CHIMERA
The CHIMERA platform already represents a major leap in biofabrication by integrating multiple manufacturing technologies into one machine. By incorporating XAI, we can further optimize: 🔹 Process control: AI can predict and suggest the best fabrication parameters for specific biomedical applications 🔹 Material performance: Understanding the impact of electrospinning parameters and polymer blends for improved tissue scaffolding 🔹 Scalability: Automating and refining material synthesis for faster, reproducible, high-quality outputs
Conference takeaways
This conference brought together global leaders at the intersection of materials science and AI, covering groundbreaking topics like: 🔹 Autonomous materials discovery – AI-driven experimentation for faster innovation 🔹 Neuromorphic & quantum computing – expanding computational power for molecular simulations 🔹 2D materials in computing – enabling next-gen, ultra-efficient devices
Ari’s key takeaway from the conference was that the future of materials science is becoming increasingly self-learning, autonomous, and interdisciplinary. AI is not only accelerating discovery but also reshaping the way scientific experimentation is conducted.
Her discussions with global experts reinforced the transformative potential of integrating XAI with biofabrication. By making AI-driven material design more transparent and interpretable, researchers can optimize fabrication processes, reduce trial and error, and accelerate innovation in biomedical materials. The insights she gained during this event will help push AI’s role even further in the development of next-generation biomaterials.
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